914 KB0 KB201420262023Alfaro-Ponce, M.Chairez, I.Jimenez-Martinez, M.Molina, ArturoMunoz-Ibanez, C.letnik:18številka:2str. 163-174DOI:10.14743/apem2023.2.464ISSN:1854-6250COBISSID_HOST:268255747URN:URN:NBN:SI:doc-3G2S4307enFakulteta za strojništvo, Inštitut za proizvodno strojništvoAdvances in production engineering and managementaluminium die-castingcost factorsentity modellingforecating modelshibridni modelihybrid modelsoptimizacijaoptimizationproject planningprojektno planiranjetime factorsHybrid forecasting modelling of cost and time entities for planning and optimizing projects in the die-cast aluminium industry|The techniques employed to manage an industrial project are based on tools that aim to achieve the objectives set by an organization. Most of these techniques consider the development of operative and predictive models. The difficulty in developing project planning models relies on estimating large sets of parameters and the need to include model sections of poorly identifiable, that increase costs and time. This work develops a hybrid forecasting model for all the phases that make up die-casting projects through a series of parameters and sub-models that contemplate the particularities of each case, thereby achieving greater precision in the forecast. The model identifies the cost and time factors that affect project planning, specifically in the die-casting industry, and intends to predict their future behaviour when certain initially given conditions are modified. To estimate the parameters of the hybrid model, several factors in the processes were considered that interact in this industry, such as primary matter costs and activities associated to the process. The considered processes that have a substantial economic impact on the implementation of the project were selected. The criteria for this selection considered identifying the relevant parts of the design and manufacturing in the die-casting industry. Process factors such as the Cost of aluminium and its related activities, whose processes will be grouped into cost and time entities to build a set of metrics that allow better control over them. Finally, the proposed model is based on analytical, parametric, and analog methods that achieve accuracy greater than 85 % in predicting the time and Cost of the processTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Mariboru, Fakulteta za strojništvo