ISSN 1854-6250 APEM journal Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 Published by PEI apem-journal.org University of Mari bor Advances in Production Engineering & Management Identification Statement APEM ISSN 1854-6250 | Abbreviated key title: Adv produc engineer manag | Start year: 2006 ISSN 1855-6531 (on-line) Published quarterly by Production Engineering Institute (PEI), University of Maribor Smetanova ulica 17, SI - 2000 Maribor, Slovenia, European Union (EU) Phone: 00386 2 2207522,Fax: 00386 2 2207990 Language of text: English APEM homepage: apem-journal.org UniversityofMaribor University homePage: WWW.um.si APEM Editorial Editor-in-Chief Miran Brezocnik editor@apem-journal.org, info@apem-journal.org University of Maribor, Faculty of Mechanical Engineering Smetanova ulica 17, SI - 2000 Maribor, Slovenia, EU Desk Editor Tomaz Irgolic deski@apem-journal.org Website Technical Editor Lucija Brezocnik lucija.brezocnik@um.si Editorial Board Members Eberhard Abele, Technical University of Darmstadt, Germany Bojan Acko, University of Maribor, Slovenia Joze Balic, University of Maribor, Slovenia Agostino Bruzzone, University of Genoa, Italy Borut Buchmeister, University of Maribor, Slovenia Ludwig Cardon, Ghent University, Belgium Nirupam Chakraborti, Indian Institute of Technology, Kharagpur, India Edward Chlebus, Wroclaw University of Technology, Poland Franci Cus, University of Maribor, Slovenia Igor Drstvensek, University of Maribor, Slovenia Illes Dudas, University of Miskolc, Hungary Mirko Ficko, University of Maribor, Slovenia Vlatka Hlupic, University of Westminster, UK David Hui, University of New Orleans, USA Pramod K. Jain, Indian Institute of Technology Roorkee, India Isak Karabegovic, University of Bihac, Bosnia and Herzegovina Janez Kopac, University of Ljubljana, Slovenia Iztok Palcic, University of Maribor, Slovenia Krsto Pandza, University of Leeds, UK Andrej Polajnar, University of Maribor, Slovenia Antonio Pouzada, University of Minho, Portugal Rajiv Kumar Sharma, National Institute of Technology, India Katica Simunovic, J.J. Strossmayer University of Osijek, Croatia Daizhong Su, Nottingham Trent University, UK Soemon Takakuwa, Nagoya University, Japan Nikos Tsourveloudis, Technical University of Crete, Greece Tomo Udiljak, University of Zagreb, Croatia Ivica Veza, University of Split, Croatia Limited Permission to Photocopy: Permission is granted to photocopy portions of this publication for personal use and for the use of clients and students as allowed by national copyright laws. This permission does not extend to other types of reproduction nor to copying for incorporation into commercial advertising or any other profit-making purpose. Subscription Rate: 120 EUR for 4 issues (worldwide postage included); 30 EUR for single copies (plus 10 EUR for postage); for details about payment please contact: info@apem-journal.org Cover and interior design: Miran Brezocnik Printed: Tiskarna Kostomaj, Celje, Slovenia Subsidizer: The journal is subsidized by Slovenian Research Agency Statements and opinions expressed in the articles and communications are those of the individual contributors and not necessarily those of the editors or the publisher. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. Production Engineering Institute assumes no responsibility or liability for any damage or injury to persons or property arising from the use of any materials, instructions, methods or ideas contained herein. Copyright © 2016 PEI, University of Maribor. All rights reserved. Advances in Production Engineering & Management is indexed and abstracted in the WEB OF SCIENCE (maintained by THOMSON REUTERS): Science Citation Index Expanded, Journal Citation Reports - Science Edition, Current Contents - Engineering, Computing and Technology • Scopus (maintained by Elsevier) • Inspec • EBSCO: Academic Search Alumni Edition, Academic Search Complete, Academic Search Elite, Academic Search Premier, Engineering Source, Sales & Marketing Source, TOC Premier • ProQuest: CSA Engineering Research Database -Cambridge Scientific Abstracts, Materials Business File, Materials Research Database, Mechanical & Transportation Engineering Abstracts, ProQuest SciTech Collection • TEMA (DOMA) • The journal is listed in Ulrich's Periodicals Directory and Cabell's Directory journal University of Maribor Production Engineering Institute (PEI) Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 155-254 Contents Scope and topics 158 Production monitoring system for understanding product robustness 159 Boorla, S.M.; Howard, T.J. Business plan feedback for cost effective business processes 173 Ivanisevic, A.; Katic, I.; Buchmeister, B.; Leber, M. Studies of corrosion on AA 6061 and AZ 61 friction stir welded plates 183 Raguraman, D.; Muruganandam, D.; Kumaraswami Dhas, L.A. Scheduling batches in multi hybrid cell manufacturing system considering 192 worker resources: A case study from pipeline industry Yilmaz, O.F.; Cevikcan, E.; Durmusoglu, M.B. Consideration of a buyback contract model that features game-leading 207 marketing strategies He, H.; Jian, M.; Fang, X. A green production strategies for carbon-sensitive products with a carbon cap policy 216 Ma, C.; Liu, X.; Zhang, H.; Wu, Y. Investigation of dynamic elastic deformation of parts processed by 227 fused deposition modeling additive manufacturing Mohamed, Omar A.; Masood, Syed H.; Bhowmik, Jahar L. Tool wear and cost evaluation of face milling grade 5 titanium alloy for 239 sustainable machining Masood, I.; Jahanzaib, M.; Haider, A. Calendar of events 251 Notes for contributors 253 Journal homepage: apem-journal.org ISSN 1854-6250 (print) ISSN 1855-6531 (on-line) ©2016 PEI, University of Maribor. All rights reserved. 157 Scope and topics Advances in Production Engineering & Management (APEM journal) is an interdisciplinary refer-eed international academic journal published quarterly by the Production Engineering Institute at the University of Maribor. The main goal of the APEM journal is to present original, high quality, theoretical and application-oriented research developments in all areas of production engineering and production management to a broad audience of academics and practitioners. In order to bridge the gap between theory and practice, applications based on advanced theory and case studies are particularly welcome. For theoretical papers, their originality and research contributions are the main factors in the evaluation process. General approaches, formalisms, algorithms or techniques should be illustrated with significant applications that demonstrate their applicability to real-world problems. Although the APEM journal main goal is to publish original research papers, review articles and professional papers are occasionally published. Fields of interest include, but are not limited to: Additive Manufacturing Processes Advanced Production Technologies Artificial Intelligence in Production Assembly Systems Automation Computer-Integrated Manufacturing Cutting and Forming Processes Decision Support Systems Discrete Systems and Methodology e-Manufacturing Evolutionary Computation in Production Fuzzy Systems Human Factor Engineering, Ergonomics Industrial Engineering Industrial Processes Industrial Robotics Intelligent Manufacturing Systems Joining Processes Knowledge Management Logistics Machine Learning in Production Machine Tools Machining Systems Manufacturing Systems Materials Science, Multidisciplinary Mechanical Engineering Mechatronics Metrology Modelling and Simulation Numerical Techniques Operations Research Operations Planning, Scheduling and Control Optimisation Techniques Project Management Quality Management Risk and Uncertainty Self-Organizing Systems Statistical Methods Supply Chain Management Virtual Reality in Production 158 Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 159-172 http://dx.doi.Org/10.14743/apem2016.3.217 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Production monitoring system for understanding product robustness Boorla, S.M.a*, Howard, T.J.a aTechnical University of Denmark, Department of Mechanical Engineering, Denmark A B S T R A C T In the current quality paradigm, the performance of a product is kept within specification by ensuring that its parts are within specification. Product performance is then validated after final assembly. However, this does not control how robust the product performance is, i.e. how much it will vary between the specification limits. In this paper, a model for predicting product performance is proposed, taking into account design, assembly and process parameters live from production. This empowers production to maintain final product performance, instead of part quality. The PRECI-IN case study is used to demonstrate how the monitoring system can be used to efficiently guide corrective action to improve product performance. It is claimed that the monitoring system can be used to dramatically cut the time taken to identify, plan and execute corrective action related to typical quality issues. To substantiate this claim, two further cases comparable to PRECI-IN, in terms of complexity, material and manufacturing process, were taken from different industries. The interviews with quality experts revealed that the typical time taken for corrective action for both cases was accounted to be seven days. Using the monitoring system for the PRECI-IN case, similar corrective action would have been achieved almost immediately. © 2016 PEI, University of Maribor. All rights reserved. A R T I C L E I N F O Keywords: Product robustness Performance variation Robustness monitoring system Performance consistency Unit to unit robustness *Corresponding author: srimbo@mek.dtu.dk (Boorla, S.M.) Article history: Received 27 May 2016 Revised 5 August 2016 Accepted 17 August 2016 1. Introduction 1.1 General A robust product has consistent performance, showing little variation from one unit to the next. The variation in functional performance is determined by both design and production [1], as described by the Variation Management Framework (VMF) [2]. Several researchers have explained how to handle robustness during design [3-5]. Performance variation is driven by its sensitivity to design parameters and how much they vary. Many tools are available to help design engineers to manage design parameter variation and design sensitivity [6]. However, few methods have been developed to achieve robustness from a manufacturing perspective which currently has the focus on producing part to a specification determined by design. Quality estimations, monitoring and control in industry are driven by Statistical Quality Control (SQC) and Statistical Process Control (SPC) [7]. Common practice is to understand process variables through SPC techniques and change their process settings for reducing product variation. This is to much an extent reactive control of performances and the estimation accuracies are limited due to limited sampling. Technology enhancement with a high degree of automation, allowing 100 % in-line inspection, can improve the control of final product variation [8]. However these 159 Boorla, Howard quality systems work on the principle of controlling the variables, not adjusting one to compensate another. Also performance estimations are of larger volumes can't currently be used to estimate the performance of a specific unit running on the line. The current state of the art performance prediction can be exemplified by the Artificial Neural Network Performance prediction model [9], which suggests that batches of parts be produced and measured before assembly to allow for matching complementary variations in parts for better performance. Neural network principles are effectively applied in process manufacturing industries to estimate the final product performance with measured variables beginning of the cycle [10]. This method also considers the variables relationship. However this system is proactive to only assembly, not for manufacturing. Also does not address products with multiple functions and parameters interlinked. This research focuses on method of reducing unit to unit product variation during mass production by complimenting variables with their relationships for each unit. This means that if all units of the product were to be functionally tested after production, there would be less variation between the performances of each unit. However the paper does not address the change in performance of a product through its life, or in different use condition/scenarios. Unit to unit performance variation of a product is the result of variations in its parts and processes. Fig. 1 shows the representation of variation inflow to the typical production process. Material qualily Parts controls Assembly controls Functional specification through drawings through drawings requirement Material variation Pans dimensional with in specification variations with in drawing specification Fig. 1 A schematic representation of the production process with controls and variations The research approach is to create a robustness monitoring system for production, which allows for understanding the incoming variation "a" and "b" (Fig. 1) and opportunities to compensate for them through "1" and "2" (Fig. 1). To create a model connecting the end product performance with all of the influencing variables, one needs to extract the relationships throughout, which are derived during product design and production tools development. 1.2 Parameter sensitivity Variations of the part dimensions influence different product functions. Their degree of influence is known as sensitivity and can be non-linear and therefore dependent on nominal value. Sensitivity plays an important role while improving the parts and their design parameters in production to achieve consistency in final product performance. 1.3 Parameter interactions It is not always the case that parameters influence product functions independently. Situations are present when the influence of a design parameter changes due to variations in the other design parameter. These interactions add more complexity when it comes to mapping influences of relationships in a design. However, good news is that the interaction effects are not as common as first order interactions and they also tend to have less influence than the first order interactions [11]. 160 Advances in Production Engineering & Management 11(3) 2016 Production monitoring system for understanding product robustness 1.4 Axiomatic design Complexity increases when each design parameter influences more than one function. For the design of a snap hook, the thickness of snap hook arm has an influence on the force required to deflect the arm and also the tensile strength of the hook once the snap has engaged. If we need to increase the tensile strength of the snap increasing the arm thickness will help but will create greater resistance to deflection. 1.5 Assembly process parameters Assembly strategy gets defined along with product geometry concept. Variables in assembly can be dimensions of the parts and also fixtures in use [12, 13]. Sometimes even sub processes, like amount of glue applied, torque applied, etc. can be assembly variables. When these variables connect to product performance, applying them to compensate for parts variations is an opportunity in assembly to achieve performance constancy. 1.6 Manufacturing process parameters The manufacturing process used for part production generates variation in part's dimensions. An injection moulding process relies on, pressure, temperature and cooling time, etc. as process parameters; similarly a machining process relies on speed, feed, tool size etc. as process variables. Those can be applied to generate the parts as needed. For example, in an outsourced part is made produced where the measurement report shows the batch to be close to the upper specification limit, a corresponding in-house part can be made closure to the lower specification limit to compensate. The basic principle of this approach is "as we cannot eliminate the variations, apply them in order to compensate one another, nullify their effect on the final product". The monitoring system developed in this article allows this approach to be done more effectively. 2. Method for building robustness monitoring system Engineering design philosophy builds the relationship of each design parameter to the final product functional requirements. Eq. 1, and Eq. 2 shows the simplest form of a product functional requirement and its variation, in which DP refers to Design Parameter and s refers to the Sensitivity of the function to variation of that DP. Fn = OlxDPl) + (>1xDP2)+ ..+(^snxDPn) (1) AFn = 01x ADP1) + (>1xADP2) + ..+(snxADPn) (2) DP variations (ADP) are caused by various process and equipment influences in manufacturing. Identifying all those Influencing Factors (IF) and quantifying the DP sensitivity to each of them is required to establish the link between variations in functional requirement to variations in manufacturing. The nature of the IFs derives the monitoring system requirements. The method followed to establish the monitoring system is shown in Fig. 2. Fig. 2 Steps followed to establish monitoring system content and structure Advances in Production Engineering & Management 11(3) 2016 161 Boorla, Howard 2.1 Identifying IFs and their nature Different influencing factors cause variations at various stages of production shown in the Fig. 3. The nature of all the IFs is not the same and production's ability to apply change differs. Compensating one IF by controlling another, is dependent on many aspects as outlined here: Time: In the chain of production activities, the first generated variation becomes the base for later IF to be adjusted accordingly. Certain outsourced parts arrive at the assembly warehouse before may constitute the first IF. Changeability: Certain influencers are rigid in nature. For example, a dimension in plastic mould is made 15 microns bigger is well within the machining tolerance, and cannot be changed in production. Agility: How quick production can act on IF also differs. Changing a tightening torque is quick, and may take only a few seconds but mould temperature change takes an hour to stabilize. This limit's the application while choosing the IF for compensation. Axiomatic conditions: When IFs affect the performance of multiple functions of the product, it becomes more complex to manage. Adjusting one IF to compensate for another, may bring the performance of one function back to the target value, but may have negative effects on other functions. Degree of Control: All IFs will not completely be within production control. For example, raw material characteristics are specified with certain variation acceptance. As long as it is maintained within the range, material batches are quality passed, and cannot be asked to change. These are semi controlled. Ambient temperature and humidity etc. are often uncontrolled. However, both semi-controlled and uncontrolled can be measured and compensated by other controlled IFs. Fig. 3 Mapping of IFs over production process 162 Advances in Production Engineering & Management 11(3) 2016 Production monitoring system for understanding product robustness 2.2 Establishing relationships Design Parameters (DPs) are identified at product design. Relationship equations of these design parameters to the Functional Requirements (FRs) are determined by the designed concept. Many products designs choose final assembly dimensions as targets to achieve FRs, e.g. spring compression length in final assembly is maintained in production to achieve push force function. These Dimensional Targets (DT) build with DPs to achieve FRs indirectly. Some cases DT itself can be FR, e.g. product length, flushness, gap uniformity, etc. Many assembly processes involve the use of fixtures to achieve DTs. In this case the dimensions of the fixture would be an assembly parameter (AP) which also influences on the FRs. However the relationship of manufacturing Process Parameters (PPs) to DPs is generated through the tools design (moulds, dies, fixtures, etc.)[14-16]. Fig. 4 shows the production process with variables identification. Here all APs and PPs are IFs. r (PPs] _J_ Material quality specification [DPs] Parts controls through drawings (APs) (DTs) Assembly controls through drawings (FRs) Functional requirement Fig. 4 Variables in discussion identified on production process 2.3 Monitoring system requirement The intention of the monitoring system is to indicate which function is varying, by how much and due to which IFs. After identification, the relationship between those IFs and the function requirement helps in determine by how much they need to be adjusted to improve intended function. Generic quality focused production approaches aim to maintain parts and assemblies as per drawing specifications. This ensures the functional requirements to achieve within specification. When unit to unit product robustness is in focus, FRs not only needs to be within specification, but they also need to be consistent from unit to unit. Product robustness as defined by John P King [17] is "A system that is more ROBUST is less sensitive to the sources of variability in its performance". To understand the robustness achievement, measuring and maintaining of performance variation is required. For this, production has to note, not only the DP achievement but also "how that DP is impacting on performance", requiring a change to the information flow from design to manufacturing. Table 1 shows the difference in both. Sensitivity of an FR to a DP, describes the ratio of how much variation is induced in the FR by variation of DP. This allows the calculation of the DP's contribution to that specific FR achievement [18]. Transfer function describes how the sensitivity of the FR to the DP changes for different values of the DP. If the transfer function is linear then the sensitivity will remain constant for any value of the DP. This allows an estimation of the exact change required in DP to achieve the required improvement in FR. Couplings/Axiomatic condition describes how all of the FRs are influenced by different DPs. This enables the FRs to be balanced while applying changes in DPs adding to the transparency of the effects of adjusting the DPs. Advances in Production Engineering & Management 11(3) 2016 163 Boorla, Howard Table 1 Robustness focused organizations need more information flow than those with a traditional quality focus Quality Robustness Purpose 1. 3D models 1. 3D models For making tools and fixtures 2. Drawings with design parameter controls/ specifications 2. Drawings with design parameter controls/ specifications To measure and maintain 3. Assembly process 3. Assembly process To establish assembly line 4. Sensitivity Count the DP contribution 5. Transfer functions Act according to the DP position 6. Couplings - Design parameters connected with more than one functional requirements Balancing FRs while changing DPs. It must be emphasised that individually, each of the above techniques are well researched and various tolerance analysis methods in practice allow for counting sensitivity and contribution of design parameters [19-24]. To calculate the performance of any product picked up from the end of the assembly line, one has to know the measurements of each part from the same assembly. Assembly lines of mass production work on different logistic principles, for example, in a Just In Sequence (JIS) system; parts from manufacturing units reach the assembly line in the same sequence as the assembly plan. In these systems part measurements happen at the part manufacturing location only. In the present globalized situation, often parts come from overseas. Measurement data captured at various locations needs to be bought together and analysed. Advancements in PDM/PLM tools in addition to part making and identification technology, make monitoring and adjustment approaches such as the one being proposed in this article, both a feasible and probable capability in the near future. 