*Corr. Author’s Address: University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000 Ljubljana, Slovenia, nejc.rozman@fs.uni-lj.si 585
Strojniški vestnik - Journal of Mechanical Engineering 68(2022)10, 585-609 Received for review: 2022-09-02
© 2022 The Authors. CC BY 4.0 Int. Licensee: SV-JME Received revised form: 2022-09-26
DOI:10.5545/sv-jme.2022.355 Review Scientific Paper Accepted for publication: 2022-09-26
Scalability Solutions in Blockchain-Supported Manufacturing:  
A Survey
Rožman, N. – Corn, M. – Škulj, G. – Diaci, J. – Podržaj, P.
Nejc Rožman
* – 
Marko Corn – Gašper Škulj – Janez Diaci – Primož Podržaj
1
University of Ljubljana, Faculty of Mechanical Engineering, Slovenia
Researchers in the field of smart manufacturing have recognized the benefits of blockchain technology, which solves the trust problem 
in the open network without relying on any trusted third party. Blockchain technology enables interaction between otherwise competing 
manufacturing entities to satisfy increasing customer demands in a trustful way. However, existing blockchain networks are facing limitations, 
which are defined by the trade-off between scalability, decentralization, and security. The scalability of the blockchain network is defined as 
the ability of the network to support an increasing load of transactions and it is lower compared to the non-blockchain systems. In order to 
omit the effects of the limitations, scalability solutions are being presented. This research reviews the literature in the field of blockchain-
supported manufacturing concerning scalability solutions. The selected literature has been reviewed and classified according to the type of 
scalability solution. For each type of scalability solution, the main features of the concepts and connection between blockchain technology 
and manufacturing system are highlighted and discussed. The main findings of the study are that Layer 1 scalability solutions are better 
represented in the literature and are predominating in the case of general smart manufacturing systems, whereas Layer 2 scalability solutions 
are better represented in the case of specific smart manufacturing systems. Based on insights obtained from the presented analysis, future 
directions and open issues regarding the scalability limitations and solutions in blockchain-supported manufacturing are presented.
Keywords: blockchain, manufacturing, scalability, trilemma
Highlights
• 	 The literature on scalability solutions in blockchain-supported manufacturing does not follow the trends of publications in the 
field of scalability solutions.
• 	 Most solutions employ the consensus mechanism, off-chain solutions, or optimize blockchain structures.
• 	 Layer 1 scalability solutions are better represented than Layer 2 solutions. Layer 1 solutions are predominating in the case of 
general smart manufacturing systems and Layer 2 in specific smart manufacturing systems.
• 	 The research on scalability limitations in blockchain-supported manufacturing is increasing and improving over time.
• 	 Major open issue with the literature proposed solutions is a lack of implementation in the industry and insufficient analysis of 
the scalability trilemma in connection with practical limitations.
0  INTRODUCTION
In recent years, we have witnessed an increase in the 
literature introducing blockchain technology in the 
field of manufacturing [1]. Technological advances 
and the stringent requirements of Industry 4.0 have 
resulted in new concepts of smart manufacturing 
systems. Manufacturing systems are formed into 
complex organizational networks that take advantage 
of a high level of digitized manufacturing segments to 
meet as many diverse customers’ needs as possible [2]. 
Individual manufacturing activities are packaged in 
services that are available on shared virtual platforms 
(service-oriented architecture) [3]. By connecting 
through such organized networks, manufacturers 
can meet a larger number and more diverse market 
requirements. As there are interactions between 
production entities that have different owners, the 
problem of trust in the operation of the system arises. 
Typically, centrally managed platforms that allowed 
the integration of production entities were identified 
as weaknesses in the system, mainly from a security 
perspective [4].
Blockchain technology, which was implemented 
in response to the manipulations of centralized 
organizations in the banking system, is presented as 
an answer to the problem of ensuring trust between 
competing entities participating in global networks [4]. 
In conjunction with smart manufacturing networks, it 
can ensure that interactions between individual entities 
in the system are recorded, in a self-executing manner, 
in a transparent and immutable blockchain maintained 
by a decentralized network [5]. So far, these features 
have been used in manufacturing to address various 
cyber security issues in smart manufacturing systems 
(SMS) to increase trust in the system [6].
However, blockchain technology also has certain 
limitations. The limitations of blockchain technology 
are represented by the scalability trilemma [7]. 
Similar to the consistency, availability, and partition 
tolerance (CAP) theorem [8] in the traditional field 
of the distributed system, a trade-off occurs between 
three blockchain network properties, namely: 

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scalability, security, and decentralization. In the 
currently existing major public blockchain networks 
(e.g. Bitcoin, Ethereum), it has repeatedly happened 
that the network failed to process a large increase of 
transaction requests resulting in much longer waiting 
times than usual for transactions to be processed. 
Scalability limitations of blockchain technology thus 
affected the operation of the blockchain-supported 
systems [9]. Due to the limited transaction throughput, 
pending transactions are waiting longer in line to 
be confirmed. Priority in line is defined by a set 
transaction fee, therefore, the transaction fee increases 
due to the competition for faster confirmation. The 
reduced number of nodes storing the whole ever-
growing blockchain results in delays when querying 
data from the blockchain.
Global manufacturing networks are big and a 
large number of interactions between entities can 
be assumed. This, in turn, means that the traditional 
approach of implementing blockchain technology 
in manufacturing systems would not satisfy the 
performance requirements and at the same time ensure 
decentralization and security (which creates the trust). 
For example, if the frequency of incoming transactions 
from the manufacturing system exceeds the frequency 
of transaction confirmation of the blockchain network 
(e.g. Ethereum confirms 15 transactions per second), 
the manufacturing system would be affected by the 
scalability limitations of the blockchain technology. 
However, scalability solutions have already been 
presented in the field of blockchain technology, 
which can increase scalability or enable the change 
of blockchain network properties according to the 
trilemma and thus offer different properties according 
to user requirements.
There are already several published literature 
reviews on the topic of integrating blockchain 
technology into manufacturing. A survey was made 
regarding different aspects of the engineering 
and manufacturing processes where researchers 
or developers have already proposed or applied 
blockchain [1]. Similar research was conducted 
regarding the existing blockchain applications in 
Industry 4.0 and industrial internet of things (IIoT) 
settings [10]. Another survey discusses the research 
progress of blockchain-secured smart manufacturing 
and how blockchain technology is applied to address 
cybersecurity issues in the smart manufacturing 
system [6]. The research was also presented regarding 
the literature on achieving sustainability by employing 
blockchain technology in manufacturing systems and 
product lifecycle management [11].
However, so far no one has discussed how 
the presented concepts of blockchain-supported 
manufacturing take into account the constraint on 
scalability that blockchain technology introduces into 
manufacturing. In this paper, a review of the literature 
on the scalability solutions applied in blockchain-
supported manufacturing is presented. The main 
contributions of the paper are summarized as follows:
• Literature addressing the problem of scalability 
and scalability solutions in blockchain-supported 
manufacturing is identified and classified 
according to existing scalability solutions. The 
main features of emerging concepts are presented.
• The literature is analyzed regarding the type of 
scalability solutions, the type of manufacturing 
system, and the extent of concept presentation.
• Based on the analysis, future directions 
and open issues in research regarding the 
scalability limitations in blockchain-supported 
manufacturing are given.
1  BLOCKCHAIN TECHNOLOGY IN MANUFACTURING
1.1  Blockchain Technology
A blockchain is a distributed database where an 
ordered list of various records is stored on. Records are 
stored in blocks connected through links in form of a 
chain [12]. The links between blocks are made with the 
use of asymmetric cryptography [13]. Each record in 
the blockchain made by a user is signed with a set of 
encryption keys that are self-managed and unique [14]. 
Nodes in the blockchain network are communicating 
with each other to establish synchronization of the 
written data in the blockchain.
The key properties of the blockchain network are:
• decentralized consensus,
• immutability of the records,
• transparent database,
• self-executing environment.
The first key property of the blockchain network is 
its ability to reach a consensus among the nodes in the 
network (solving Byzantine generals problems [15]). 
Consensus must be reached for every change of data 
written on the blockchain (e.g. new transaction). The 
consensus mechanism represents democratic voting on 
the changes of the written records on the blockchain 
and it further means that all of the written changes of 
the blockchain were made in agreement of the majority 
of the participants in the transaction confirmation on 
the blockchain network. This coordination between 
nodes in the network instills trust that the data in 
the blockchain was written correctly and without 

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587 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
tempering, [16]. There exist two types of blockchain 
networks that are distinguished according to how 
openness to participation in the consensus mechanism 
is defined, namely: permissioned and permissionless 
[17]. Permissioned networks allow only selected 
confirmators to join the consensus mechanism and 
permissionless allow anyone to join the consensus 
process in the network.
Because of the decentralized consensus, 
tampering with existing data written on the blockchain 
is almost impossible. It is only possible if the majority 
of the nodes in the network agree to the changes. 
Therefore, blockchain technology offers immutability 
of the records [18]. Again, there exist two types of 
blockchain networks regarding access to the usage 
of the blockchain network and the access to the data 
written on the blockchain, namely: private and public 
[19]. Private blockchain networks have a strictly 
defined list of users that can create new transactions 
and have access to the written transactions on the 
blockchain. Public blockchain networks on the other 
side enable anyone to create transactions and the 
whole blockchain is publicly accessible. In both types 
of blockchain networks, users that have access to the 
blockchain can verify which blockchain data was 
written and when it was written on the blockchain, 
meaning that blockchain technology provides a 
transparent database.