3. Results and discussion The principle of robustness monitoring system is "predicting functional performance by calculating with actual parts and processes achievement using their relationships ". From design, FRs and DTs flow down the system to DPs. Further relationships from DPs to PPs are generated through tools and equipment design. The assembly process gets defined at design but APs are derived from assembly line design. Linking PPs - DPs - APs - DTs - FRs of the product in an easy readable form is the backbone of the monitoring system. This also aims to display the variation contribution of each parameter. This indicates HOW product performances vary and directs WHICH parameter and HOWMUCH to adjust in order to compensate. Furthermore monitoring system gives the overview of HOW TO CHANGE by selecting the quickest and minimum number of parameters. A schematic representation of the robustness monitoring system is as shown in Fig. 5. The robustness monitoring system communicates three levels of information. Levell - Shows the status of final product Dimensional Targets and Functional Requirement. Mathematical relationships of DTs and FRs are derived from design philosophy. Level2 - Shows the status of Design parameters. The relationship between Levell and Level2 is derived through Assembly parameters. These relationships are determined from assembly equipment design. Outsourced parts are maintained by suppliers within specified limits and join at this Level. These DPs are known only once they have arrived and cannot be changed further. 164 Advances in Production Engineering & Management 11(3) 2016 Production monitoring system for understanding product robustness Level3 - Shows the status of Process Parameters as controlled, semi and uncontrolled. Controlled refers to production floor opportunities like, Speed, Feed, injection pressure, etc. Those can be varied within a set range of values anytime during production. Semi controlled refers to incoming variables like raw material characteristics which are within specified limits but cannot be changed every day or every instant of production. Uncontrolled refers to parameters that do not have any specifications like ambient temperature, humidity etc. the relationships with Lev-el2 are derived during the Tool design process (Moulds, Dies, etc.) by virtual simulation or physical DOEs before the Start Of Production (SOP). Fig. 6 shows the robustness monitoring system operating process flow and description of its steps. This monitoring system is in principle suitable for any type of product and process. Performance variation can be minimised using this tool without the need to tighten parameter tolerances. Once the system established, product upgrades and design improvements can be easily applied. Identifying the robustness monitoring requirements and building its structure is the key step for successful adoption. By linking PPs to their time and cost criteria the monitoring system can incorporate algorithms for suggesting the quickest and cheapest adjustments. Higher measurement frequency and data alignment increases the prediction accuracy. Higher complexity products, like automotive vehicle production, may need multiple monitoring systems, broken down in to different sets of relevant FRs. Whenever production tools and equipment is replaced, their PP sensitivities are to be updated. Robustness monitoring to be initiated during design and continued by the development team. This demands a strategic document flow along with stage gate process from design to manufacturing. Alignment between design and process parameter verification at digital and physical levels is critical for system reliability. Product Functional requirements Levell Level 2 Level 3 FR1 FR2 FR3 FRn DTI DT2 DTn Robustness Monitoring System PP1 PP2 PP3 PP ti+l Performance information Vjlow API DPI AP2 DP2 Performance information flow Robustness improvement Section flb DP3 Robustness improvement action flow APn DPn Assembly Parameters DP PPn PP 1 1 n Semi / Uncontrolled variable controlled variable Outsourced Fig. 5 A schematic representation of the robustness monitoring system connecting PPs and FRs Advances in Production Engineering & Management 11(3) 2016 165 Boorla, Howard Description \ t Calculate mean values and variation of FRs with all previous data ( End ) Ambient temperature, Humidity, raw material characteristics , etc are inputs to generate DPs. Time gap between data capturing and part production reduces the accuracy. Challenge is synchronization of varied frequency of inputs. FRs estimation tells the impact of input variables. This gives opportunity to work with controlled APs to compensate through controlled DTs Opportunity for APs are created through assembly process. Example, when the seal hardness is at higher side (semi controlled DP), tightening torque (AP| can be increased according, to get final DT required. APs change is limited to its machine/fixture built range. Multiple APs may compensate one or multiple input variables impact. Change requirement In PP can be understood precise, when relationship equations are accurate. Example, a higher density (semi controlled PP] raw material may be better processed with higher melting temp(controlled PP) to keep the DP required. Building these relationship through DOE Is key for success. PPs change limited to operating conditions defined trough manufacturing process. Multiple PPs may address the total compensation required. Increasing mixing speed may allow temperature to changeless. Estimation afteractions at controlled PPs and APs is aimed to bring the FRs to their mean value. Not able to get to the mean indicates that impact of uncontrolled and semi controlled variables is higher than controlled variables. Performance Estimation changes every change in variable either controlled, uncontrolled or semi controlled, It may be multiple times in a day or once in a week or can set for every hour. Capturing system need to be linked with application. Mean values may populate by day, week , month, etc. Day to day improvements are to keep the performances towards mean values. Lower the variation, higher the product robustness achieved. Fig. 6 Monitoring system application flow 4. Case study A portion of the PRECI-IN injection device concept have been simplified and modelled as a case study to exemplify the application of robustness monitoring system. 4.1 Information from design This module of injection device is attachable to various types of drug cartridges. A dose setting mechanism allows the user to dial a dose by rotating the scale, which in turn generates tension in a torsional spring. By pushing the button the spring tension is converted to axial movement of piston rod. Fig. 7 shows the assembly and parts with relevant design parameters. 166 Advances in Production Engineering & Management 11(3) 2016 Production monitoring system for understanding product robustness Functional requirements of Push Button Force (PBF), Dosage Accuracy (DA), Dialling Force (DF), Back Dialling Force (BDF), and Indicator Mismatch (IM) are considered for monitoring. Based on the design, the relationship equations of the FRs to DPs are imported from variation analysis to monitoring system. 4.2 Relationships of PPs to DPs Data driven production departments will already have good methods to determine the relative influence of the different PPs to the DPs. Dimensional variation of mass produced, injection moulded parts can be modelled based on the analysis of previous experiments determining the influence of different factors. Such experiments are common practice in production departments, although the systematic recording and re-use of data is not done by many companies. All DPs produced in-house are related to their PPs. For e.g. The DP, Pitch of the piston (Pp) from Fig. 7 related to moulding process parameters for that specific tool and machine characteristics simplified as in Eq. 3 Similarly the DP, button snap (Bs) from the Fig. 7 in Eq. 4. APp = (2-10-4 • AA/T) + (3-10-4 -AHP) - (2.5 • 10"4 • ACT) + (3- 1(T3 • A7) + (5 • 10-3 - AH) (3) Advances in Production Engineering & Management 11(3) 2016 95 Boorla, Howard ABs = (1.4 • 10-2 • AMT) + (6.4 • 10"4 (4 ■AHP) - (1.65 • 10-2 10"2 - AH) ACT) - (2-10-2 ■ AT) + (4) Where, AMT, AHP, ACT, AT, and AH are change in mould temperature, holding pressure, cooling time, ambient temperature, and ambient relative humidity, respectively. 4.3 The PRECI-IN robustness monitoring system Fig. 8 shows the experimental monitoring system for six PRECI-IN functional requirements, related to their PPs through DPs. As the concept does not contain assembly dimensional controls, no DTs are identified in Fig. 8. However the Indicator Mismatch (IM) is a final assembly dimension which is counted as an FR. As all the parts are assembled on to features of other parts, no Assembly Parameters (APs) are used and thus do not feature in Fig. 8. The captured status in Fig. 8 is a single instance of simulated production. The monitoring system treats all controlled variables (Yellow) as opportunities for change. The red cells are the measured but uncontrolled PPs, which can be entered as actual values. The orange cells are DPs of outsourced parts and therefore can only be entered as actual values based on measurement reports. Variation occurring in the FRs can be captured from Levell cells. To decrease the FR variation, the contribution of each DP and their sensitivity /contribution direction (whether positive or negative correlation) helps to identify which DP to change and in turn, which PP to change. In the current status, BDF (one of FR circled in Fig. 8) is highly deviated by -5.47 N. Opportunities for decreasing the variation is through the DPs, Ab, Hp, Dhl, Dhw and Dht. These DPs are linked to three PPs, MT-S1, HP-S1 and CT-S1. The intention is to change as few PPs as possible to improve BDF, at the same time, affecting the other FRs positive or minimally. Fig. 9 shows the FR improvement after PP change application through the monitoring system. Variation of BDF is reduced to -0.14 by increasing one of PP, MT-S1 (circled) from 90 to 102. PRECI-IN PRODUCT ROBUSTNESS MONITORING SYSTEM MT-Sl HP-SI CT-S1 I 90 If35 i I " » 11 5.3005 33 II lOTOO -ami" 0,02 SB il I a.™ I 0,0000 II 0.0000 ocei II 0,0000 II 0.0000 i r~ JL ]IZZI ~ll o.xco -0,0010 II -0,0270 0.0000 II 0,2000 0500 II -0,2000 0,0200 II 0,1200 0,0003 II -0,2000 3,0300 II 0r1000 0,0*00 II -0,0500 2000 M -0,1700 0.0250 II -0,0350 Uncontrolled variables Controlled variable Contribution inversely proportionate to variables Contributiondirectly proportionate to variable Semi-controlled var table Fig. 8 Monitoring system for six functions of PRECI-IN product concept 168 Advances in Production Engineering & Management 11(3) 2016 Production monitoring system for understanding product robustness PRECI-IN PRODUCT ROBUSTNESS DASHBOARD 1 ievell 1 L.v.u A' -s. Levels 1 iSFBF ¿DAI ¿DA2 AFDF £BDF AIM / / HT41 \HF31 CT-S1 MT-S2 HP-S2 CTÜ2 MT-S3 HP-SE CTÜ ï H TC [ 0.23 [ 0,00 1 [ -0,01 ][ -0,02 ] U»l 1 1 PREtl lN i 1 ™ |[,lS!i 11 13 II SO II 35 II S II 250 II 120 | 15 iKBHSHn J I 1 1 ......... 1 (KB 1 1 25,077 1 Af 1 0.0066 II 0.0000 II .............. 11 0.0000 II «.0000 11 0.071» 1 1 1 II II 1 6b,« 1 -0.26 1 1 S5.0SS |Ab 1 o.ioH-c II o.:cco II .............. Jl 0 0000 II o.ooaoM " 1 0.« 1 || o.oi II — ' n 0000 |( 0,0000 )| ........... ll 0.0300 1' 1 1 11 "-"Il II II II II 1 Fig. 9 Monitoring system after FR variation reduction The robustness monitoring system helps to identify the most effective parameters to act on for FR improvement, which leads to the minimum number of changes. In traditional quality focused approach organizations tend to identify all possible improvements across the production value chain. Brief comparison of both approaches is shown in Table 2. Table 2 Advantages of robustness focused approach over quality focused Task Traditional quality focused approach Robustness focused approach Performance improvement identification Final inspection/testing Predictive, before assembly/ manufacturing Improvement action identification Root cause analysis techniques Monitoring system directs the action Action focus Applying all possible improvements Changing minimum parameters Action reliability Less, being iterative process More, due to calculated approach Time for action identification Final inspection + Root cause analysis time Data filling time (instantaneous) Depending on the quality issue and the complexity of the product, the time saving from using proposed monitoring system to guide corrective action could be anything from hours to months. To give some examples of the impact, two quality experts were interviewed from different industries to describe the procedure and the time required to achieve typical quality aims. To ensure a reasonable comparison, projects were chosen with similar characteristics to PRECI-IN, in terms of, the number of components, plastic components and the forces involved. The context were also similar, where the performance was with-in specification but improvement intended. Table 3 describes the main differences between the PRECI-IN case and those described in the interviews. The industry influences the analysis procedure and time for concluding actions. Assembly cycle time indicates the minimum time required to make a PP change visible in the final product for each iteration. The production volume impacts the time available for iteration, The percentage of in-house manufacturing determines the number of controlled PPs. The time to conclude action and the # of DPs and PPs acted on were recalled/estimated by the interviewees. Analysing the nature of iterations in both the cases reveals the missing information, which can eliminate the iterations is shown in the Table 4. Table 3 Average time taken and number of DPs and PPs acted on in various industries Industry Production Cycle time In-house Time for No. of DPs No. of PPs manufacturing concluding action acted on acted on Automotive 6000/day 4 h 10% 7 days 2 7 Home appliances 300/day 3 h 10% 7 days 1 3 Advances in Production Engineering & Management 11(3) 2016 169 Boorla, Howard Table 4 Iterations and their related information missing Industry: Improved FR Concluding DPs Concluding PPs Missing information Automotive: Gap uniformity around the switch bezel found higher side in door trim assembly Iterative process : First - Bezel hook position changed equal to the non-uniformity observed. Second - Higher pressure on snap opposite to the hook lifted the bezel up and flushness disturbed, to reduce the stress snap interference reduced. Third - Uniformity not improved as expected, once again hook position changed. Iterative process: First - One uncontrolled PP has been changed Second - Second Uncontrolled PP has been changed Third - First changed PP is changed again. Along with three of controlled PPs also adjusted. 1. No DP to FR relationships are defined. 2. Specific DP change impact on other FRs is not known 3. Contribution of Uncontrolled and controlled PPs together in DPs is not clear. Home appliances: Mixer-A load transmission gear life is noted lower side of its defined warranty. Iterative process: First - Gear strength increased by changing the material grade. Second - As the gear life not increased as expected, material grade changed again for higher strength Third - Once again improved the material with latest grade. Iterative process: At every time of grade change, three PPs are re-established. 1. No FR to DP relationships established, No DP contribution analysis could perform. 2. No performance linkages available for narrowing correct DP. 4.4 Predictability accuracy The accuracy of prediction for any product at any instance of production depends on how frequent the uncontrolled and semi controlled variables measurements are available. If we are able to capture variation data for every part, with the monitoring system it is possible to predict the performance of every product coming off the line. When the variable represents a batch of parts, estimation accuracy is directly proportional to the batch variation. This is similar for uncontrolled PPs, such as ambient temperature and humidity. If the ambient temperature is noted for every 2 °C change, prediction accuracy is affected by 2 °C. Table 5 shows the list of variables influencing prediction accuracy. Table 5 Prediction accuracy of various performances influenced due to semi controlled DPs and uncontrolled PPs DP variation acceptance within the batch PP variation with in frequency Total influence Performance Rsl Dsa Dsp AT AH on prediction variation 0.4 mm 0.05 mm 0.05 mm 2 ° C 1 % accuracy APBH 0.20 NA NA 0.03 0.02 0.25 ADA1 NA NA NA 0.00 0.00 0.00 ADA2 NA NA NA 0.10 0.01 0.11 AFDF NA NA NA 0.40 0.49 0.89 ABDF NA NA NA 1.41 1.04 2.45 AIM NA 0.10 0.10 0.10 0.00 0.30 5. Conclusion Proposed monitoring system found capable to reduce final product performance variation dynamically by providing most effective adjustments in process parameters. This is analysed for injection moulded parts assembly case. Adapting this monitoring system as part of a project from the beginning allows ensuring the correct information flow from design. This shifts the present paradigm of quality control at mass production from part dimensions to product performance. Same tool can be further extended to estimate customer / stakeholder perceived quality loss, due to variation which can be defined at the beginning of the product development [25]. 170 Advances in Production Engineering & Management 11(3) 2016 Production monitoring system for understanding product robustness Some challenges that left for future work are: • Deriving relationship equations at the manufacturing stage demands conscious experiments and data validation. The challenge of applying uncontrolled variables in the experiments reduces the accuracy of relationship equations. • Industry follows several approaches to calculate contribution and sensitivity. This may lead to different interpretations of the same information. Acknowledgement The authors would like to acknowledge Novo Nordisk for the research funding under the DTU-Novo Nordisk Robust Design Programme. Authors thank the quality engineers and their respective organizations that participated and evaluated the research proposal. References [1] Helten, K., Hellenbrand, D., Lindemann, U. (2009). Product robustness as a basis for the improvement of production planning processes-key factors in early design phases, In: DS 58-7: Proceedings of ICED 09, the 17th International Conference on Engineering Design, Palo Alto, CA, USA, 197-206. [2] Howard, T.J., Ebro, M., Eifler, T., Göhler, S.M., Pedersen, S.N., Christiansen, A., Rafn, A. (2014). The variation management framework (VMF) for robust design, In: 1st International Symposium on Robust Design, Technical University of Denmark, Copenhagen, Denmark, 171-175, doi: 10.4122/dtu:2104. [3] Saha, A., Ray, T. (2011). Practical robust design optimization using evolutionary algorithms, Journal of Mechanical Design, Vol. 133, No. 10, 101012-101012-19, doi: 10.1115/1.4004807. [4] Ebro, M., Howard, T.J. (2016). Robust design principles for reducing variation in functional performance, Journal of Engineering Design, Vol. 27, No. 1-3, 75-92, doi: 10.1080/09544828.2015.1103844. [5] Ebro, M., Howard, T.J., Rasmussen, J.J. (2012). The foundation for robust design: Enabling robustness through kinematic design and design clarity, In: DS 70: Proceedings of Design 2012, the 12th International Design Conference, Dubrovnik, Croatia, 817-826. [6] Göhler, S.M., Howard, T.J. (2014). Framework for the application of robust design methods and tools, In: Proceedings of the First International Symposium of Robust Design 2014, Technical University of Denmark, Copenhagen, Denmark, 123-133, doi: 10.4122/dtu:2099. [7] Montgomery, D.C. (2009), Statistical quality control, Wiley, New York, USA. [8] Xie, M., Lu, X.S., Goh, T.N., Chan, L.Y. (1999). A quality monitoring and decision-making scheme for automated production processes, International Journal of Quality & Reliability Management, Vol. 16, No. 2, 148-157, doi: 10.1108/02656719910218238. [9] El-Midany, T.T., El-Baz, M.A., Abdelwahed, M.S. (2013). Improve characteristics of manufactured products using artificial neural network performance prediction model, International Journal of Recent Advances in Mechanical Engineering, Vol. 2, No. 4, 23-34. [10] Zhang, C., Liu, X., Shi, J., Zhu, J. (2006). Neural soft-sensor of product quality prediction, In: 6th World Congress on Intelligent Control and Automation, Dalian, China, 4881-4885, doi: 10.1109/WCICA.2006.1713312. [11] Frey, D.D., Li, X. (2008). Using hierarchical probability models to evaluate robust parameter design methods, Journal of Quality Technology, Vol. 40, No. 1, 59-77. [12] Xiong, C., Rong, Y., Koganti, R.P., Zaluzec, M.J., Wang, N. (2002). Geometric variation prediction in automotive assembling, Assembly Automation, Vol. 22, No. 3, 260-269, doi: 10.1108/01445150210436473. [13] Carlson, J.S., Söderberg, R. (2003). Assembly root cause analysis: A way to reduce dimensional variation in assembled products, International Journal of Flexible Manufacturing Systems, Vol. 15, No. 2, 113-150, doi: 10.1023/ A:1024453207632. [14] Kim, I.S., Son, K.J., Yang, Y.S., Yaragada, P.K.D.V. (2003). Sensitivity analysis for process parameters in GMA welding processes using a factorial design method, International Journal of Machine Tools and Manufacture, Vol. 43, No. 8, 763-769, doi: 10.1016/S0890-6955(03)00054-3. [15] Kazmer, D., Roser, C. (1999). Evaluation of product and process design robustness, Research in Engineering Design, Vol. 11, No. 1, 20-30, doi: 10.1007/s001630050002. [16] Nejad, M.K., Vignat, F., Villeneuve, F. (2012). Tolerance analysis in machining using the model of manufactured part (MMP) - comparison and evaluation of three different approaches, International Journal of Computer Integrated Manufacturing, Vol. 25, No. 2, 136-149, doi: 10.1080/0951192X.2011.627943. [17] King, J.P., Jewett, W.S. (2010). Robustness development and reliability growth: Value adding strategies for new products and processes, Prentice Hall, New Yersey, USA. [18] Göhler, S.M., Eifler, T., Howard T.J. (2016). Robustness metrics: Consolidating the multiple approaches to quantify robustness, Journal of Mechanical Design, (In Press), doi: 10.1115/1.4034112. [19] Augusto, O.B., Bennis, F., Caro, S. (2012). Multiobjective engineering design optimization problems: A sensitivity analysis approach, Pesquisa Operational, Vol. 32, No. 3, 575-596, doi: 10.1590/S0101-74382012005000028. Advances in Production Engineering & Management 11(3) 2016 171 Boorla, Howard [20] Hung, T.-C., Chan, K.-Y. (2013). Multi-objective design and tolerance allocation for single- and multi-level systems, Journal of Intelligent Manufacturing, Vol. 24, No. 3, 559-573, doi: 10.1007/s10845-011-0608-3. [21] Bosire, J., Wang, S., Khasawneh, M., Gandhi, T., Srihari, K. (2016). Designing an integrated surgical care delivery system using axiomatic design and petri net modeling, Advances in Healthcare Informatics and Analytics, Vol. 19, 73-101, doi: 10.1007/978-3-319-23294-2 4. [22] Ghanmi, S., Guedri, M., Bouazizi, M.-L., Bouhaddi, N. (2011). Robust multi-objective and multi-level optimization of complex mechanical structures, Mechanical Systems and Signal Processing, Vol. 25, No. 7, 2444-2461, doi: 10.1016/j.ymssp.2011.02.011. [23] Tong, C., Graziani, F. (2008). A practical global sensitivity analysis methodology for multi-physics applications, Computational Methods in Transport: Verification and Validation, Vol. 62, 277-299, doi: 10.1007/978-3-54077362-7 12. [24] Kackar, R.N. (1989). Off-line quality control, parameter design, and the Taguchi method. In: Dehnad, K. (ed.), Quality Control, Robust Design, and the Taguchi Method, Springer US, New York, USA, 51-76, doi: 10.1007/978-14684-1472-1 4. [25] Pedersen, S.N., Christensen, M.E., Howard, T.J. (2016). Robust design requirements specification: A quantitative method for requirements development using quality loss functions, Journal of Engineering Design, Vol. 27, No. 8, 544-567, doi: 10.1080/09544828.2016.1183163. 172 Advances in Production Engineering & Management 11(3) 2016 Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 173-182 http://dx.doi.Org/10.14743/apem2016.3.218 ISSN 1854-625G Journal home: apem-journal.org Original scientific paper Business plan feedback for cost effective business processes Ivanisevic, A.a*, Katic, I.a, Buchmeister, B.b, Leber, M.b aUniversity of Novi Sad, Faculty of technical sciences, Novi Sad, Serbia bUniversity of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia A B S T R A C T A R T I C L E I N F O Business planning encompasses all the goals, strategies and actions to ensure company's business survival, prosperity, and growth. Literature review and analysis of business processes of production systems show that the business plan is considered as a rigid system, even though it is being prepared in a world of constantly changing business conditions. The possibility of correction of a business plan that is being realized in the course of a year is only a theoretical possibility, and the introduction of a feedback system as an element of correction remains only as an idea. The aim of this paper is to propose and introduce a system in the business technology that would be similar to the designing principles for automated technical systems. In the paper an original business planning model with feedback is presented. The model includes planning, monitoring and harmonization of business operations. It is appropriate for unstable conditions too, regarding the essential influences from the business environment, thus adapting the company's operations. It could be used in small-and medium-sized companies, in industries of all types. The model enables the assessment of present and future business results. Verification of the model has been successfully carried out at three levels. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Cost-effectiveness Feedback Business plan Business process External and internal influences *Corresponding author: andreai@uns.ac.rs (Ivanisevic, A.) Article history: Received 21 March 2016 Revised 3 August 2016 Accepted 16 August 2016 1. Introduction Changes of business conditions affect the mechanism of income formation. This results in lower profit which sometimes leads to the loss and, not so rarely, to the shutting down of the production system. The consequences of deteriorating business conditions can be measured and, based on this, changes of some business conditions can ensue. The result of the changed business operation is calculated and if the correction is not satisfactory, the next correction is automatically proceeded with. The main elements that require correction are: cost reduction, production increase, reduction of staff, reduced salaries, etc. The preparatory work included the analysis of business operation of fifteen small and medium-sized enterprises, and a model was made based on their financial plans, planning systems, and business results. One of these companies was subjected to a test (theoretical) calculation of model application. The main question of this research is: is it possible to develop a new model of planning, monitoring and coordination of industrial enterprises in the course of the fiscal year, as a function of the character and intensity of the impact of changes in the environment? In accordance with the defined problem, the subject of the research presented in this paper is business planning of the industrial enterprises in the conditions of dynamic changes, and harmonization of business operations with the changes taking place in the everyday environment. 101 Ivanisevic, Katic, Buchmeister, Leber The objectives of the present study were to: • identify the key influences from the environment and mechanisms of business operation corrections for each influence separately, providing the appropriate mathematical calculations, • defines the procedures of harmonization of plan and business operations, • propose a scientific description of the new model of planning, monitoring and harmonization of business operations. The goal of the research activities was to develop a model of planning, monitoring and harmonization of operations which will apply the feedback mechanism to harmonize the operations with influences from the environment, thus adapting the company's operations with negative or positive impacts from the environment. 2. Literature overview The literature covering the subjects of business planning distinguishes between the business planning process which is continually implemented throughout the year, i.e. procedures used for developing the plans and results of business planning [1]. In addition, there is literature that emphasizes the importance of the process of business planning because it helps understand the business and offers the possibility of learning [2]. Planning enables enterprises to have control over the achievement of the objectives. In the case of deviations from the plan, the causes of those deviations can be identified and incorporated into future business operations [3]. The supporters of business planning emphasize that the importance of business planning is particularly evident in a dynamic and unstable environment because it reduces the level of planning uncertainty, facilitates and speeds up the decisionmaking process [4, 5]. It is a well-known fact that the external environment of the company (suppliers, customers, etc.), as well as the company's employees, often want to see the business plan in order to assess the viability of the business and the level of attraction from an economic point of view. Thus, the process of business planning often has a purely formal character and is a result of certain internal and external pressures due to obligations and legitimacy, instead of being an instrument to ensure better business results. Several authors [6] recognized these tendencies and described them. As it can be expected, the studies dealing with the analysis of the planning system in companies which do not have it as a formal process, determine different effects on business compared to those companies which focus solely on written documentation as a result of following the business plan during the year without reviewing it [7]. The literature proposes two options to help the company ensure continuity and stable operation, that is, its survival and development [8]. The first option proposes greater degree of company's isolation from the environment which implies break of company's relations with its own environment, and, although possible, this does not give a positive result. Another option is to build mechanisms by which the company would regulate its own functioning, i.e. to adapt the operating system to external influences. The process of adaptation introduces changes in the system in order to achieve stability and continuity of business operations. Adaptability exists in the degree to which the system can survive the changes caused by the external environment, i.e. viability equals the adaptability [9]. Some companies find it difficult to adapt to changes occurring in the environment for several reasons. Companies often do not have developed systems to monitor the changes occurring in the environment, and systems that register different types of impacts. How to implement changes is a key question for every organization or company. Change management (also known as change control) is a professional discipline, which focuses on supporting organizations on their way to a successful transition from a less-than-ideal status quo to a desired future state. Change management is one of the skills every manager should master to a sufficient degree because it represents an integral part of business operations and the process of constant change. It denotes 174 Advances in Production Engineering & Management 11(3) 2016 Business plan feedback for cost effective business processes a dispersed set of processes, tools, techniques, methods and approaches for achieving a desired state through change. Change management approaches have two main objectives: • to assist the organization in achieving its goals which cannot be attained with the existing organizational structure, functioning and client servicing, and • to minimize the adverse effects of any changes made [10]. The implementation of lean manufacturing methods is very important for optimization of business processes. Nguyen described the implementation of lean concepts in the context of developing countries [11]. Adjustment of the structure and parameters of production systems should ensure the operation of these systems in more favourable manufacturing and economic conditions [12]. This process is often directly linked with the starting of investment process with the goal of achieving minimal production costs [13]. When it comes to adjusting the mechanisms of company's operations to the changes that result from internal and / or external influences, contemporary literature commonly uses the term of "adjustment of business operations based on the feedback mechanism"; however, it does not offer an elaborate and usable model. As this is the main subject of this paper, the review of relating literature has shown that except the theoretical explanations, there is no operationalization of the subject model. 3. Research The management and planning models are mainly oriented towards the future, giving priority to the preventive control over subsequent control, with the aim of undertaking the prevention measures before the differences between the planned and actual performance occur. It is clear that, in a number of cases, the recognition of deviations outside the set limits is much more important because, then, there is a need to redefine the initially established plans and make them more flexible. Thus, the control can be viewed as a causal variable that provides input for improvement of planning and organization in the event of changes in the internal or external environment. A number of control classifications can be found in literature, and one of them is the division into: • preventive, feedforward control, • corrective, feedback control. The preventive control (feedforward control system) was noted to be more effective when applied to business processes because, in the corrective control system (feedback system), the correction output is returned to the process flow. With preventive control (feedforward system), the unwanted variations of inputs are returned to the flow of inputs to be corrected, or into the process itself, before the output is completed. Preventive control should be defined by comparing it with subsequent control which has been defined by the authors for different disciplines, but which basic idea can be easily applied in the field of management. Considering the existence of different views, different approaches and, finally, the existence of different planning systems in companies, a general conclusion is that there is no consistent concept or model of a business plan that can be uniquely determined and widely accepted. This paper is a contribution to the development of this issue, as it sets a model of flexible business planning system based on the principles of feedback mechanism. The main purpose and objective of a business plan is to define the criteria for basic principles and directions of business operations during a fiscal year so that the fiscal year can end within the limits of forecasted or reduced, but still above the minimum projected profit. As the business plan is typically made before the start of the fiscal, usually a calendar year, there is a fact that that it is very difficult to predict all operating conditions, the intensity of external and internal changes, and the impact on the realization of the business plan during the year. Advances in Production Engineering & Management 11(3) 2016 175 Ivanisevic, Katic, Buchmeister, Leber A complete basic business plan of a production system and its harmonization with altered business conditions can be only considered as a guideline for appropriate corrections of the business system and allocation of resources that the production system has at its disposal. Bearing in mind that unstable conditions make it almost impossible to predict, with high accuracy, all possible changes of the conditions, the only option that remains is to define the business plan only as the starting framework so that it can be corrected in accordance with the changes of various external and/or internal influences, but it also to show the company's management possible directions of business operations in order to stop, slow down or improve the negative trends of profit and/or other performance indicators. The basic principles are illustrated with short explanations and illustrations. This is a flexible business plan that allows introduction of changes based on which the effects on profit are automatically calculated, and it proposes orders for the correction of some elements of the business which makes it a system with the feedback (Fig. 1). Feedback Fig. 1 Block diagram of the mechanism of business planning with feedback The difference between total income (Z1) and total expenditures (X2) is the profit of business system (X3P/). External and internal influences affect Xi and X2 directly and, through them, reduce the planned profit (X3P/). According to the model developed in the study (PPS), the amount of profit calculated with respect to external or internal type of „influences", and the calculated value for profit (X3i) are automatically introduced as the correction coefficients which number and numerical value show the type of change and average annual profit K = f (X3). The whole process of calculation of profit and correction coefficients is completed automatically except the occurred changes which are imported manually in accordance with the criteria defined by the model. The necessary basis for the application of business plan with feedback (PPS) is the preparation of the basic business plan which structure is adjusted to the program of profit calculation with respect to the type and intensity of the change „EI influence". 3.1 Business plan model Table 1 shows a list and groups of elements of the plan, each with a mark that is used in the overall model of business planning with feedback (PPS) regardless of whether it is an analysis, computer processing or other activities. Every group of the plan elements is elaborated thoroughly and the plan will include only planned numerical values of the elements that are valid for a company that uses them. The business plan is done annually (Table 1). It is prepared based on the projections for a business year and, as such, it is unalterable regardless of external and/or internal changes. There is the large number of different negative 'influences' on business operations: • externa/ influences: inflation, reduction in sales volume, increase in energy prices, reduction in product prices, increase in transport costs, increase in contribution and taxes, increase in business costs, increase in loan interests and other, • interna/ influences: increase in personal incomes, reduction in production, increase in credit debt, inadequate maintenance services, unplanned failure of machines and other. 176 Advances in Production Engineering & Management 11(3) 2016 Business plan feedback for cost effective business processes Table 1 The concept of business plan - basis for the model construction Mark Name of variable Basis for the plan or calculation Xi TOTAL INCOME Calculated X2 TOTAL EXPENDITURES Calculated X21 BUSINESS EXPENDITURES Calculated X22 INVESTMENTS Planned X23 FINANCIAL EXPENDITURES Calculated X24 OTHER EXPENDITURES Calculated X3 PROFIT X1 - X2 Calculated It is certain that a unique automatic program cannot include all the influences on business operations because of both a large number of variables and the fact that those variables do not often act individually. Usually, there is a combination of two or three variables at the same time and they often have different influence on business operations with the same performance. This way, for example, the following can be combined: reduction in sales volume and price reduction. In addition, the degree of change of different EI influences on business operations can neither be equal. By combining different number of influences of varying intensity and level that can be made on business operations, then thousands of different groups of influences can be obtained which would make the planned and designed model completely useless, not only because of a large number of combinations but because of the numerous mistakes that can be made in case of development of the model itself and definition of initial numerical values of influences and numerical valorization of the degree of their impact on business operations. The selected 'influences' can be divided into two groups and are included in the business planning model with feedback (PPS) in the data processing which is used for: a) automatic calculation of the profit with feedback (reduction in sales volume, production price, increase in costs of business operations) and b) analysis of EI influences only for the calculation of the future business results on a onetime basis without any corrections and feedback (increase in the price of raw materials and energy prices, increase in employees' incomes, increase in values of external services and internal costs). 3.2 Definition of typical zones of profit and introduction of business correction coefficients The basic criteria for business performance - profit (X3) in PPS model is determined by five typical levels and four zones of profit (Fig. 2). The set levels and zones of profit are defined in the PPS model based in Fig. 2. However, considering the fact that it is an open model, the levels and zones can be changed in accordance with the concepts of company's management or specific position of a company on the market. A. Start: Planned profit X3R - the first zone at the beginning of the planned period X3pl. B. Second profit level which is in compliance with the established relationship between the calculated and planned profits: X3r1 = 0.55 • X3pl (1) C. Third profit level complies with the established relationship between calculated and planned profits: X3r2 = 0.30 • X3pl (2) D. Fourth profit level complies with the established relationship between calculated and planned profits: X3r3 = 0.15 • X3pl (3) E. Fifth profit level complies with the established relationship between calculated and planned profits: X3kr = 0.10 • X3pl (4) Advances in Production Engineering & Management 11(3) 2016 177 Ivanisevic, Katic, Buchmeister, Leber X3R -1 planned profit X3R = 0.55 X3PI - F^t level profit i k X3R = 0.30 X3pl - Second level profit X3R = 0.15 X3pl - Third level profit 1 ' 1 L XjGK-critical profit level at the end of business year time Fig. 2 Typical levels and zones of profit in case of EI influences (X3pl - planned level of profit; X3R - calculated level of profit 1, 2 or 3; X3R - critical level of profit) Coefficients for defining the profit levels are provisional (0.55, 0.30, and 0.15) and can be changed. Four zones of profit, which depends on the intensity of EI influences, are defined within the specified values (Fig. 2) and this paper focuses only on the summary of the research (model): • first zone in which the calculated profit ranges: 0.55 • X3pl < X3r < X3pl - a zone in which no correction of business operations is performed, • second zone in which the calculated profit ranges: 0.3 • X3pl < X3r < 0.55 • X3pl - a zone in which the first level of correction of business operations K1 is introduced on a one-time basis based on the criteria defined for each EI influence separately, • third zone in which the calculated profit ranges: 0.15 • X3pl < X3r < 0.30 • X3pl - a zone in which the second level of correction of business operations K2 is introduced on a one-time basis based on the criteria defined for each EI influence separately, • fourth zone in which the calculated profit ranges: 0.00 • X3pl < X3r < 0.15 • X3pl - a zone in which the third level of correction of business operations K3 is introduced on a one-time basis based on the criteria defined for each EI influence separately; the critical level of minimum allowed profit X3kr is in this zone. At the third level of correction (the fourth zone of profit), the system of feedback is introduced based on three bases: • first, when, after the introduction of the first corrective measures (K3.1), the profit remains in the fourth zone and does not pass into the second zone, the correction is repeated (K3.1), • second, when, after the introduction of the first corrective measures (K3.1), the profit changes and passes into the third zone and during further business operations it again enters the fourth zone - the second corrective measures are introduced (K3.2), and • third, when the profit is below the critical level X3kr and when the third level of corrective measures is introduced, comprising repeated corrections K3.1, and then K3.2 until the profit is increased above the critical level. The reduction of the planned profit X3pl to the level indicated later in Fig. 4 as X3.12 is the result of EI influences and depends on the SPV coefficient. When the decrease in the profit is so large that the profit passes into the second zone (0.55 • X3pl < X3R < X3pl), the business operations are corrected based on the defined criteria. A new variable is introduced in the business planning model with feedback: time and duration of the change, and then it is connected with the calculated results of business operations which are presented through the achieved profit. This is done because of adverse effects which, besides 178 Advances in Production Engineering & Management 11(3) 2016 Business plan feedback for cost effective business processes they appear in different periods of a year, never change evenly during a year, 1 %, 2 %, 3 % (for example, there is an increase of 1 % every month). When we take into account the time constant, we should bear in mind that the change in business plan could be analyzed ahead by defined time cut-off points and/or at the moment of change. A system of parallel monitoring in time (for example, every month) and at the time of change have to be applied for the complete PPS model application. Therefore, conceptually different profits can be defined: • planned profit (X3pl), • present value of the profit in the month when such a change occurred or at any other cutoff point X3R, • average profit of a company in the period preceding the change (X3p), including the profit in the month when the change occurred (analyzing the influence of the change on the profit in the preceding period), and • average profit of a company during a year (X3g) under the conditions when the last change occurred in the current year (analyzing the influence resulting from the change on the business operations throughout a year). Mathematical interpretation of the influence of 'time and duration of change' variable on the profit for all three methods is presented graphically in Figs. 3 and 4. X3 X3pl X3P SPV (EI influences) « X3G t X3T a zone of profit at the time of change during a year a zone of profit after the change with time variables at annual basis. 12 Fig. 3 Graphical overview of defining a profit Fig. 3 shows an average profit during a year (distributed to 12 months), X3G. The numerical value at the cut-off point (i) at annual basis is calculated based on the following equation: %3GÍ — X 3G(t-l) • m X — + 3 Gi (12 — m) mr mr (5) where i is a cut-off point during which a profit is calculated at annual basis, m is a number of months of business operations to the i-cut-off point, mG is a number of months in a year (mG = 12), X3G1 is the calculated level of profit at the i-cut-off point of change in EI conditions, obtained as a difference between total revenues and total expenditures, X3G(M) is the calculated level of profit at previous cut-off point of change in EI conditions. The calculation of change in profit is presented graphically in Fig. 4. Fig. 4 shows the calculation of corrections of business operations under the modified business conditions which, in the planned year, lead to the fall in planned profit during a year at all levels. When the business conditions, which have adverse effects, change and the profit falls from X3pl to X3.11 (at annual level, but not below the limit which is marked with the first level X3 = 0.55 • X3pl), the first correction K1 is made, and then by applying the mechanism of calculation based on 'time variable', the planned profit falls annually (or it does not fall) to the level X32.1 at annual basis. In case that, for example, the business conditions change during a year and the profit falls under the level 2 (X3R = 0.30 • X3pl), then the correction is made based on the criterion defined as K2. If the hypothetically defined fall in profit continues during a year, then the correction K3 is made. Advances in Production Engineering & Management 11(3) 2016 179 Ivanisevic, Katic, Buchmeister, Leber Xa-1 Î The level ofplaruied profit at annual bans Xjpi Profit reduction (j) SPV X3 1Z -Ki X3 = 0.55 X3p) X3.22 I Profil © I reduction 1X3.21 , ■ X3 31 1 X3 31 7 X332 2 X» = 0.30-XSpt K3 1 - X3G K3I X3r = 0.15-Xïpi — X3 32Î time Fig. 4 The change in designed profit due to EI influences and mechanisms of corrections of business operations - K1; K2; K3.1; K3.2 All impacts on business, which lead to the fall in profit, and correction coefficients are clearly marked within the model and they are only numerically valued due to mathematical processing of the model and they can be easily defined in line with the business conditions of the company which implements the described model. This has already been done because, for example, the increase in the fuel price has different influence on business operations of the transportation company then on the business operations of the metalworking company. 3.3 Mathematical statement of business plan elements and procedures of corrections of business operations In mathematical statement of the PPS model, elements of the plan can be divided into several groups with the same mechanism of operation: • Elements of the plan that change due to external-internal influences (through changes defined as SPV). Those are the initial changes and their numerical value follow the change of EI influences. • Elements of the plan that change (through PKP coefficient) in compliance with the change of EI influences, follow it because of the plan correction, and they are introduced when the profit is in the zones 1, 2 or 3 (presented in Fig. 2). Those are the elements of correction of costs which aim is to achieve the harmonization between the reduced and planned (X3) profits. The numerical value of these variable coefficients of the plan correction (PKP) follows, with the same percentage, the change in EI influences expressed through SPV coefficient. • Elements that are defined and constant until the next planned change (through KPK coefficient), the so-called constant elements (KPK1 = 1Y, that is, KPK2 = 0.5-7; Yin %) and corrections of plan costs with the aim of adjustment are reduced by the planned (X3) profit. The introduction of KPK is not related to the change in SPV but to the moment the profit enters critical zone 3. Mathematical statement of a single case of impact - reduction in the product price: X3 = (X11 + Xi2>SPV - (( X211 + X212 + X213PKP + X214KPK + X215PKP) + (X221+ X222PKP) + (X231PKP + X232PKP + X233PKP + X234PKP + X235PKP)) (6) where: Y SPV (PKP; RKP) = 1 - 100 (7) 180 Advances in Production Engineering & Management 11(3) 2016 Business plan feedback for cost effective business processes and Y is a change (reduction -; increase +) in %, SPV is a constant change in business operations, initial influence, PKP is a variable coefficient of correction of business operations, KPK is a constant coefficient of correction of business operations, X11 are the revenues from domestic market; X12 are the revenues from foreign market, X211 are the costs of raw materials from domestic market, X212 are the costs of raw materials from foreign market, X213 are the production services, X214 are the personal incomes, X215 is the investment and development, X22i are the costs of credit, X222 are other financial expenses, X231 are the costs of fuel and energy, X232 are the costs of transportation services, X233 are the maintenance costs, X234 are non-production services, X235 are other expenses. 4. Concluding remarks A flexible planning system, which follows the logic of thinking, that is, understanding of the company's management, should result in generally accepted business philosophy around the world - profit maximization. The research on needs, possibilities for design and implementation of business planning model with feedback were carried out based on the formulated hypotheses. The achieved level of research in the field which is the topic of this paper (business planning with feedback) was analyzed during preparatory works. A large number of papers deal with the problem of doing business in unstable conditions and demonstrate the need to implement business planning with feedback. Some principles are mentioned, however, the designed model, or its preliminary version, could not be discovered. In the paper, this idea was developed and designed in a model-based research. Verification of a company's business planning model with feedback (PPS), as a process of adjustment of company's business operations with plans under the influence of environment, was carried out at three levels. The aim of the verification was to: a) Include the most important elements of business operations in the plan which was defined as alterable for companies of different size and configuration, b) Examine the possibilities for implementation of model in the company's business, c) Analyze the achieved results, assess their real meaning and application in current business operations, d) Examine the possibilities for implementation of the model in the process of assessment of business results in the future, assumed business conditions and anticipated influences from the environment. Based on the conducted research, the goal to develop a verified model for business planning, monitoring and harmonization has been achieved. The model proposed in this paper can be upgraded in various ways. One option is to upgrade this model with the change management model. Given the complexity of the problem, the dynamics of changes in the environment and lack of the correct solutions in the literature, even in its initial form, the aim is to continue with the research on this topic with focus on: • Programming of the business planning model with feedback (PPS) in some of the software applications which configuration is regulated to solve the mentioned problem. • Implementation of the business planning model with feedback (PPS) in a few selected typical companies, according to which the entire process of planning, monitoring of changes and results of business operations would be adjusted with the model. Advances in Production Engineering & Management 11(3) 2016 181 Ivanisevic, Katic, Buchmeister, Leber References [1] Badri, M.A., Davis, D., Davis, D. (2000). Operations strategy, environmental uncertainty and performance: a path analytic model of industries in developing countries, Omega, Vol. 28, No. 2, 155-173, doi: 10.1016/S0305-0483(99)00041-9. [2] Dyner, I., Larsen, E.R. (2001). From planning to strategy in the electricity industry, Energy Policy, Vol. 29, No. 13, 1145-1154, doi: 10.1016/S0301-4215(01)00040-4. [3] Lekovic, B., Ivanisevic, A., Maric, B., Demko-Rihter, J. (2013). Assessment of the most significant impacts of environment on the changes in company cost structure, Economic Research, Vol. 26, No. 1, 225-242, doi: 10.1080/1331677X.2013.11517599. [4] Dean Jr, J.W., Sharfman, M.P. (1996). Does decision process matter? A study of strategic decision-making effectiveness, Academy of Management Journal, Vol. 39, No. 2, 368-392, doi: 10.2307/256784. [5] Delmar, F., Shane, S. (2003). Does business planning facilitate the development of new ventures?, Strategic Management Journal, Vol. 24, No. 12, 1165-1185, doi: 10.1002/smj.349. [6] Honig, B. (2004). Entrepreneurship education: Toward a model of contingency-based business planning, Academy of Management Learning & Education, Vol. 3, No. 3, 258-273, doi: 10.5465/AMLE.2004.14242112. [7] Oltra, M.J., Flor, M.L. (2010). The moderating effect of business strategy on the relationship between operations strategy and firms' results, International Journal of Operations and Production Management, Vol. 30, No. 6, 612638, doi: 10.1108/01443571011046049. [8] Miller, C.C., Cardinal, L.B. (1994). Strategic planning and firm performance: A synthesis of more than two decades of research, Academy of Management Journal, Vol. 37, No. 6, 1649-1665, doi: 10.2307/256804. [9] Bittlingmayer, G. (2001). Regulatory uncertainty and investment: Evidence from antitrust enforcement, Cato Journal, Vol. 20, No. 3, 295-325. [10] Vedenik, G., Leber, M. (2015). Change management with the aid of a generic model for restructuring business processes, International Journal of Simulation Modelling, Vol. 14, No. 4, 584-595, doi: 10.2507/IISIMM14 (4)2.302. [11] Nguyen, D.M. (2015). A new application model of lean management in small and medium sized enterprises, International Journal of Simulation Modelling, Vol. 14, No. 2, 289-298, doi: 10.2507/IJSIMM14(2)9.304. [12] Cerjakovic, E., Topčic, A., Tufekčic, D., Veža, I. (2015). Influence of structure of manufacturing system and amount of investment on production costs, Tehnički Vjestnik - Technical Gazette, Vol. 22, No. 3, 771-780, doi: 10.17559/TV-20140307103216. [13] Zhao, R. (2012). Simulation-based environmental cost analysis for work-in-process, International Journal of Simulation Modelling, Vol. 11, No. 4, 211-224, doi: 10.2507/IJSIMM11(4)4.218. 110 Advances in Production Engineering & Management 11(3) 2016 Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 183-191 http://dx.doi.Org/10.14743/apem2016.3.219 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Studies of corrosion on AA 6061 and AZ 61 friction stir welded plates Raguraman, D.a*, Muruganandam, D.a, Kumaraswami Dhas, L.A.b aDepartment of Production Engineering, Sri Sairam Engineering College, Chennai, India bDepartment of Mining Machinery and Engineering, Indian Institute of Technology Dhanbad (ISM), Dhanbad, India A B S T R A C T A R T I C L E I N F O A remarkably new welding process, namely friction stir welding (FSW) has reached a tremendous research interest on the present decade due to bonding of similar or dissimilar materials at solidus state. This welding technique is environment friendly and versatile. In specific, FSW can be used to join the high strength aluminium alloys and other dissimilar alloys that are difficult to weld by conventional fusion welding. The process parameters have a major role in changing the characterisation of the joint. In this work, three parameters of the weld, namely rotational speed (rpm), axial load (KN), and weld speed (mm/min) are considered. Three pairs of AA 6061 and AZ 61 plates were welded with three different sets of these parameters. The welded zone was immersed in corrosive solution of NaOH for six months period. Corrosion behaviour was studied with the help of SEM and EDAX. Through this investigation, the importance of weld parameters control for the study of effects on the susceptibility for corrosion on the welded region can be sought. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Friction stir welding AA 6061 AZ 61 Tool geometry Corrosion behaviour *Corresponding author: raguraman150807@gmail.com (Raguraman, D.) Article history: Received 15 December 2015 Revised 24 May 2016 Accepted 16 June 2016 1. Introduction The corrosion is caused by the chemical and/or electro chemical reaction of metals with environment. Due to the conservation and safety requirements of alloys the need of investigation on corrosion arises. To reduce the impact of corrosion, corrosion engineers and scientists made a major investigation on the corrosion of piping, bridges, marine structures, ships, metal components of machines and so on to reduce material losses and to utilize the conservation metal usage. Corrosion study is vast important in the safety point of operating equipment. Loss of metal by corrosion is not only affecting the physical strength but also raising the cost consumption for the replacement of the corroded metal structures within its useful life. In addition, rebuilding of corroded components involves further investment of all the men, materials and machines resources. It is well known that aluminium is one among the most abundant metals in nature. It is ductile in mechanical characterization and can be easily cast and machined. Adequate properties kept aluminium as a different alloy from other alloys. First, it is lighter compared to all other engineering alloys except magnesium and beryllium. It has a density value of about 2990 kg/m3. A second noted property of aluminium is its electrical and thermal conductivity. The third property which is made the most responsible for the selection of aluminium alloys is their corrosion resistance. Resistance welding can be preferred on some aluminium alloys but the surface preparation is expensive and the formation of surface oxide being a major problem. 183 Raguraman, Muruganandam, Kumaraswami Dhas The magnesium series alloys are often used in automobile applications, marine and aviation due to their high strength to density ratio. The challenge on making fatigue and fracture resistant welds in aluminium alloys have been found wider use for joining aerospace structures. In the case of magnesium alloy, if the aluminium content is a dominant alloying element then it is characterized as magnesium alloy AZ61. Aluminium and magnesium are found as one of the lightweight alloys with noted corrosion resistance with good thermal and electrical conductivity. The remarked corrosion behaviour of aluminium is effected by the small amounts of impurities present in the metal; all these impurities, with the exception of magnesium which tends to be cathodic to aluminium. The distinct applications of aluminium alloys in aerospace and automobile industries directs the choice on welding behaviour and selection of most appropriate welding method. Aluminium alloys of 2xxx, 6xxx and 7xxx series have been adopted for remarkable usage in these industries [1]. This transpires the desirable strength to weight ratio, good form ability, appropriate weld ability and acceptable corrosion resistance [2]. According to the particular application, corrosion behaviour have a major role on the strength of welded joint [3]. Magnesium (Mg) alloys are considered to be one of the light weight metallic alloys due to its higher mechanical stiffness and lower density which is around 1.74 g/cm3 [4]. In the presence of seawater, the benefits of magnesium are distinctive by high corrosion rate compared to aluminium or Steel [5]. The areas unexposed to the atmosphere such as electronic boxes and car seats have been regulated its usage of alloy by high corrosion resistance of magnesium [6, 7]. Earlier investigations on the tool geometry design were aimed on optimization of the tool pin with respect to mechanical properties and micro structure [8-11]. Recent investigation on corrosion behaviour on aluminium alloy 6061 reveals that control of grain size enhances the susceptibility to corrosion and intermetallics dominates the role on formation of galvanic corrosion couples [12]. Also as per microstructure analysis on corroded surfaces of Al-Zn-Mg aluminium alloy 7039 FSW joints, the different weld zones like HAZ, TMAZ and NZ are compared for corrosion behaviour. By comparison the Heat Affected Zone (HAZ) was more susceptible to corrosion [13]. The studies focused to the effect of tool pin profile and change of weld parameter on the corrosion behaviour and micro structure on corroded surface are less [14]. The current investigation is with corrosion behaviour of FSW joints with different weld parameter for threaded pin profile. 2. Friction stir welding 2.1 Process The basic principle of FSW is concerned with a metal flow of metal/alloy to be welded by a combination of axial load and rotational as well as transverse feed by a rotating tool which is having a specifically designed probe (pin) and shoulder profiles. The tool preserves two primary functions such as heat input on the metal to be welded and metal flow mechanism in the joint The heat input is generated by friction on the tool and the metal/alloy interface. The localized heat generation softens the metal/alloy which is welded closer to the probe and combination of tool rotation speed and transverse speed plays major role in the metal flow. Because of different geometrical features of the tool profiles and the metal flow intense plastic deformation set up. Due to the efficient utilization of energy, environment friendly feature and versatility the FSW is considered to be the most effective metal joining method in the recent decade. 2.2 Tool geometry The design/selection of tool geometry is the influential aspect of heat input in FSW process. Since tool geometry have a critical role in material flow and in turn it governs the heat generation rate at which FSW is processes [15]. An FSW tool consists of a shoulder and a probe (pin) as shown schematically in Fig. 1. 184 Advances in Production Engineering & Management 11(3) 2016 Studies of corrosion on AA 6061 and AZ 61 friction stir welded plates Sufficient force to maintain registered contact Retreating side of well d Leading edge of the rotating tool Advancing side of weld Triflute Probe Fig. 1 A schematic view of friction stir welding [16] As per literature, the tool has two primary functions one is a mechanism of material flow, and the other one is localized heating. In tool plunging, the heat generation is primarily due to the friction on the interface of tool and base metal/alloy. Later additional heat results from plastic deformation of material. From the heat generation point of view, the relative size of tool probe and shoulder is significant. According to the recent numerical evaluation the weld nugget zone experiences a serious compression and shear [17]. The uniformity of micro structure and physical properties are affected by the tool design. Generally a threaded cylindrical pins and concave shoulder are used. Complex features on tool profile have been added to improve material flow and reduction of process loads. Fig. 2 shows the tool geometries tested for the corrosion behaviour. Featureless Shoulder Scrolled Shoulder (viewed from underneath) Shoulder-Pin ■ Threaded Pin Threaded Pin with Flutes Fig. 2 Line sketch of friction stir welding tool used for the butt joints of AA 6061-AZ 61 alloys for corrosion behaviour study [15] 2.3 Welding parameters As per the trend on weld parameters of FSW for investigation tool rotation rate (v, rpm) and tool traverse speed (n, mm/min) are the most involved along the line of joint The rotation of tool with some axial load results in stirring and mixing of base metal/alloy to be welded and the translation of tool pushes the stirred material from the advancing side to the retreating side. Higher tool rotation rates set up a higher temperature because of higher friction and results in more influences on stirring and mixing of material. However, the friction of tool with weld metal is responsible for the heating [18]. 3. Experimental work The corrosion behaviour was tested experimentally for the components which are joined with different weld parameters using friction stir welding. Advances in Production Engineering & Management 11(3) 2C1S 185 Raguraman, Muruganandam, Kumaraswami Dhas 3.1 Welding Two alloys were chosen for the current corrosion investigation of FSW butt joint. One is from the AA 6XXX series - AA 6061. The other one is from the magnesium alloy series - AZ 61. Three plates each of 5 mm thickness of these alloys were taken. The dimensions of the plates are 100 mm x 100 mm. The friction stir welding of these plates was carried on these plates using three different weld parameters listed as in Table 1. Table 1 Weld parameters of the three samples Samples Weld parameters ------- A B C Load (kN) 10 12 16 Rotational speed (rpm) 400 600 1200 _Transverse speed (mm/min)_30_40_50_ Thus, three different samples were prepared. These samples were left as such for six months. During this aging period, the atmospheric corrosive agents will affects the defective surfaces on the welded region, if any. The aged plate is then taken for further analysis. 3.2 Corrosion testing To examine the effect of corrosion on the weld it was decided to immerse the welded region in strong alkaline solution for specific time periods. Then NaOH solution of pH 8 was prepared. The welded portion of each sample was cut into five pieces of 10 mm width, Fig. 3. These were separately immersed in 100 ml of the NaOH solution prepared. They were immersed for six month time periods. After removing the samples from the solution, they were washed in distilled water. Then they were washed with acetone to prevent further corrosion of the samples. These samples were concealed in airtight covers and labelled. A few photographs of the samples tested are shown in the Fig. 4. Fig. 3 Welded sample: A cut into pieces for corrosion testing (a) (b) (c) Fig. 4 Welded samples dipped in NaOH: (a) sample A (FSW joint with parameters axial load 10 kN, rotational speed 400 rpm and transverse speed 30 mm/min); (b) sample B (FSW joint with parameters axial load 12 kN, rotational speed 600 rpm and transverse speed 40 mm/min); (c) sample C (FSW joint with parameters axial load 16 kN, rotational speed 1200 rpm and transverse speed 50 mm/min) [19] 186 Advances in Production Engineering & Management 11(3) 2016 Studies of corrosion on AA 6061 and AZ 61 friction stir welded plates 3.3 Microscopic examination Each specimen was examined under metallurgical microscope. The effects of corrosion were hard to find under it. So the samples were examined with a Scanning Electron Microscope (SEM). The images were taken at the portion where the welded region met with the parent metal and at the centre of the welded region. The Energy Dispersive X-Ray Analysis (EDAX) was also carried out for the welded and corroded region. (a) ' (b) ' '(c) " Fig. 5 SEM images of sample A with FSW joint with parameters axial load 10 kN, rotational speed 400 rpm and transverse speed 30 mm/min: (a) left side of nugget zone; (b) nugget zone; (c) right side of nugget zone Fig. 5 shows the scanning electron microscopic images of sample A. It has three parts: (a) showing the left side of the weld zone, (b) showing the right side of the weld zone and (c) showing the centre of the weld zone. The sample A shows severe attack of the alkaline solution on the surface of the welded plate. The corrosion of the metal is found to have occurred in the welded zone. The oxides of metal are formed on the surface. Pitting corrosion is found to take place in the welded zone. Fig. 6 shows the scanning electron microscopic images of sample B. It has three parts: (a) showing the left side of the weld zone, (b) showing the right side of the weld zone and (c) showing the centre of the weld zone. The alkaline solution, in which the welded plate was immersed, is found to have caused some effect on the surface. There are no severe traces of corrosion in sample B. The sample B shows considerable corrosion resistance. Fig. 7 shows the scanning electron microscopic images of sample C. It has three parts: (a) showing the left side of the weld zone, (b) showing the right side of the weld zone and (c) showing the centre of the weld zone. The welded surface is found to be least attacked by the alkaline solution in sample C. There are traces of oxides present on the surface. It is not as severe in sample A. Fig. 6 SEM images of sample B with FSW joint with parameters axial load 12 kN, rotational speed 600 rpm and transverse speed 40 mm/min: (a) left side of nugget zone; (b) nugget zone; (c) right side of nugget zone Advances in Production Engineering & Management 11(3) 2C1S 187 Raguraman, Muruganandam, Kumaraswami Dhas (a) (b) (c) Fig. 7 SEM images of sample C with FSW joint with parameters axial load 16 kN, rotational speed 1200 rpm and transverse speed 50 mm/min: (a) left side of nugget zone; (b) nugget zone; (c) right side of nugget zone 4. Result and discussion 4.1 EDAX analysis of sample A The EDAX image of sample A is shown in the Fig. 8. This shows the presence of oxides of aluminium alone. The spectrum shows that 23.56% of O and remaining Al are present. Thus, the welded zone is severely corroded. The pitting corrosion has occurred on the surface due to the effect of the alkaline solution. 12 9 4 rulSc*te 1931 cts Cursor 0 000 S « 1 S 9 to 11 12 13 UV u cpî/ev 10 M ■ _ Cps/ev 2 1 0 WO MO MO 400 500 coo Aluminium keV t>"t Cps/ev 2 1 CopperkeV Pm S i» Me IM KCr MO uc Oxvgen keV pm Fig. 8 EDAX images of sample A (FSW joint with parameters axial load 10 kN, rotational speed 400 rpm and transverse speed 30 mm/min) 4.2 EDAX analysis of sample B The EDAX image of the sample B (Fig. 9) shows the presence of 28.34 % of O, 18.75 % of C, 5.20 % of Cu, 1.29 % of Mg 1.21 % of Si, 1.12 % of Na, 0.86 % of Fe, 0.71 % of Mn, 0.50 % of Cl, 0.42 % of Ca and remaining Al by weight This shows that the percentage composition by weight of sample B shows small deviation from that before corrosion. 188 Advances in Production Engineering & Management 11(3) 2016 Studies of corrosion on AA 6061 and AZ 61 friction stir welded plates 100 ZOO 300 400 500 600 Aluminium keV |¿n> 100 200 300 400 500 500 0 100 ZOO 300 400 500 600 Copper keV fm Oxygen kev um Fig. 9 EDAX images of sample B (FSW joint with parameters axial load 12 kN, rotational speed 600 rpm and transverse speed 40 mm/min) 4.3 EDAX analysis of sample C The EDAX of sample C (Figure 10) shows the presence of 28.92 % of O, 16.52 % of C, 3.51 % of C, 0.96 % of Fe, 0.82 % of Si, 0.74 % of Mg 0.42 % of Ca and remaining Al by weight. This shows that the composition percentage by weight of the corroded region shows slight variation from parent metal composition. 'N > M 4 Fig. 10 EDAX images of sample C (FSW joint with parameters axial load 16 kN, rotational speed 1200 rpm and transverse speed 50 mm/min) Thus upon experimental analysis, followed by imaging of the specimen with Scanning Electron Microscope, to study the microstructure, and the Energy Dispersive X-ray Analysis of the specimen, to study the composition, showed that two out of three specimen were much resistant to corrosion than the third specimen. The specimen B with weld parameters 12 kN, 600 rpm and Advances in Production Engineering & Management 11(3) 2C1S 189 Raguraman, Muruganandam, Kumaraswami Dhas 40 mm/min and the specimen C with weld parameters 16 kN, 1200 rpm and 40 mm/min are suitable for application. The specimen A with weld parameters 10 kN, 400 rpm and 30 mm/min is susceptible to corrosion. So it is not suitable for application in highly corrosive environments such as seawater. 5. Conclusion The aluminium and magnesium alloys have a wide range of application such household utensils, construction equipment, packaging, vessels used in industries, pipes, aircrafts, ships, marine equipment, weapons, etc. They are mainly used for their corrosion resistance property. High strength alloys of aluminium and magnesium alloys are used in aircrafts and ships. They can be welded easily only by using friction stir welding technique. Therefore, care has to be taken that there is no probability of corrosion in the welded region. This work reveals that the so called non-corrosive alloys of aluminium and magnesium are also affected by the universal process of corrosion. But it can be reduced by using the optimum parameters of the weld. Welding can take place at any set of parameters, but a safe set of parameters to weld, which will prevent the welded zone from corrosion should be chosen. According to this investigation, it is concluded that welded region is susceptible for corrosion when the axial load and the rotational speed are kept low. As the value of these parameters increased the welding is done more and more perfectly. Out of the three sets of parameters the welded sample C shows more corrosion resistance than the other two sets of parameters. So we conclude that welding the alloy plates of AA 6061 and AZ 61 at 16 kN axial load, 1600 rpm rotational speed and 50 mm/min weld speed is most suitable. References [1] Fahimpour, V., Sadrnezhaad, S.K., Karimzadeh, F. (2012). Corrosion behavior of aluminum 6061 alloy joined by friction stir welding and gas tungsten arc welding methods, Materials & Design, Vol. 39, 329-333, doi: 10.1016/ j.matdes.2012.02.043. [2] Mathers, G. (2002). The welding of aluminium and its alloys, Woodhead Publishing Limited, Abington, Cambridge, England. [3] Corral, J., Trillo, E.A., Li, Y., Murr, L.E. (2000). Corrosion of friction-stir welded aluminum alloys 2024 and 2195, Journal of Materials Science Letters, Vol. 19, No. 23, 2117-2122, doi: 10.1023/A:1026710422951. [4] Nagasawa, T., Otsuka, M., Yokota, T., Ueki, T. (2000). Structure and mechanical properties of friction stir weld joints of magnesium alloy AZ31. In: Mathaudhu, S.N., Luo, A.A., Neelameggham, N.R., Nyberg, E.A, Sillekens, W.H. (eds.), Essential Readings in Magnesium Technology, John Wiley & Sons, 517-521, doi: 10.1002/9781118859803. ch84. [5] Thirugnanaselvi, S., Kuttirani, S., Emelda, A.R. (2014). Effect of schiff base as corrosion inhibitor on AZ31 magnesium alloy in hydrochloric acid solution, Transactions of Nonferrous Metals Society of China, Vol. 24, No. 6, 19691977, doi: 10.1016/S1003-6326(14)63278-7. [6] Song, G., Atrens, A. (2003). Understanding magnesium corrosion-A framework for improved alloy performance, Advanced Engineering Materials, Vol. 5, No. 12, 837-858, doi: 10.1002/adem.200310405. [7] Shaw, B.A. (2003). Corrosion resistance of magnesium alloys, In: Cramer, S.D., Covino, B.S. (eds.), ASM Handbook Vol. 13A: Corrosion: Fundamentals, Testing, and Protection, ASM International Handbook Committee, Materials Park Campus, Ohio, USA, 692-696. [8] Hattingh, D.G., Blignault, C., Van Niekerk, T.I., James, M.N. (2008). Characterization of the influences of FSW tool geometry on welding forces and weld tensile strength using an instrumented tool, Journal of Materials Processing Technology, Vol. 203, No. 1-3, 46-57, doi: 10.1016/j.jmatprotec.2007.10.028. [9] Ramanjaneyulu, K., Madhusudhan R.G., Venugopal R.A., Markandeya, R. (2013). Structure-property correlation of AA2014 friction stir welds: Role of tool pin profile, Journal of Materials Engineering and Performance, Vol. 22, No. 8, 2224-2240, doi: 10.1007/s11665-013-0512-4. [10] Nicholas, E.D., Thomas, W.M. (1998). A review of friction processes for aerospace applications, International Journal of Materials Product Technology, Vol. 13, No. 1-2, 45-55. [11] Threadgill, P.L., Leonard, A.J., Shercliff, H.R., Withers, P.J. (2009). Friction stir welding of aluminium alloys, International Materials Reviews, Vol. 54, No. 2, 49-93, doi: 10.1179/174328009X411136. [12] Gharavi, F., Matori, K.A., Yunus, R., Othman, N.K., Fadaeifard, F. (2015). Corrosion behavior of Al6061 alloy weldment produced by friction stir welding process, Journal of Materials Research and Technology, Vol. 4, No. 3, 314-322, doi: 10.1016/j.jmrt.2015.01.007. [13] Sharma, C., Dwivedi, D.K., Kumar, P. (2015). Influences of friction stir welding on the microstructure, mechanical and corrosion behaviour of Al-Zn-Mg aluminium alloy 7039, Engineering Review, Vol. 35, No. 3, 267-274. 190 Advances in Production Engineering & Management 11(3) 2016 Studies of corrosion on AA 6061 and AZ 61 friction stir welded plates [14] Paglia, C.S., Buchheit, R.G. (2008). A look in the corrosion of aluminium alloy friction stir welds, Scripta Materi-alia, Vol. 58, No. 5, 383-387, doi: 10.1016/j.scriptamat.2007.10.043. [15] Mishra, R.S., Ma, Z.Y. (2005). Friction stir welding and processing, Materials Science and Engineering: R: Reports, Vol. 50, No. 1-2, 1-78, doi: 10.1016/j.mser.2005.07.001. [16] Thomas, W.M., Dolby, R.E. (2002). Friction stir welding developments, In: 6th International Conference on Trends in Welding Research, Pine Mountain, Georgia, USA, from h ttps://www.researchg ate.ne t/p ublicatio n/ 273060095 Friction Stir Welding Developments, accessed November 7, 2015. [17] Gao, Z., Wang, P., Cheng, D., Niu, J., Sommitsch, C. (2015). Numerical simulation of material flow in AA6082 during friction stir spot welding, Engineering Review, Vol. 35, No. 3, 283-289. [18] Indira Rani, M., Marpu, R.N., Kumar, A.C.S. (2011). A study of process parameters of friction stir welded AA 6061 aluminum alloy in O and T6 conditions, ARPN Journal of Engineering and Applied Sciences, Vol. 6, No. 2, 61-66. [19] Raguraman, D., Muruganandam, D., Kumaraswamy, L.K. (2014). Corrosion study in friction stir welded plates of AA6061 and AA7075, International Journal of ChemTech Research, Vol. 6, No. 4, 2577-2582. Advances in Production Engineering & Management 11(3) 2C1S 191 APEM jowatal Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 192-206 http://dx.doi.Org/10.14743/apem2016.3.220 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline industry Yilmaz, O.F.a*, Cevikcan, E.a, Durmusoglu, M.B.a industrial Engineering Department, Istanbul Technical University, Ma?ka/Istanbul, Turkey A B S T R A C T A R T I C L E I N F O This study considers batch scheduling problem in the multi hybrid cell manufacturing system (MHCMS) taking into account worker resources. This problem consists of determining sequence of batches, finding the starting time of each batch, and assigning workers to the batches in accordance with some pre-determined objectives. Due to a lack of studies on the batch scheduling problem in the MHCMS, a binary integer linear goal programming mathematical model is developed for bi-objective batch scheduling problem in this study. The formulated model is difficult to solve for large sized problem instances. To solve the model, we develop an efficient heuristic method called the Hybrid Cells Batch Scheduling (HCBS) heuristic. The proposed HCBS heuristic permits integrating batch scheduling and employee (worker) timetabling. Furthermore, we construct upper and lower bounds for the average flow time and the total number of workers. For evaluation of the performance of the heuristic, computational experiments are performed on generated test instances based on real production data. Results of the experiments show that the suggested heuristic method is capable of solving large sized problem instances in a reasonable amount of CPU time. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Batch scheduling Hybrid manufacturing cells Hybrid cells batch scheduling Goal programming Heuristic HCBS heuristic *Corresponding author: ofyilmaz@itu.edu.tr omer.faruk.ylmaz89@gmail.com (Yilmaz, O.F.) Article history: Received 24 June 2016 Revised 14 August 2016 Accepted 24 August 2016 1. Introduction Cellular manufacturing (CM), described as the applications of the group technology principles in a manufacturing environment, is a production system in which the parts with similar processing requirements and machines are grouped in distinct manufacturing cells [1, 2]. The main advantages offered by CM are reduction in setup time, reduction in lead time, reduction in work-in-process inventory, enhanced visibility and quality, efficient material handling, simplified scheduling and production control, and an increase in flexibility [3, 4-7]. The problems in a CMS are classified mainly into design and operational aspects. Design problems contain formation of cells and layout planning of cells while operational problems involve assignment of workers (employees) and scheduling of parts/batches-groups into the cells [1]. Operational problems have not been considered extensively in the extant literature compared to design problems [8]. This paper considers the problem of batch scheduling in the MHCMS which is a type of CMS consisting of a number of parallel independent hybrid cells. Most of the real CMSs are composed of hybrid cells, and both automatic and manual operations are performed in these cells [9]. The importance of the worker assignment on batch scheduling problems comes to light more clearly especially in the hybrid cells. Worker involvement is not 192 Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline... bounded in these cells and the number of workers in cells plays an important role and directly affects the cell cycle times. Due to the manual operations within the hybrid cells, cell cycle times vary from high to low and the flow times of the batches depend on the number of workers assigned to work on these batches. Therefore, the decisions must be made simultaneously for the batch scheduling problem in the MHCMS are the sequence of batches on each cell, the starting time of each batch and the workers assigned to operations of batches on cells. In the current study, scheduling in multi hybrid manufacturing cells, which are arranged as flowline, is addressed. A goal-programming model has been developed for scheduling of batches within cells by considering worker resource. Two conflicting objectives are identified for the problem: minimization of the average flow time and minimization of the total number of workers in the system. Since the average flow time can be used as a performance indicator for resource utilization [10], it is determined as one of the conflicting objectives in this study. To the best of our knowledge, the batch scheduling problem for the MHCMS is examined here for the first time. Due to the computational complexities, it is fairly challenging to obtain optimal solutions to scheduling problems in real sized problem instances with exact optimization methods [11]. As such, a heuristic method, namely the HCBS heuristic, is developed for this problem. Computational results show that the heuristic method presented in the paper has the capability of solving large sized problem instances with industrial pertinence efficiently. The motivation of this study is the batch scheduling problem arising in a real life CMS. Therefore, the study has the ability of adding value to industry in the way of effectively raising engineering control for scheduling activities in CMSs. In this context, the aim of the study is to propose a new batch scheduling method for multi hybrid cell manufacturing system under resource constraints and to verify this method on an industrial application. Furthermore, this study has the originality of proposing a novel goal programming model and a heuristic method via the parallel consideration of the total number of workers and the average flow time in the MHCMS. The flow of this study is as follows: review of the relevant literature is included in Section 2. Problem definition, mathematical model and a numerical example are presented and explained in Section 3. The developed heuristic method is introduced in Section 4. The experimental data and the computational results of the proposed heuristic method are reported in Section 5. The conclusions and recommendations for future research are offered in Section 6. 2. Literature review In this study, batch scheduling problem in the MHCMS is investigated by considering worker resource. Therefore, the literature is reviewed two headlines as the worker assignment and allocation problems in the CMS and the batch scheduling problem in multi cell manufacturing system. Jensen [12] used the simulation method to examine performance advantages of labor flexibility for departmens, hybrid cells and strict cells. Askin and Huang [13] proposed a multi-objective model to improve the fitness of individual workers to tasks performed in cells, and to create effective teams. Norman et al. [14] examined the problem of allocating workers to cells to improve organizational effectiveness which is affected by productivity, quality and training cost Suer and Dagli [15] examined cell loading and labor allocation problems. They created a three-stage structure and examined solutions to sequencing, labor allocation and cell loading problems. Cesani and Steudel [16] used the simulation method to investigate the effects of varied labor allocation policies on system performance. Their results show that balance in the number of workers is a significant determinant of system performance. Suer and Tummaluri [7] studied the problem of assigning operators to operations in labor intensive cells. They proposed a three-stage approach for the solution of the problem. Fowler et al. [17] examined differences between workers, in terms of their general cognitive ability (GCA), and developed a mathematical model to minimize worker-related costs over multiple periods. Davis et al. [18] used the simulation method to examine the relationship between cross training and workload imbalance, and found that workload imbalance increased the need for worker flexibility. Fan et al. [19] examined multi-objective cell formation and operator assignment problems. Suer and Alhawari [20] examined Advances in Production Engineering & Management 11(3) 2016 193 Yilmaz, Cevikcan, Durmusoglu the use of two different operator assignment strategies (Max-Min and Max) in labor intensive manufacturing cells. Azadeh et al. [21] examined the problem of operator allocation in a CMS by combining fuzzy data envelopment analysis (FDEA) and fuzzy simulation techniques. Egilmez et al. [22] examined the problem of stochastic skill-based workforce assignment in a CMS where both operation times and demand are uncertain. Niakan et al. [23] developed a new bi-objective model of the cell formation problem to handle worker assignment and environmental and social criteria. Liu et al. [24] developed a decision model for employee assignment and production control in a CMS with considering learning and forgetting effects of employees. As the body of the literature addressing the worker assignment and allocation problems in the CMS ruled out up to the present the effect of number of workers on the flow time of batches on the cells, the present study moves in that direction. Batch scheduling problem in CMS is also addressed in this study. Little research has been conducted on batch scheduling problem in multi-cell manufacturing system in the literature. The following is a review of studies that examine the batch scheduling problem in multi-cell manufacturing system. Das and Canel [25] proposed a branch and bound solution method to seek solution to the problem of scheduling of batches in the multi-cell flexible manufacturing system (MCFMS). Celano et al. [26] used simulation method to analyze the batch scheduling problem within a manufacturing system consisting of multiple cells. Hachicha et al. [2] utilized simulation method to design a CMS consisting of multiple cells in which parts are produced in batches. Balaji and Por-selvi [27] proposed a model for batch scheduling problem in a MCFMS having sequence dependent batch setup time with flowline structure. When considering the large body of the extant literature, it is revealed that there have been studies in the literature that focus on the batch scheduling problem in CMSs having multi cells. However, there has not been any published study addressing the influence of assignment of workers on flow times of batches for the batch scheduling problem in the CMSs. This problem has been observed in a real cellular manufacturing system in the pipeline industry and it has not been addressed in the literature before. This study bridges this gap in the literature. 3. Descriptions of the problem and mathematical model 3.1 Description of the problem In the current study, the batch scheduling problem in the hybrid cells having missing operations (some parts may skip some operations on some machines) is examined. The distinctive feature of this problem is the dependence of the batch flow times on the number of workers assigned to the main operations of batches on cells. The hybrid cells need attendance of workers constantly. Because of the presence of manual operations, changing the number of workers assigned to the operations in this type of manufacturing cells causes changes in cell cycle times, which in turn changes flow times of batches on cells. An increment in the number of workers in cells results in a decreases in flow times of batches, and vice versa. For this reason, determination of number of workers, which are assigned to cells to perform operations of batches, is important in the hybrid cell scheduling studies. Therefore, when seeking solutions to the batch scheduling problem in a CMS which consists of parallel hybrid cells, it is necessary to consider the sequence of batches, the starting times of batches and the worker assignment to the batches. There are K unrelated parallel labour-intensive hybrid cells in the CMS. The hybrid cells consist of M machines, designed as flowlines, dedicated to process I batches. The assumptions which have been made in the study are as follows: • The cell compositions and the assignment of batches to cells are known in advance. • Each machine in a cell corresponds to an operation, and these operations combine to form main operation. Pre-emption of operations and main operations is not allowed. 194 Advances in Production Engineering & Management 11(3) 2016 Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline... • Parts are produced in batches, and one-piece flow is applied within the cells. The flow is uni-directional and no back-tracking is allowed. • Batches are processed from only one family in each cell and at most one batch can be processed in a cell at the same time. • The batch sizes are equal to order sizes and batch splitting is not permitted. • Batchesareavailableforprocessingattime zero and processing times include setup times. • Each worker has same multi-skills to perform all operations on cells. 3.2 Mathematical model In this section, to describe the problem more clearly, a binary integer linear goal programming mathematical model is developed to address conflicting objectives which are the total number of workers and the average flow time. The purpose of the proposed mathematical model is to contribute to the apperception of the scheduling problem addressed in the study. The indices, parameters, variables, deviational variables, decision variables and mathematical models are introduced in this section. Indices i,j - Indices of batches (i,j = 1,..., N) z - Index of workers (z = 1,., Z) m - Index of machines (m = 1,., M) k,t - Indices of cells (k,t = 1,., K) Parameters W1 - Weight of the first objective (average flow time) W2 - Weight of the second objective (total number of workers) akijk - If batch i is allocated to cell k, 1; if not, 0 avewalkingk - Average walking time of workers in cell k totalwalkingk - Total walking time of workers in cell using,k,m - If machine m is used for operation of batch i on cell k, 1; if not, 0 manualpro,k,m - Manual processing time for parts in batch i on machine m in cell k autoproi,k,m - Automatic processing time for parts in batch i on machine m in cell k pti,k,m - Processing time of parts in batch i on machine m in cell k Ipt,k - Longest processing time for parts in batch i on cell k FLTi,k - Completion time for the first part in the batch i on cell k cycmini, k - Minimum cell cycle time for batch i on cell k cycmaxi, k - Maximum cell cycle time for batch i on cell k qi - Number of parts in batch i Variables fi, k - Flow time of batch i on cell k c,k - Completion time of batch i on cell k cyci, k - Cell cycle time for batch i on cell k timej, t - Starting time of batch j on cell t workforcej, t - Total number of workers at the start of main operation of batch j on cell l mw - Maximum number of workers in the system woz,i,k,m - If worker z is assigned to machine m for main operation of batch i on cell k, 1; if not, 0 Xi,k - Number of workers assigned for batch i on cell k gi,kj,t - If main operation of batch i on cell k and the main operation of batch j on cell t overlap in time, 1; if not, 0 Deviational Variables d1-d2 - Positive deviational variables for the average flow time and the total number of workers, respectively Decision variables w1z, i, k - If worker z is assigned to main operation of batch i on cell k, 1; if not, 0 Advances in Production Engineering & Management 11(3) 2016 195 Yilmaz, Cevikcan, Durmusoglu bi,j,k - If batch i is processed after batch j on cell k, 1; if not, 0 (Note that after does not necessarily means immediately after) The mathematical formulation of the binary integer linear goal programming model is as follows: Objective Function min objective =w1x (d1/(UP1 - LB1)) +w2x (d2/(UP2 - LB2)) (1) Constraints Efc=i (maxck/maxck) — d1 = LB1 (2) mw — d2 = LB2 (3) cilk >(clk +filk)-Mx(l-biijik) Vi,j, k (4) Ci,k ^fi,k k (5) maxck >cik Vi, k (6) bijik + bj,i,k = akik xak]k Vi,j, k i^j (7) timeJt = cJt -fJt Vj, t (8) (ci,fc ~fi,k)~ timej,t workloadjt Vj,t (25) bij,k 0 or 1 ; xi k integer ; d1 > 0 ; d2 > 0 (26) The objective function (Eq. 1) involves two terms, one for each of the conflicting objectives, and presents a weighted average of deviations from developed lower bounds. The first term minimizes the deviation of the average flow time from LB1 (Eq. 32). The second term attempts to minimize the deviation of the total number of workers from LB2 (Eq. 34), and is in conflict with the first term. In Eq. 1, a zero-one normalisation scheme is used to scale all unwanted positive deviations (d1 and d2) onto a zero-one range [28]. Eq. 2 and Eq. 3 are soft constraints which 196 Advances in Production Engineering & Management 11(3) 2016 Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline... represent positive deviations from target levels (LB1 and LB2) for the average flow time and the total number of workers objectives, respectively. Eq. 4 ensures that each cell can process at most one batch at the same time. Eq. 5 implies that the completion time of a batch is greater than or equal to the flow time of this batch. Eq. 6 implies that the total completion time in a cell (Ck) is greater than or equal to the largest completion times of batches in this cell. Eq. 7 ensures that if batches i and j are allocated to the cell k, then bjk or b,ijk gets value equal to one. Eq. 8 represents the time points (timej, t) at which the total number of workers can change, that is to say, the starting times of the main operations. Eqs. 9-14 are used to determine main operations which coincide with the time points (timej, t) . Eq. 15 is used to calculate the flow times of the batches. Eq. 16 and Eq. 17 are used to determine the cell cycle time for each batch. Eq. 18 is used to assign workers to machines where the operations are performed. Eq. 19 ensures that worker assigned to machine for operation is also assigned to the cell that contains the machine in question. Eq. 20 prevents the assignment of the same worker to two main operations which overlap in time. Eq. 21 is used to calculate the number of workers assigned for operation of batch i on cell k. Eqs. 2224 are used to calculate the total number of workers at each time point (workforcej, t). Eq. 25 indicates that the total number of workers in the system is greater than or equal to the total number of workers at each time points. Eq. 26 is used for the binary, integer and sign bounds on the variables. The constant M in the equations should be sufficiently large. Since the mathematical model is developed considering the parallel cells in this study, the related studies involve parallel machine/cell scheduling problems and mathematical models constructed by Yang et al. [11] and Dalfard et al. [29] can be examined by interested readers to obtain comprehensive perspective. It is also important to emphasize that although Eqs. 17-19 are proposed to calculate the cell cycle times in case of dedicated assignment of workers to the hybrid cells. Eq. 27 is developed to obtain the cell cycle times. Since each worker has the same multi-skills in the MHCMS, theoretical values of the cell cycle times, which are the best values that can be reached, are calculated by using Eq. 27 and it is used in the solution of the model instead of Eqs. 17-19. cyci,k = {cycmaxiik/xiik) Vi,k (27) The parameters cycmax,k and cycmin,k are calculated using Eq. 28 and Eq. 29, respectively. cycmaxiik = YJm=iiiotalwalkingk+manualproiikm) Vi,k (28) cycminik = lptiik Vi, k (29) The longest processing time for parts on a cell is equal to maximum of processing times of machines in this cell. This fact is stated in Eq. 30. The processing time for parts on each machine is equal to sum of manual processing time and automatic processing time. This fact is stated in Eq. 31. Iphk =maxVrn(p£i,fc,rn) (30) pti,k,m = manualproikm + autoproikm Vi,k,m (31) In this paper, we put forward lower and upper bounds for the average flow time and the total number of workers. The derivation of lower bound (LB1) and upper bound (UP1) for average flow time are expressed as follows: LB1 = (Z^=1ZjL1(cycmini,fc x -1) +FLTLk)/K) (32) UP1 = (ELiZiLi(<-ycmaxi,k X fa -D+FLTiik)/K) (33) The derivation of lower bound (LB2) and upper bound (UP2) for the total number of workers are expressed as follows: LB2 =El=1{minvi{cycmaxiik/cycmaxik)) (34) UP2 = ££=1 (maxvi(cycmaxiik/cycminik)+^ (35) Advances in Production Engineering & Management 11(3) 2016 197 Yilmaz, Cevikcan, Durmusoglu 3.3 Numerical illustration To explain the problem considered in this paper, we present a MHCMS with missing operations (MO). In this system, batches between 1 and 6 are to be scheduled in the first cell and batches between 7 and 12 are to be scheduled in the second cell. The maximum and minimum cell cycle times for each batch, the batch sizes, the automatic and the manual processing times of parts of batches on machines and the total walking time in cells are presented in Table 1. As seen in this table, the operations on machine 3 are missing operations for batch 1 to batch 6 and the operations on machine 2 are missing operations for the batch 7 to batch 12. There is a worker pool which is consists of five different workers. The mathematical model was coded in the GAMS CPLEX software package for equal weights of objectives (w1 = 0.5; w2 = 0.5). The optimal solution was derived in 676 min. of computational time. The results of the CPLEX software are reported below. Fig. 1 illustrates optimum solution of this problem. In Fig. 1, the best sequence of batches on Cell 1 and Cell 2 is obtained as 1-4-3-5-2-6 and 9-8-7-10-11-12, respectively. The starting and completion times of each batch are shown in Fig. 1. The average flow time is equal to 1800, the total number of workers is equal to 3 and the objective function is equal to 0.26. Table 1 Data for a 12-batch 2-cell batch scheduling example Cell Cycle Times Batch Machine! Machine2 Machine3 Machine4 Total Walking Max Min Size Aut. Man. Aut. Man. Aut. Man. Aut. Man. Batch 1 60 30 5 5 15 0 10 MO MO 10 20 15 Batch 2 80 30 10 0 20 0 15 MO MO 0 30 15 1 Batch 3 55 30 5 10 10 0 15 MO MO 15 15 15 20000 gl 15000 < 10000 5000 " so -H il M ï C* C1 ff1 NN« S S S a .ÈP S C .a S -SP î = ■= S o = e SP J J ,ÏP 7,3 7,2 7,1 7,0 6,9 6,8 6,7 6,6 6,5 v* - '' O <=>- fC f- NBEC BS MPT TWEC ï C -O S J s .SP I J '■s s u S NBEC s .SP o 5 -SP o = .SP = = = S BS S MPT E TWEC (a) (b) Fig. 4 Results for average flow time (a), and total number of workers (b) The average flow time and the total number of workers for different levels of factors are provided in Fig. 4. According to this figure, the average flow time is crucially increasing with the rising level of the NBEC, BSEC and MPT. The observed trend of TWEC can be regarded as consistent with Table 4. The average flow time reaches its maximum value when the level of BSEC is high and its minimum value when the level of BSEC is low. Also, the total number of workers reaches its maximum value when the level of NBEC is high and its minimum value when the level of NBEC is low. As far as the total number of workers is concerned, slight differences among different levels have been observed for each factor. The results also show a non-monotonic trend of BSEC, MPT and TWEC in respect of the total number of workers. The reason for the small changes in total number of workers along with different levels of factors is that the sensitivity of the total number of workers to different level of factors is quite low compared to sensitivity of average flow time. As mentioned before, a new mathematical formulation can be developed and used in the method to increase the sensitivity level of the total number of workers to the factor levels. Table 5 Multiple comparisons among factor levels Factor Dep. var. I J Mean difference (I-J) Sig. (P) Factor Dep. var. I J Mean difference (I-J) Sig. (P) NBEC AFT Low Medium High -4413.296* -18311.111* 0.000 0.000 MPT AFT Low Medium High -8943.148* -17272.704* 0.000 0.000 Medium High -13897.815* 0.000 Medium High -8329.556* 0.000 TNW Low Medium High -0.074 -0.333* 0.415 0.000 TNW Low Medium High 0.007 -0.104 0.935 0.255 Medium High -0.259* 0.005 Medium High -0.111 0.223 BSEC AFT Low Medium High -6479.741* -19770.778* 0.000 0.000 TWEC AFT Low Medium High 215.481 -83.778 0.839 0.937 Medium High -13291.037* 0.000 Medium High -299.259 0.778 TNW Low Medium High -0.015 0.074 0.870 0.415 TNW Low Medium High -0.111 -0.074 0.223 0.415 Medium High 0.089 0.329 Medium High 0.037 0.683 (*)The mean difference is significant at the 0.05 level. 204 Advances in Production Engineering & Management 11(3) 2016 Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline... As seen in Table 5, each of the pairwise differences among the different levels of NBEC, BSEC and MPT is found to be statistically significant (p < 0.05) for the average flow time (AFT). However, the reverse results have been observed for TWEC. The results show that the factor levels of NBEC, BSEC and MPT have an important effect on the performance of the proposed method for the HCBS when the performance is evaluated in terms of the average flow time. Table 5 also indicates that the slight differences between different factor levels (except NBEC) are not found to be statistically significant for the total number of workers (TNW). Regarding TNW, it is concluded that the factor levels of NBEC, MPT and TWEC have not an important effect on the performance of the proposed method for the HCBS when the performance is evaluated in terms of the total number of workers. 6. Conclusion This paper addresses the batch scheduling problem in the MHCMS by considering worker resource and flow times simultaneously-something that is largely overlooked in the literature of batch scheduling in CMS. A goal programming mathematical model is proposed, in which the first objective is minimization of the average flow time and the second is minimization of the total number of workers. Due to the complexity of the problem, we developed a heuristic method, namely the HCBS heuristic. To validate the suitability and applicability of the heuristic, it is implemented in a real life expansion joint production system in a pipeline industry. Hence, this research is thought to assist to the engineering managers with important insights to enhance control level for batch scheduling in CMS. By the end of this research, the findings dealing with capacity requirements show that the proposed HCBS heuristic creates different level of freed-up workforce capacity for different combinations of objective function coefficients. It should also be noted that critical success factors, which are accuracy and topicality of production data, lean applications in manufacturing environment and work study, were made critical contribution to the findings of research. For this reason, attention should be paid to these critical success factors in other studies. For the extension of the present research, other worker related issues can be considered, such as different worker skills for assignment and worker skill levels to perform operations. Moreover, in order to represent the real manufacturing systems more realistic, uncertain parameters and stochastic approaches can be added in future research. As another future research direction, the results obtained by the proposed method can be compared with some non-deterministic methods, such as particle swarm optimization (PSO), genetic algorithm (GA), and other evolutionary algorithms. References [1] Venkataramanaiah, S. (2008). Scheduling in cellular manufacturing systems: an heuristic approach, International Journal of Production Research, Vol. 46, No. 2, 429-449, doi: 10.1080/00207540601138577. [2] Hachicha, W., Masmoudi, F., Haddar, M., (2007). An improvement of a cellular manufacturing system design using simulation analysis, International Journal of Simulation Modelling, Vol. 6, No. 4, 193-205, doi: 10.2507/IISIMM06(4)1.089. [3] Wang, J.X. (2015). Cellular manufacturing: Mitigating risk and uncertainty, CRC Press, Boca Raton, USA, doi: 10.1201/b18009-1. [4] Wemmerlov, U., Hyer, N.L. (1989). Cellular manufacturing in the US industry: A survey of users, The international journal of production research, Vol. 27, No. 9, 1511-1530, doi: 10.1080/00207548908942637. [5] Olorunniwo, F., Udo, G. (2002). The impact of management and employees on cellular manufacturing implementation, International Journal of Production Economics, Vol. 76, No. 1, 27-38, doi: 10.1016/S0925-5273(01)00155-4. [6] Solimanpur, M., Vrat, P., Shankar, R. (2004). A heuristic to minimize makespan of cell scheduling problem, International journal of production economics, Vol. 88, No. 3, 231-241, doi: 10.1016/S0925-5273(03)00196-8. [7] Süer, G.A., Tummaluri, R.R. (2008). Multi-period operator assignment considering skills, learning and forgetting in labour-intensive cells, International Journal of Production Research, Vol. 46, No. 2, 469-493, doi: 10.1080/00207540601138551. [8] Tavakkoli-Moghaddam, R., Javadian, N., Khorrami, A., Gholipour-Kanani, Y. (2010). Design of a scatter search method for a novel multi-criteria group scheduling problem in a cellular manufacturing system, Expert Systems with Applications, Vol. 37, No. 3, 2661-2669, doi: 10.1016/j.eswa.2009.08.012. Advances in Production Engineering & Management 11(3) 2016 205 Yilmaz, Cevikcan, Durmusoglu [9] Hyer, H., Wemmerlov, U. (2002). Reorganizing the factory competing through cellular manufacturing, CRC Press, Boca Raton, USA. [10] Rathinam, B., Govindan, K., Neelakandan, B., Raghavan, S.S. (2015). Rule based heuristic approach for minimizing total flow time in permutation flow shop scheduling, Tehnicki vjesnik - Tehnical Gazette, Vol. 22, No. 1, 25-32, doi: 10.17559/TV-20130704132725. [11] Yang, Z., Qiu, H.-L., Luo, X.-W., Shen, D. (2015). Simulating schedule optimization problem in steel making continuous casting process, International Journal of Simulation Modelling, Vol. 14, No. 4, 710-718, doi: 10.2507/ IISIMM14(4)CO17. [12] Jensen, J.B. (2000). The impact of resource flexibility and staffing decisions on cellular and departmental shop performance, European Journal of Operational Research, Vol. 127, No. 2, 279-296, doi: 10.1016/S0377-2217(99)00491-9. [13] Askin, R.G., Huang, Y. (2001). Forming effective worker teams for cellular manufacturing, International Journal of Production Research, Vol. 39, No. 11, 2431-2451, doi: 10.1080/00207540110040466. [14] Norman, B.A., Tharmmaphornphilas, W., Needy, K.L., Bidanda, B., Warner, R.C. (2002). Worker assignment in cellular manufacturing considering technical and human skills, International Journal of Production Research, Vol. 40, No. 6, 1479-1492, doi: 10.1080/00207540110118082. [15] Suer, G.A., Dagli, C. (2005). Intra-cell manpower transfers and cell loading in labor-intensive manufacturing cells, Computers & Industrial Engineering, Vol. 48, No. 3, 643-655, doi: 10.1016/j.cie.2003.03.006. [16] Cesani, V.I., Steudel, H.J. (2005). A study of labor assignment flexibility in cellular manufacturing systems, Computers & Industrial Engineering, Vol. 48, No. 3, 571-591, doi: 10.1016/j.cie.2003.04.001. [17] Fowler, J.W., Wirojanagud, P., Gel, E.S. (2008). Heuristics for workforce planning with worker differences, European Journal of Operational Research, Vol. 190, No. 3, 724-740, doi: 10.1016/j.ejor.2007.06.038. [18] Davis, D.J., Kher, H.V., Wagner, B.J. (2009). Influence of workload imbalances on the need for worker flexibility, Computers & Industrial Engineering, Vol. 57, No. 1, 319-329, doi: 10.1016/j.cie.2008.11.029. [19] Fan, J., Cao, M., Feng, D. (2010). Multi-objective dual resource-constrained model for cell formation problem, In: Management of Innovation and Technology (ICMIT), 2010 IEEE International Conference on Management of Innovation and Technology, doi: 10.1109/ icmit.2010.5492881. [20] Suer, G.A., Alhawari, O. (2012). Operator assignment decisions in a highly dynamic cellular environment, In: Modrak, V., Pandian, R.S. (ed.), Operations management research and cellular manufacturing systems: Innovative methods and approaches, IGI Global, Hershey, PA, USA, 258-276, doi: 10.4018/978-1-61350-047-7.ch012. [21] Azadeh, A., Sheikhalishahi, M., Koushan, M. (2013). An integrated fuzzy DEA-Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS, Applied Mathematical Modelling, Vol. 37, No. 24, 9922-9933, doi: 10.1016/j.apm.2013.05.039. [22] Egilmez, G., Erenay, B., Suer, G.A. (2014). Stochastic skill-based manpower allocation in a cellular manufacturing system, Journal of Manufacturing Systems, Vol. 33, No. 4, 578-588, doi: 10.1016/j.jmsy.2014.05.005. [23] Niakan, F., Baboli, A., Moyaux, T., Botta-Genoulaz, V. (2016). A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria, Applied Mathematical Modelling, Vol. 40, No. 4, 2674-2691, doi: 10.1016/j.apm.2015.09.047. [24] Liu, C., Wang, J., Leung, J.-Y.T. (2016). Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm, Computers & Industrial Engineering, Vol. 96, 162179, doi: 10.1016/j.cie.2016.03.020. [25] Das, S.R., Canel, C. (2005). An algorithm for scheduling batches of parts in a multi-cell flexible manufacturing system, International Journal of Production Economics, Vol. 97, No. 3, 247-262, doi: 10.1016/j.ijpe.2004.07.006. [26] Celano, G., Costa, A., Fichera, S. (2008). Scheduling of unrelated parallel manufacturing cells with limited human resources, International Journal of Production Research, Vol. 46, No. 2, 405-427, doi: 10.1080/002075406011 38452. [27] Balaji, A.N., Porselvi, S. (2014). Artificial immune system algorithm and simulated annealing algorithm for scheduling batches of parts based on job availability model in a multi-cell flexible manufacturing system, Procedia Engineering, Vol. 97, 1524-1533, doi: 10.1016/j.proeng.2014.12.436. [28] Jones, D., Tamiz, M. (2010). Practical goal programming, Springer, New York, USA, doi: 10.1007/978-1-44195771-9. [29] Dalfard, V.M., Ardakani, A., Banihashemi, T.N. (2011). Hybrid genetic algorithm for assembly flow-shop scheduling problem with sequence-dependent setup and transportation times, Tehnicki vjesnik - Tehnical Gazette, Vol. 18, Vol. 4, 497-504. [30] Nicholas, J., Soni, A. (2005). The portal to lean production: Principles and practices for doing more with less, CRC Press, Boca Raton, USA. [31] Schaller, J. (2000). A comparison of heuristics for family and job scheduling in a flow-line manufacturing cell, International Journal of Production Research, Vol. 38, No. 22, 287-308, doi: 10.1080/002075400189419. 206 Advances in Production Engineering & Management 11(3) 2016 APEM jowatal Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 207-215 http://dx.doi.Org/10.14743/apem2016.3.221 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper Consideration of a buyback contract model that features game-leading marketing strategies He, H.a b *, Jian, M.a, Fang, X.c aSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China bTian Fu College of Southwestern University of Finance and Economics, Chengdu, China cSchool of Business Planning, Chongqing Technology and Business University, Chongqing, China A B S T R A C T A R T I C L E I N F O Enterprises will sacrifice profits for market shares. For this reason, the make-to-stock upstream expects the downstream to order more. The paper argues the game leader sales-oriented upstream, motivating downstream make no shortage, and attempts to execute a buyback contract to reach realistic decisions. In this article, we research a supplier that is a sales-oriented leader and a retailer that is a profit-oriented follower. The retailer is required to order more than its optimal quantity. The primary analysis emphasizes either enhancing the buyback price or reducing the wholesale price. In the results, the buyback contract parameters are limited by both the sales-oriented supplier's retained earnings and the distribution of market demand. Numerical examples are given to illustrate contract parameters that affect the supply chain coordination, the order quantity of the retailer and the profit of the supply chain. The previous buyback contract literature assumes not only that the supplier and retailer are profit oriented but also that they achieve both supply-chain coordination and Pareto optimality. However, the paper discusses the parameters of the buyback contract when the supplier is sales oriented. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Buyback contract Marketing strategy Supply chain coordination *Corresponding author: 9073637@qq.com (He, H.) Article history: Received 12 June 2016 Revised 8 July 2016 Accepted 18 July 2016 1. Introduction The making new products in many ways have done jobs. Exactly, the motor vehicle manufacturers have released new products whose functionality and service are higher than ever before due to the trend of motor vehicles purchase quota policy in China. Even the oligopolies have not hesitated to sacrifice profits to increase sales and grab market share. Hence, the marketing strategy would pull the production manufacture. Chen et al. [1] research has shown that a Website info-mediary provides retailers with a demand-referral service and customers with incentive rebates. Studies have also examined rebate sensitivity and market share in the context of which policies are optimal to achieve an integrated supply chain. The importance of market share is widely recognized. Pasternack [2] study a buyback contract is one in which the supplier charges a retailer the wholesale price before the selling season and then buys back any unsold products at a buyback price at the end of selling season. Essentially, a buyback contract motivates retailers to order more. Cachon [3] points to a comparative study of classic supply-chain contracts shows that under certain circumstances, a buyback contract is equal to a revenue-sharing contact He et al. [4], this paper investigates the revenue-sharing contract in supply chains with a sales-oriented supplier, examining both supply-chain collaboration solutions and the Pareto improvement between the supplier and the retailer when the quantity of a retailer's order falls 207 He, Jian, Fang within a certain range. In addition, it asks whether the classic buyback contract is a solution when a supplier is a game leader that is sales oriented. The research presents the buyback contract as a return policy pursuant to which the supplier buys back any unsold products at the end of the contract period. In this scenario, the retailer orders an optimal quantity. Lee et al. [5] research the buyback price (or the price subsidy) is used to solve technical problems that lead to a decrease in a product's market price pursuant to reach on price protection in the information technology (IT) industry that discusses these problems. Yan and Huang [6], those researchers discuss the return problem in the electronics market. The solution is for the retailer to sell the unsold products online and then to deduce the optimal order quantities using both the traditional market and the electronic market. Ding and Chen [7] focus on situations in which an appropriate return policy coordinates a three-echelon supply chain, whose members will fully distribute its profit. Cai et al. [8] investigate a specific buyback contract in which the supplier subsidizes the retailer's inventory and the retailer's order quantities exceed the supplier's objective. Traditionally, the supply-chain contracting literature has focused on aligning economically rational players' incentives. Additionally, the buyback contract research assumes that both suppliers and retailers are not only profit oriented but also achieve both supply-chain coordination and Pareto optimality. In reality, Loch and Wu [9], a portion of the research is distinct from economic incentives, providing experimental evidence that human behaviour affects economic decision making in supply-chain performance. More importantly, supply-chain parties deviate from the predictions of self-interested profit-maximization models. One study, Ho et al. [10] consider how fairness influenced economic outcomes in a supply chain and designed the supply chain contract In another, Lin and Hou [11], empirical theoretical feedback uncovers the cause of failed buyback contract coordination by analyzing the correlation between wholesale price and buyback price. More recently, Zhang et al. [12] research has considered the loss-averse supplier and how to establish a critical ratio between buyback contracts and share revenue contracts. Another study, Sluis and Giovanni [13] provide an empirical contribution on the subject of coordination with contracts, which has turned out to be primarily based on game theory. In this paper, the supplier has greater motivation to incentivize the retailer to order more than his optimal quantity. The results show the buyback contract parameters how to adjust A notational system is presented in section 2; the buyback contract with profit-oriented suppliers, realized supply-chain coordination (Chen et al.) [14] and Pareto optimality (Ding et al.) [15] are discussed in section 3; sales-oriented suppliers' buyback-contract strategies are discussed in section 4; numerical examples illustrate the two types of strategies in section 5; and a summary and future research are presented in section 6. 2. Notational systems This paper assumes the supplier is the leader and the retailer is the follower in a two-echelon supply chain playing the Stackelberg game. The market demand is stochastic x, the density function f(x) and cumulative distribution function F(x), F(x) is a monotone continuous increasing function, and has first derivative F(0) = 0. The list of variables below describes this article's notations. And P(q) = xf(x)dx + J™ qf(x)dx , I(q) = — x)f(x)dx , L{q) = — q)f(x)dx. qt (q2) - The order quantity of the profit-oriented (sales-oriented) retailer w1 (w2) - The wholesale per unit of the profit-oriented (sales-oriented) supplier ¿1 (¿2) - The buyback price per unit of the profit-oriented (sales-oriented) supplier p - The market price per unit of product c - The supplier's marginal cost per unit g - The retailer's shortage cost per unit v - The retailer's salvage value of unsold product P(q) - The sales quantity 208 Advances in Production Engineering & Management 11(3) 2016 Consideration of a buyback contract model that features game-leading marketing strategies I(q) - The unsold quantity L(q) - The shortage quantity Pr() - Probability function 3. Profit-oriented supplier strategies Because the supplier is profit oriented and strives for maximum profit, the supplier provides the set of buyback contract parameters (w1,b1) to the retailer. Here (w1,b1) are the wholesale and buyback price per unit of the profit-oriented supplier. The retailer's order quantity is according to the contract parameters above. Then, the expected profits of supplier and retailer are Ensl(q; w1,b1) and Enrl(q; w1,b1): Ensl(q; w1,b1) = (wt -c)q- b^^q) (!) Enrl(q;w1,b1) = pP(q) + (bt + v)I(q) -gL(q) -wtq (2) P(q) are the expected sales; /(q) are the expected total unsold products; and L(q) are the expected shortages. According to the formula < 0, the retailer's profit increases when the wholesale price decreases. When the wholesale price approaches the product cost, the expected profit of retailer, is amended by the other formula, En(q): £rc(q) =pP(q) + vl(q) -gL(q) -cq (3) Eq. 3 is the optimal profit of the centralized supply chain. Plug these equations into Eq. 1 and Eq. 2, derivative with q and get the optimal order quantity of retailer q1 and centralized supply chain q* is satisfied with equations F(q1) = p^f^™^ and E(q) = respectively. The con- tract parameters are discussed in the context of F(q1)and F(q). If b1 = b(w1), which is the buy-back price, is a function of the wholesale price, then qi = q*. Plug ¿(wi) = ^ into Ensl(q;w1,b1) and Enrl(q;w1,b1), simplify them, just get: Enrl(q; w1,b1) = y(En(q*) + g/u) — gn; Ensi(q; wi,bi) = (1 - 7)(£,?7:(q*) + giS). Let the parameter y be y = Wl. Given wt > c, theny e (0,1), thus the supply chain would be coordinated by the buyback contract with the profit-oriented supplier. 4. Sales-oriented supplier strategies Because the supplier who strives for maximum sales quantity is sales oriented, it provides the set of buyback contract parameters(w2,¿2) to the retailer. Here (w2,b2) are the sales-oriented supplier's wholesale and buyback prices per unit. The retailer's order quantity is according to the contract parameters set forth above. Then, the expected profits of supplier and retailer are Ens2(q;w2,b2) and Enr2(q; w2,b2): Ens2(q; w2,b2) = (w2-c)q- b2I(q) (4) Enri(q;w2,b2) = pP(q) + (b2 + v)I(q) -gL(q) -w2q (5) From the first-order optimal condition of Eq. 5, the optimal order quantity of retailer q2 is satisfied with: p + g-w2 Hq*) = P + g-(b2 + v) (6) 4.1 Maintain the wholesale price and increase the buyback price In chapter 3, the retailer's optimal order quantity is the centralized supply chain's optimal product when the buyback parameters are ¿(wi) = ^ The centralized supply chain's Advances in Production Engineering & Management 11(3) 2016 209 He, Jian, Fang optimal product means that reach supply chain's Pareto optimality. However, the sales-oriented supplier expects maximum sales quantity and minimum (or even no) shortage. Then, the sales-oriented supplier proposes a new incentive contract and requires the retailer's order quantity q2E(q*,q). Moreover, q makes L(q) = 0. Compare F(q1) and F(q2) when the sales-oriented supplier would regulate the contract parameters to realize q2 G(q*, q): one is the buyback price increasing, the other is the wholesale price decreasing. When the buyback price increases, the contract parameters (w2,b2) are satisfied with the following conditions: (w2 =w1,b2 >b(w1)) to build the model P. d r-^ y + g-wi , P:maxF ±(----) (7) b2 p + g — (b2 + v) (7) f Ens2(q*;w2,b2) >En^ (8) s. t. ] Enr2 (q; w2,b2) >Enrl (q*;w1,b1) (9) { qe(q*,q) (10) Following is a further discussion of this model. The sales-oriented supplier has a higher amount of current revenue when the wholesale price is increased. The inventory cost would also be transferred because the retailer is expected to order products in excess of his optimal order quantity. The next problem is whether the retailer is motivated to pay more. 4.2 Decrease the wholesale price and maintain the buyback price The supplier's strategy, which remains unchanged with regard to the wholesale price and establishes a higher buyback price, must be confronted with the retailer's capital constraint before the selling season. If the retailer has no financing, the contract will not motivate it to participate. Following is a discussion of another supplier's strategy in that case. Model When the wholesale price decreases, the contract parameters (w2,b2) are satisfied with the conditions: (w2 EW;^ (10) s.t.Enri(q*;wi,b(wi)) (11) I qe(q*,q] (12) In the two models above, Eq. 7 and Eq .9 are the objectives of the supplier's decision-making in which the incentive mechanism is acted on by the contract parameters b2 or w2 to guarantee the retailer's maximum order quantity. Eq. 6 and Eq. 10 represent the supplier's reserved earnings. Eq. 7 and Eq. 11 represent the retailer's participation constraints. Eq. 8 and Eq. 12 are decision variables and their domain of definitions; q makes L(q) = 0 For property 1, the buyback contract parameters are limited by the supplier's reserved earnings and the distribution of market demand when the supplier is sales oriented. When the wholesale price is decreased, the retailer is motivated to order more products within the capital constraint. Indeed, the supplier's objective, which is to encourage the retailer to order more, results in an expectation of greater market share. Nevertheless, this approach does not necessarily result in higher sales. 4.3 Sales efforts If more market demand is not created, the supplier would not believe that more orders lead to more sales. In this situation, sales effort would directly change market demand, thus affecting the retailer's order quantity. Tirole [16] shows that sales are not only influenced by market price but also (eventually) related to sales effort. This section will discuss what happens when the retailer's sales efforts satisfy the sales-oriented supplier's objective. 210 Advances in Production Engineering & Management 11(3) 2016 Consideration of a buyback contract model that features game-leading marketing strategies Model The variable e is sales effort, De is stochastic market demand and increasing function. dG(x e) DeG(x, e) = Pr(D(e) < x) is distribution function and —< 0 is a monotonic continuous increasing function. Both are changed with sales effort. g(e) is the cost of the retailer's sales effort and ^(0) = 0 is a monotonic continuous increasing function with the first derivative. P(q,e) is expected sales within sales effort, P(q, e) = E min(x, D(e)) . l(q, e) = E(q — D(e)+) D" r-1 i P+3-Wl \ (13) P:maxeF 11-1 ^ J e Kp + g-fa + v) Ens2(q*;w2,b(w1)) >En^ (14) s.t.\Enr2(q;w2,b(w1)) >Enrl(q*;w1,b(w1)) (15) qe(q*,q) (16) Sales effort influenced order quantity and expected sales increased. However, sales effort did not solve the retailer's capital constraint. 5 Numerical examples 5.1 Set parameters A supplier and a retailer align in a two-echelon supply chain with a buyback contract. The supplier has two potential strategies: the sales-oriented strategy and the profit-oriented strategy. Both of the strategies in the above discussion have the same parameters: the market price, p = 10, the product cost, c = 4 , the salvage value of unit, g = 2, the shortage cost of unit, v = 1, market demand X is subject to normal distribution, the mean is p. = 100, and the standard deviation is a = 20. 5.2 Optimal profit-oriented decisions The expected shortage: L(QC) = 3.6089. A wholesale price and a buyback price form a set of buyback contract parameters. Table 1 shows that the retailer's optimal order quantities are q* = 11.18246 and the supply-chain revenues are nc = 52.4831 with changes in the wholesale price and buyback price. The initial wholesale price is 4 and increases one unit every time until 10; the buyback price, retailer's profit and supplier's profit correspond. Figure 1 shows that when the supplier is profit oriented, the wholesale price is increased, leading to an increase in the supplier's profits and a decrease in the retailer's profits. However, the supply-chain revenue remains unchanged. The buyback price is higher if the wholesale price is increased. The following discusses the three numerical strategies when the supplier is sales oriented. Table 1 Optimal profit-oriented decisions w b nr ns W b nr ns 5 1.3750 43.4227 9.0604 8 5.5000 16.2415 36.2415 6 2.7500 34.3623 18.1208 9 6.8750 7.1812 45.3019 7 4.1250 25.3019 27.1812 q*c 11.1824 nc 52.4831 Advances in Production Engineering & Management 11(3) 2016 211 He, Jian, Fang Fig. 1. The wholesale price effect on supply chain performance and buyback price 5.3 Strategy 1: Maintain the wholesale price and increase the buyback price Here, the expected shortage in the profit-oriented scenario is still used. The five group parameters are set in Table 1: subscript 1 represents the original in the profit-oriented scenario; subscript 2 is the parameter after the buyback price was raised. b2 is the independent variable in each group and the dependent variables are nr2,ns2,n2. Table 1 is used to make a comparison. Table 2 shows all numerical information up to the incentive mechanism, when the retailer orders more to satisfy the supplier's objective. However, the supplier's lost profits are greater than those of the supply chain. In general, to reach the same order quantity q2 than optimal quantity in the profit-oriented scenario, the supplier's losses are not equal to the supply chain's losses or the retailer's increments compared to several groups' arguments in strategy 1. The supply chain's revenue is almost unchanged front and back, as Fig. 2 indicates. The supplier's loss is less than the retailer's increments; additionally, whenever the losses or increments decrease, the wholesale price increases. In Fig. 2, the solid line and the dotted line represent the supply chain's performance in the profit-oriented and the sales-oriented scenarios, respectively. It is obvious that the retailer's profit is increasing and the supplier's profits are decreasing, which is the basis of the profit-oriented scenario. However, the increment or the decrement is gradual and the profit lines almost overlap with the increasing wholesale price. Table 2 Sales-oriented strategy 1 w (:nrl,nr2) (nsl,ns2) (k1,K2) 5 1.3750^2.0234 43.4227^44.5046 9.0604^7.8694 6 2.7500^3.3058 34.3623^35.2896 18.1208^17.0844 4.1250^4.5882 111.8236^ 25.3019^26.0747 52.4831^ 7 27.1812^26.2993 115.4325 52.3740 8 5.5000^5.8705 16.2415^16.8597 36.2415^35.5143 9 6.8750^7.1529 7.1812^7.6448 45.3019^ 44.7292 212 Advances in Production Engineering & Management 11(3) 2016 Consideration of a buyback contract model that features game-leading marketing strategies Fig. 2 Supply-chain performance after increasing the buyback price versus making the optimal decision 5.4 Strategy 2: Increase the wholesale price and maintain the buyback price The parameters are set in accordance with Table 2. The difference is thatw2 is the independent variable in each group. Table 3 shows the numerical incentive mechanism. Comparing strategy 1 with strategy 2 reveals some differences: each group's parameters show that it is obvious that the retailer makes more profit in strategy 2 than in strategy 1. Additionally, the supplier's decrement is more than the retailer's increments; even the supply chain's revenue remains unchanged in the sales-oriented scenario. In Fig. 3, the solid line and the dotted line are used as in Fig. 2. It is obvious that the area between the solid line and the dotted line is larger than in Fig. 2. Table 3 Sales-oriented strategy 2 b (wx,w2) (nrl,nr2) (nsl,ns2) 1.3750 5^4.4944 43.4227^49.1641 9.0604^3.2099 2.7500 6^5.5666 34.3623^39.2835 18.1208^13.0905 4.1250 7^6.6388 111.8236^115.4325 25.3019^29.4029 27.1812^22.9711 52.4831^52.3740 5.5000 8^7.7111 16.2415^19.5223 36.2415^32.8517 6.8750 9^8.7833 7.1812^9.6417 45.3019^42.7323 Fig. 3. Supply-chain performance after decreasing the wholesale price versus making the optimal decision Advances in Production Engineering & Management 11(3) 2016 213 He, Jian, Fang 5.5 Strategy 3: Sales effort This section discusses the retailer's sales effort to order more products. Here, assuming g(e) = ±ke2 (Xu et al., 2004) [17], D(e,x) = ex. (Xu etal 2004)[17]. k is the ratio of sales effort cost and the independent variable; the other parameters are dependent variables. The first k and e are set as a benchmark, k = 20, e = 2. k is increased by 10 every time. The retailer would decide qe and e using the maximum profits. The calculated results are in Table 4. In accordance with Table 4, Fig. 4 describes the relationship between the independent and dependent variables: • k and e are negatively correlated except for two inflection points, k = 26, k = 52. e experiences any change before k = 26 and after k = 52; • k and q are negatively correlated except for two inflection points, k = 26, k = 52. q experiences any change before k = 26 and after k = 52; • k and the retailer's profit are negatively correlated. Table 4 Sales-oriented strategy 3 k e q 20 2.0000 22.4248 65.2061 30 1.7600 19.7194 46.0887 40 1.3200 14.7896 34.5064 50 1.0600 11.8437 27.5562 60 1.0000 11.1824 22.4831 Fig. 4 Relationship of the sales effort cost ratio 6. Conclusion This paper investigates three supplier strategies to motivate the retailer to order more than its optimal quantity through the mechanism of the buyback contract. The supplier's marketing strategy types in the two-echelon supply chain include both profit-oriented and sales-oriented strategies. Following is the main conclusion: • The new buyback contract parameters are limited by both the reserved earnings of the supplier and the distribution of market demand when the supplier is sales oriented; the 214 Advances in Production Engineering & Management 11(3) 2016 Consideration of a buyback contract model that features game-leading marketing strategies supplier's expected profit is a decreasing function of the wholesale price or the buyback price. In contrast, the retailer's expected profit is an increasing function of the wholesale price or the buyback price. The supplier would prefer a higher buyback price to stimulate the retailer to order more, but the retailer would prefer a lower wholesale price. The reason for these preferences is that from which the supplier or the retailer benefits on the transfer-payment front. • Based on the former two figures, the supply-chain revenue experiences almost no change when the supplier motivates the retailer to order more than its optimal quantity. In that situation, it is possible to satisfy the sales-oriented supplier's objective. The issue is how to distribute the supply chain's profit. However, all strategies above are based on the same expected shortage in quantity, meaning that more orders create the need for more sales. The former two strategies do not solve this problem. • The order quantity must be increased if the retailer strengthens his sales effort. Strategy 3 discusses the retailer's sales efforts, which are made at a certain cost to the retailer. This situation requires an optimal level of sales effort to obtain more profits; however, it leads to smaller orders. Further research on the fair distribution of supply-chain revenue, with the retailer ordering more and selling as much as possible to effect the supplier's strategy, should be conducted in the future. References [1] Chen, Y.G., Zhang, W.Y., Yang, S.Q., Wang, Z.J., Chen, S.F. (2014). Referral service and customer incentive in online retail supply chain, Journal of Applied Research and Technology, Vol. 12, No. 2, 261-269, doi: 10.1016/S1665-6423(14)72342-9. [2] Pasternack, B.A. (1985). Optimal pricing and return policies for perishable commodities, Marketing Science, Vol. 4, No. 2, 166-176, doi: 10.1287/mksc.4.2.166. [3] Cachon, G.P. (2003). Supply chain coordination with contracts, Handbooks in operations research and management science, Vol. 11, 227-339, doi: 10.1016/S0927-0507(03)11006-7. [4] He, H., Fang, X., Du, Y. (2014). Revenue sharing contract design with marketing strategy types of suppliers. In: LISS 2014 - Proceedings of 4th International Conference on Logistics, Informatics and Service Science, SpringerVerlag, Berlin Heidelberg, Germany, 275-280, doi: 10.1007/978-3-662-43871-8 42. [5] Lee, H.L., Padmanabhan, V., Taylor, T.A., Whang, S. (2000). Price protection in the personal computer industry, Management Science, Vol. 46, No. 4, 467-482, doi: 10.1287/mnsc.46.4.467.12058. [6] Yan, N.-N., Huang X.-Y. (2005). Returns policy model for supply chain with E-marketplace, Systems Engineering -Theory Methodology Application, Vol. 14, No. 6, 492-496. [7] Ding, D., Chen, J. (2008). Coordinating a three level supply chain with flexible return policies, Omega, Vol. 36, No. 5, 865-876, doi: 10.1016/j.omega.2006.04.004. [8] Cai, J.H., Huang, W.L., Zhang, Z.G. (2008). Study on a two-echelon supply chain inventory model under buy-back contract, Journal of Industrial Engineering and Engineering Management, Vol. 22, No. 1, 122-124. [9] Loch, C.H., Wu, Y. (2008). Social preferences and supply chain performance: An experimental study, Management Science, Vol. 54, No. 11, 1835-1849, doi: 10.1287/mnsc.1080.0910. [10] Ho, T.H., Su, X., Wu, Y. (2014). Distributional and peer-induced fairness in supply chain contract design, Production and Operations Management, Vol. 23, No. 2, 161-175, doi: 10.2139/ssrn.2246818. [11] Lin, R.., Hou R. (2014). Experimental tests of buyback contract coordination and analysis for its failure, Journal of Management Science, Vol. 27, No. 3, 75-82. [12] Zhang, Y., Donohue, K., Cui, T.H. (2015). Contract preferences and performance for the loss-averse supplier: Buyback vs. revenue sharing, Management Science, Vol. 62, No. 6, 1734-1754, doi: 10.1287/mnsc.2015.2182. [13] Sluis, S., De Giovanni, P. (2016). The selection of contracts in supply chains: An empirical analysis, Journal of Operations Management, Vol. 41, 1-11, doi: 10.1016/i.iom.2015.10.002. [14] Chen, J.X., Yu, C.H., Jin, L. (2004). Mathematical Analysis, Vol. 2, 94-96, High Education Press, Beijing, China. [15] Ding, H., Guo, B., Liu, Z. (2011). Information sharing and profit allotment based on supply chain cooperation, International Journal of Production Economics, Vol. 133, No. 1, 70-79, doi: 10.1016/j.iipe.2010.06.015. [16] Tirole, J. (1997). Industrial Knowledge Theory, Vol. 1, 138-179, China People's Publishing House, Beijing, China. [17] Xu, Z., Zhu, D.-L., Zhu, W.-G. (2008). Supply chain coordination under buy-back contract with sales effort effects, Systems Engineering - Theory & Practice, Vol. 133, No. 1, 1-11. Advances in Production Engineering & Management 11(3) 2016 215 Advances in Production Engineering & Management Volume 11 | Number 3 | September 2016 | pp 216-226 http://dx.doi.Org/10.14743/apem2016.3.222 ISSN 1854-6250 Journal home: apem-journal.org Original scientific paper A green production strategies for carbon-sensitive products with a carbon cap policy Ma, C.a, Liu, X.b*, Zhang, H.b, Wu, Y.b aTian Fu College of SouthWestern University of Finance and Economics, Chengdu, China international Business School, Sichuan Technology and Business University, Chengdu, China A B S T R A C T A R T I C L E I N F O This paper discusses the production strategies used by manufacturers of carbon-sensitive products that have a carbon cap Policy under both deterministic demand and stochastic demand. In this study, we examine green manufacturing strategies for carbon-sensitive products under carbon cap policy regulations. We primarily consider the two scenarios of deterministic demand and stochastic demand. When the carbon cap Policy regulation has no restriction to the production of the manufacturers, the higher the carbon sensitivity coefficient of the product, the lower the profit of the manufacturing enterprise. When carbon cap Policy regulation of manufacturing enterprise production is a constraint, for the deterministic demand, with the higher carbon sensitive coefficient, manufacturing enterprise profit is higher; for stochastic demand, With the increasingly high carbon sensitive coefficient, manufacturing enterprise profit is low. Through the above research, the conclusion of this paper has reference value and guiding role to carbon-sensitive products' green production strategies with a carbon cap policy. © 2016 PEI, University of Maribor. All rights reserved. Keywords: Production strategy Carbon sensitive Carbon cap policy *Corresponding author: uestc-vip@163.com (Liu, X. ) Article history: Received 12 June 2016 Revised 5 July 2016 Accepted 18 July 2016 1. Introduction Productivity has greatly improved since the Industrial Revolution. However, that production consumes a significant amount of energy and produces large quantities of carbon dioxide, which has triggered changes in the global climate. The International Energy Agency (IEA) estimates a world gross domestic product (GDP) of 70 trillion in 2011 and 3.4 percent average annual growth from 2008 to 2035. With economic development, energy consumption has greatly increased, and our country will soon be confronted by the serious issue of energy-resource shortages. If each 1 percent GDP increase results in a 0.47 percent energy-consumption increase, world economic development will primarily rely on fossil fuels. More importantly, a non-profit government consulting institute, the LMI Research Institute, has stated that commercial activity in all manufacturing sectors count for much in carbon emissions. The carbon emissions produced by the manufacturing industry are caused by the use of raw materials (the transportation of semiconductors, steel, energy resources), manufacturing processes (heating treatments, welding, pressing) and waste-disposal process (carbon emission from waste-disposal plants). To mitigate global warming and reduce environmental pollution, governments worldwide are actively responding by publishing policies intended to solve this problem. The primary issue is how to transform human production and lifestyles to achieve a low-carbon economy and lifestyle. The Kyoto protocol provided a standard and direction for solving the global-warming 216 A green production strategies for carbon-sensitive products with a carbon cap policy problem. The implementation of a carbon quota has been derived from the Kyoto Protocol, which aims to achieve effective emissions reduction through a binding, legal requirement that greenhouse-gas emissions be maintained within a certain range. Furthermore, with an increase in environmental protection consciousness, consumers hope decrease carbon emissions as well as lower the prices, and enhance environmental protections. However, industrially manufactured products are carbon-sensitive products. With the establishment of a carbon quota mechanism, enterprises must consider the issue of carbon emissions. Simultaneously, because consumers are more likely to buy products with low carbon and environmental protections, a product's carbon sensitivity also has an impact on product demand. In this context, production enterprises can both improve market demand and increase corporate profits by emphasizing the low-carbon , environmentally protective characteristics of carbon-sensitive products. Therefore, when an enterprise is required to adopt a carbon quota policy, the question of how it can realize sustainable development and social responsibility while growing its profits becomes a key aspect of both enterprise operation and enterprise development. Simultaneously, this issue has become the subject of major research both at home and abroad. Therefore, research on the production strategy of carbon-sensitive products under a carbon cap policy can provide the basis of and reference for an enterprise's production activities. There have been relevant studies both at home and abroad on the production strategy associated with carbon quota policies. Hong et al. [1] considers retailer ordering and pricing decisions under carbon cap policies and discusses the impact of carbon emissions trading on retailer ordering, pricing and maximizing expected profit. Bouchery et al. [2] add carbon cap-and-trade to the inventory model, analyzing the effect of carbon quotas on the inventory model. Chaabane et al. [3] find that regarding carbon emissions trading, with the establishment of a relevant supply chain model, carbon limits can effectively reduce carbon emissions. Benjaafar et al. [4] study the impact of carbon limitation and transaction policies on enterprises' behavior associated with investment, production, inventory and ordering decisions. Enterprises can maximize profits by modifying order quantity. Zhang and Xu [5] investigate the multi-item production-planning issue associated with carbon cap-and-trade mechanisms where an enterprise produces vary products that fulfil independent stochastic demands with a common capacity and carbon emission quota; those authors use numerical analyses both to illustrate their findings and to identify managerial insights and policy implications. Using an economic order quantity (EOQ) model, Chen et al. [6] provide a situation where it is possible to lower emissions by altering the number of orders. They also provide the situations where the emissions reduction is comparatively greater than the cost increase. Moreover, they study the elements that influences differences in the magnitude of decrease in emission and rise in cost Ma et al. [7] demonstrates the effectiveness of the use of cap-and-trade policy as a mechanism to encourage manufacturers to reduce carbon emissions while obtaining expected profits through their use of green technology inputs. Qi et al. [8] stress the value of centralized management of value chain decisions and sharing of knowledge for Mass customization capability. Regarding to economic benefit and emission reduction, a multi-goal optimization model has been set by Qu et al. to show their relationship; they show that when it compared with the original policy, the collection of diverse emission-reduction policies make greater-efficiency emission reduction and less economic loss. Mutingi [10] plays an important role in both academics and professionals in the field of green supply-chain management. First, Mutingi's study provides a great deal of information to construct a practical tool or framework for managers in the development of green supply-chain tactics given the certain industrial situations where those tactics are used. Second, Mutingi's taxonomic framework provides managerial view about the effects of the selection of certain strategies for a supply chain's operations policies. Using a duopoly model, Wang and Wang [11] quantitatively explore the impact of a carbon offsetting scheme on both emission-trading participants' profits and industry output by drawing on the advanced experience of carbon-offsetting schemes in developed countries. A negative relationship between firms' carbon intensity and their equilibrium output in the product market is revealed from the outcomes. Furthermore, that study presents a commencement for the com- Advances in Production Engineering & Management 11(3) 2016 217 Ma, Liu, Zhang, Wu pared importance of duopoly enterprises' carbon intensity where their absolute output will differ dramatically. Sengupta [12] considers that when consumers are aware of a product's green and environmental protections, they assume that green technology can both improve the production of green products and offer environmental protection; an appropriate increase in prices will generate additional profits. Koren et al. [13] analyses the effect of technical and organizational views on the product complexity and to identify where most incentives for innovation initiate, and the influence on the product complexity. Buchmeister et al. [14] think the implication of weak demand discrepancy and level constraints within the supply chain on the bullwhip effect was evident. Liu et al. [15] use a Stackelberg model to study the problem of competition in the two stages of the supply chain, discussing not only product competition among suppliers but also competition among retailers. Those authors consider how suppliers and retailers can both obtain more benefits and improve their level of competitiveness. Xu and Zhao [16] show that supply chain cooperation can raise the emissions reduction level and increase the expected total profit. Finally, the effects of different parameters on the coordination of supply chain's performance are discussed. Li et al. [17] through the establishment of the Stackelberg game model, it is concluded that the optimal emission reduction level and the optimal proportion of the retail and supply, and the optimal profit value of the two in different contract forms. Huang and Zhao [18] study bargaining between manufacturers and retailers in the case of consumers' low carbon preferences, analysing both the influence of a manufacturer's pricing on the retailer and the function of the two parties. Because of the relevant environmental protection policy and consumer awareness of both environmental protection and low carbon emissions, research on carbon-sensitive product manufacturers' production strategies under a Cap policy can provide manufacturers with valuable information. 2. Problem statements and basic assumptions This paper studies a manufacturer in a monopoly market. The manufacturer produces only one product (for example, a smart phone); the remaining inventory is produced in accordance with residual value processing at the end of a sales period. The product's decision-making value is its production; the manufacturer's decision-making goal is profit maximization. The government has specified the largest carbon emissions E, under its carbon cap policy. To achieve carbon-emissions reduction targets, the carbon emissions of manufacturers' production activities cannot exceed the maximum level set by the government. At the same time, consumers demand low-carbon and environmental-protection features in their products; those features are associated with the products' carbon-sensitive coefficient k. Therefore, consumer demand influences production. This paper primarily studies the following two issues: Under the deterministic demand condition, demand is equal to the economic order quantity (EOQ) and thus, to both a manufacturer's production strategy with a carbon cap policy and the influence of a carbon-sensitive coefficient on profits; and Under the stochastic demand condition, requirements are related to price and a product's degree of carbon sensitivity and thus, to both a manufacturers' production strategy with a carbon cap policy and the influence of a carbon-sensitive coefficient on profits. For convenience, the model's main variables are listed below: k - Carbon-sensitive coefficient e - Product's per-unit carbon emissions E - Government limit on carbon emissions a - Unit of time of potential market demand D - Deterministic demand per unit of time Q - Production v - Residual value per unit product 218 A - Deterministic costs of each order at a particular time h - Annual inventory holding cost per unit product c - Cost of production per unit product p - Unit price of the product g - Shortage cost of one unit of the product 146 Advances in Production Engineering & Management 11(3) 2016 A green production strategies for carbon-sensitive products with a carbon cap policy 3. Deterministic demand model establishment and analysis Under the deterministic demand condition, demand is equal to EOQ and the relationship between demand and the carbon-sensitive coefficient k is D = a — ke(D,a,k,e > 0). 3.1. Basic model In the case of no carbon constraints, take the related parameters into the EOQ formulae: D Q TC = cD + -A + 2h (1) TC of Q derivative: dTC (a — ke)A h ~dQ= QÏ +2 Make ^^ = 0, and obtain the optimal production: Q* = M 2(a — ke)A ,„, — (2J h The optimal profit of the manufacturer is: n*(Q) = (p — c)Q*, that is, M by Eq. 3 2{a — ke)A (3) h Proposition 1: In the absence of a carbon quota restriction, if other conditions remain unchanged, the optimal profit n*(Q) is a decreasing function of the carbon-sensitive coefficient k. Proof: n*(Q) of k derivative: d n*(Q) = dk M 2e2A <0 h{a — ke) The profit is a decreasing function n*(Q) of the carbon-sensitive coefficient k; with an increase in k, n*(Q) decreases, while with a decrease in k, n*(Q) increases. End of proof. 3.2. Manufacturers' production strategy under a carbon cap policy Under the carbon-limitation condition, the EOQ can be obtained: TC = cD +-A+-h (4) Q 2 s.t.eQ 0, the constraint conditions can be: eQ-E <0 (6) Advances in Production Engineering & Management 11(3) 2016 219 Ma, Liu, Zhang, Wu e = 0 (8) When

0, by Eq. 8, ^ = (a~fc2eM + ^ = q)e>0; therefore Qa E, then Qa E, the profit function of a manufacturer of carbon-sensitive products na is an increasing function of the carbon-sensitive coefficient k, and with a decrease in k, na decreases, while with an increase in k, na increases. Proof: 1. Because eQ* < E, equivalent to a non-carbon cap, with a proof of theorem 1. 2. When eQ* >E, na(Qa) of k derivative: eQ*E (9) Theorem 2: (10) 220 Advances in Production Engineering & Management 11(3) 2016 A green production strategies for carbon-sensitive products with a carbon cap policy Because g—— > 0, the profit function na is an increasing function of the carbonsensitive coefficient k; with a decrease in k, na decreases, while with an increase in k, na increases. End of proof. In summary, the demand is determined, there is a carbon quota policy regulation, and the optimal production quantity of manufacturing enterprises for Q* = if carbon emissions from manufacturing enterprises are far less than the carbon limits and will not exceed the carbon limits. With an increased carbon-sensitive coefficient k, manufacturing enterprises might consider it appropriate to reduce production and increase profits. With a decrease in the carbonsensitive coefficient k, manufacturing enterprises might consider it appropriate to reduce production and increase profits. With a decrease in the carbon-sensitive coefficient, manufacturing enterprises can appropriately increase production and profits. If production increases, a manufacturing enterprise's carbon emissions will exceed the carbon quota and the enterprise needs to control production activities. With an increase in the carbon-sensitive coefficient k, manufacturing enterprises can appropriately increase production and then improve profits. With a decrease in the carbon-sensitive coefficient k, manufacturing enterprises can consider appropriately reducing production and increasing profits. 3.3. Numerical analysis From the model solution, in a carbon-sensitive demand situation, the carbon-sensitive coefficient will affect the manufacturer's optimal production and maximum profit. To understand the influence of the carbon-sensitive coefficient and the carbon cap policy on the manufacturers' optimal production and the maximum profit, the following numerical analysis method was used to analyze the sensitivity of the parameters. For the convenience of numerical analysis, let a = 100 , e = 10 , ^ = 10 , h = 20, p = 100 , c = 50, g = 30, E = 80, e = 10. Making k e (1 , 10), we can obtain Fig. 1 and Fig. 2. From Fig. 1 and Fig. 2, we can see that with the decrease in both the carbon-sensitive coefficient and production, manufacturer's profits first decrease and then increase, which means that when a carbon cap policy does not work, with the increase in the carbon-sensitive coefficient, profits decrease. When the carbon cap policy works, with the increase in the carbon-sensitive coefficient, profits increase. The optimal production at this time is Q* = J2(a~*e)A = 9.75. 10 ° - 9.7 9.4 8- 9.2 ———a a 6 8.6 ...........................................................................^.................................................................................. ~ O - * \ 8.3 8.0 —a— n*a(Q*a) —a— n'a(Q'a) 7.7 —k 2 —x— Q* 7.0 x x Fig. 1 Carbon-sensitive coefficient impact on profits Fig. 2 Production impact on profits Advances in Production Engineering & Management 11(3) 2016 221 Ma, Liu, Zhang, Wu 4. The stochastic demand model establishment and analysis With the stochastic demand, make x as a stochastic demand and obey follow the probability density function of the demand for /(•) distribution. According to the demand function and supply function, the price function for the relationship with the carbon-sensitive coefficient k is: p= j — keQ, (, k, e, Q > 0, k as the carbon-sensitive coefficient) 4.1. Basic model In the case of no carbon constraints, we construct the model according to the relationship of price and the carbon-sensitive coefficient function, combined with the newsboy structure of profit model for production Q: n(Q) = U ~keQ rQ — v) I xf(x)dx Jo -(c-v)f Qf(x)dx + (j-keQ + g (11) Jo „ CD „ CD ~c) I Qf(x)dx — g I xf(x)dx Jn Jn If we make dn (Q) rQ dQ = (j + g — c) — 2keQ + ke I F(x)dx = 0 we then obtain J j + g — c = — j F(x)dx^ke For ease of calculation, make G(Q) = 2Q — j® F(x)dx. The optimal production is as follows: n(Q) of k derivative allows us to obtain dn(Q) rQ dk = -eQ[\ xf(x)dx+\ Qf(x)dx] <0 (13) Jo Jq Proposition 2: When demand is stochastic, there is no carbon quota policy constraint and the profit of a manufacturer of carbon-sensitive products n(Q) is a decreasing function of the carbon-sensitive coefficient k; with an increase in k, n(Q) decreases, while with a decrease in k, n(Q) increases. Proof: From (12), optimal production Q* is a decreasing function of the carbon-sensitive coefficient k, The general model of the profit function is: n = (p — c — v) Q*. Profit n is proportional to the carbon-sensitive coefficient Q*, and profit n has an inverse relationship with the carbonsensitive coefficientk; with an increase in k, n decreases, while with a decrease in k, n increases. In conclusion, the results are the same as for (13), and the proof is complete. End of proof. 4.2. Manufacturers' production strategy under carbon cap policy Under the carbon cap policy, carbon emissions in manufacturers' production activities must not exceed the government's largest carbon emissions. The largest production for manufacturers is -. Through a discussion of the optimal production strategy in this case, the following theorems are obtained: 222 Advances in Production Engineering & Management 11(3) 2016 A green production strategies for carbon-sensitive products with a carbon cap policy Theorem 3: Under conditions of stochastic demand, a manufacturer of carbon-sensitive products has a carbon cap policy of its optimal production Qa 0, can be obtained by constraint conditions: eQ-E <0 (14) q>(e Q-E) = 0 (15) rQ (j + g-c)-2(ke)Q + ke \ F(x)dx- 0, using Eq. 16 we can obtain dn^^ = (j + g — c) — 2(ke)Q + ke f® F(x)dx = 0, therefore, we can obtain Qa E, then Qa F Model 182.11 6 30.35 91.25 < 0.0001 A 0.22 1 0.22 0.67 0.4327 B 73.21 1 73.21 220.09 < 0.0001 C 0.47 1 0.47 1.40 0.2664 F 67.75 1 67.75 203.67 < 0.0001 AC 3.80 1 3.80 11.44 0.0081 BF 36.66 1 36.66 110.22 < 0.0001 Residual 2.99 9 0.33 - - Total 185.11 15 - - - : = 98.38 % 'o, Adjusted R2 = 97.30 0, <6, Predicted R2 = 94.89 %, Adequate precision = 25.454 Advances in Production Engineering & Management 11(3) 2016 231 Mohamed, Masood, Bhowmik (a) [Standardized Effect| (b) Externally Studentized Residuals Fig. 3 (a) half normal probability plot of the standardized effects, and (b) normal probability plot, for dynamic modulus of elasticity Predicted vs. Actual Residuals vs. Run (a) Actual (b) Run Number Leverage vs. Run 1.000.600.600.400.20- Dynamic motfulus ol elasticity [MPa] ■ Í4.2S7 14.240 1 4 7 10 13 16 (c) Run Number Fig. 4 (a) predicted versus actual plot, (b) residual versus run number plot, and (c) leverage versus run number plot Effect of layer thickness (slice thickness) on the dynamic modulus of elasticity of the parts can be seen in Fig. 5. With the increase in slice thickness, dynamic modulus of elasticity of the part slightly increased. It is because as the layer thickness increases, it produces thick rasters with minimum number of layer. This leads to the improvement in dynamic mechanical properties of the built part. Nevertheless, if the part is fabricated with thin layers, there would be micro-voids and tear in a part surface (see Fig. 6). Thus the sample processed with thin layers exhibits lower mechanical performance. Fig. 5 reveals the influence of air gap on the dynamic modulus of elasticity. It is found that with an increase in air gap, dynamic modulus of elasticity decreases gradually. The main reason is that when the air gap increases, a close raster and deposited beads are generated, which leads to a dense structure resulting in improvement in dynamic modulus of elasticity of parts. 232 Advances in Production Engineering & Management 11(3) 2016 Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing Fig. 5 Effect of various operating conditions on dynamic modulus of elasticity Fig. 6 Microstructure observation of the effect of thin layer on the properties of the manufactured part Fig. 5 also shows the impact of raster angle (raster pattern) on the dynamic modulus of elasticity on the samples built through FDM. It has been observed the dynamic modulus of elasticity for the manufactured part decreases with increasing raster angle from 0° to 90°. The main reason behind this phenomenon is that when the raster angle increases, the energy absorbed by the manufactured part decreases. This is due to the fact that at raster angle of 90° an adhesive failure occurs at the bonding interface level of the deposited layers (see Fig. 7). This leads to reduction in the dynamic modulus of elasticity of the processed part. Fig. 7 clearly shows the phenomena behind the influence of two raster's angles on the dynamic modulus of elasticity. Bending load Fixed sample Bending load Bending load Sample processed Sample processed with with raster an«;!« of 0 raster angle of 90° Fig. 7 Failure of different rasters under periodic bending load Advances in Production Engineering & Management 11(3) 2016 233 Mohamed, Masood, Bhowmik The effect of build orientation on dynamic modulus of elasticity is illustrated in Fig. 5. To acquire high deformation resistance for the fabricate part, it is preferable to manufacturing the part along the X-axis (0°) as this can greatly improve the curve definition for rasters, and can decrease stair-stepping effect. Fig. 5 indicates that the road width has no effect on the dynamic modulus of elasticity. Thus this factor has been removed from the regression model expressed which is by Eq. 2. However, in general it is advisable to use thin road width as thin road width provides finer raters and layers, which helps in filling more spaces on the part structure. Thus the built parts tend to have better mechanical properties, better dimensional accuracy and improved surface roughness. The effect of number of contours on dynamic modulus of elasticity is shown in Fig. 5. The results indicate that higher values of dynamic modulus of elasticity can be obtained by considering 10 contours. The maximum contour lines can guarantee elevated absorb and discharge energy levels and help the part to return to its original position after the stress is released. Because the reason for this improvement is maximum number of contours reduces the number of rasters, which helps to create the solid and dense structure (see Fig. 8) and hence increases the dynamic modulus of elasticity. Fig. 8 Microstructure observation of the effect of 10 contours on the properties of the manufactured part Fig. 9 portrays the dual influence of air gap and number of contours on dynamic modulus of elasticity at a constant level of the other processing parameters. It can be concluded that maximum dynamic modulus of elasticity is feasible with a combination of low air gap and higher number of contours. However, an interesting phenomenon can be noticed from Fig. 9 that using highest value of air gap along with maximum number of contours higher dynamic modulus of elasticity can still be obtained. This is because the part still has solid structure under this parametric combination, and hence this combination of process parameters helps to improve the mechanical properties while reducing the production cost as positive air gap minimizes the processing time. 234 Fig. 9 Combined effect of air gap and number of contours on dynamic modulus of elasticity Advances in Production Engineering & Management 11(3) 2016 Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing 20.000 15.117 0.0000 1 0000 .127 .3302 0. -a—s—s—B- —a—B—a—B— -a—B—a—B- -Q ns "(ft D O 1 r* J/ jQ ra k. en