The last key property of blockchain technology 
is that it enables a self-executing environment. By 
providing a virtual environment on the blockchain 
network, developers can code an assortment of 
automated procedures into digital transactions [20]. 
These programs are named smart contracts and are 
digital contracts allowing terms contingent on the 
decentralized consensus that is tamper-proof and 
typically self-enforcing through automated execution 
[21]. By employing smart contracts, different 
procedures directly related to the change of written 
data on the blockchain can be executed automatically.
Observing diverse blockchain networks leads to 
the conclusion that blockchain networks are unable to 
scale effectively [22], the networks are susceptible to 
various security vulnerabilities [18], and are prone to 
centralization [23]. These observations and research 
conclusions about the current state of blockchain 
networks were being joined into a single idea named 
the scalability trilemma (Fig. 1) [24] and [25]. One 
definition of the Trilemma states that when someone 
is trying to optimize a blockchain-supported system, 
there exists a trade-off between three important 
properties: scalability, decentralization, and security 
[7].
Table 1.  Def initio n	o f	blo c kc hain	ne tw o r k	pr o per ties	t erms
Term Definition
Scalability
The ability of the blockchain network to support 
an increasing load of transactions [26].
Decentralization
The number of nodes in the blockchain network 
participating in the process of transaction 
confirmation [27].
Security
The ability of the blockchain network to defend 
itself against attackers with a certain amount of 
resources [28].
For example, adding a centralized coordinator 
into the system to increase the speed of the consensus 
process would result in a more centralized system. 
Another example, shortening the block interval can 
increase the transaction throughput but also affects the 
security of the whole system because nodes are not 
synchronized. Therefore, balancing or even achieving 
these three aspects of the blockchain system well is 
essential for the future development of blockchain that 
is suitable for more complex and larger-scale scenes in 
our daily lives [29].
SCALABILITY
DECENTRALIZATION SECURITY
TRUST?
Fig. 1.  The scalability trilemma
Where an individual blockchain network is 
positioned according to its characteristics affects 
users’ trust in the operation of that network [30]. Trust 
is a psychological state comprising the intention to 
accept vulnerability based upon positive expectations 
of the intentions or behavior of another [31]. Trust in 
the correct execution of the consensus mechanism 
and recorded data on the blockchain increases with 
decentralization and security. Mainly because a 
stronger consensus has been established among a 
larger number of entities involved in the validation 
of new blocks. This, in turn, means that if we move 
towards a more scalable system with the properties 
of blockchain technology, trust in the system reduces. 
However, it is difficult to define how exactly is trust 
dependent on the three properties of the blockchain 
network (Fig. 1).

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1.2  Blockchain-Supported Manufacturing
The four key properties of blockchain technology 
(decentralized consensus, immutability of the records, 
transparent database, and self-executing environment) 
are enabling a trusted environment for cooperation 
and interaction between users who do not trust 
each other [32]. Based on this fact, researchers have 
recognized blockchain technology as a solution to the 
problem of trust between participants in global smart 
manufacturing networks [6].
The existing networked manufacturing models 
(not blockchain supported) make significant progress 
in information and resource sharing. However, the 
shortcomings of networked manufacturing regarding 
information and resource sharing are considerable 
including delays, asynchronous data between multiple 
parties, multitudes of sharing approaches, irregularity 
in monitoring mechanisms, and the possibility of 
shared data being tampered with or concealed. The 
asymmetric nature of information puts the downstream 
manufacturers’ product quality and credibility at risk. 
In contrast, due to the properties of the blockchain 
technology described in the previous section, the 
blockchain-based approach improves the quality of 
information sharing, reduces operational costs (due to 
intermediation), enables dynamic production resource 
allocation, and enables peer credit evaluation [33]. 
Therefore, blockchain provides an online environment 
for enabling decentralized self-organization and thus 
offloading and accelerating the optimization of upper-
level manufacturing planning [34].
In the general blockchain-supported 
manufacturing model, the blockchain network 
represents an additional sublayer of the infrastructure 
layer [35], which supports the establishment of the 
entire framework by providing the hardware and 
software infrastructure for the ecosystem (Fig. 2). 
Blockchain sublayer is structured to support various 
functions such as manufacturing production, materials 
and inventory management, smart supply chain, and 
security and identity management [36]. Blockchain 
technology provides methods and tools, which 
include application program interfaces, protocols, and 
software development kits to support the exchange of 
data, resources, and knowledge in a transparent, safe, 
and decentralized way [37]. The Internet of things 
(IoT) infrastructure of the manufacturing resources is 
connected directly to the blockchain network through 
extensible embedded software components [38]. 
The manufacturing activities in the smart factory are 
recorded in the blockchain and then retrieved by the 
enterprise applications for monitoring, planning, and 
control [36].
Physical layer
Infrastructure layer
Enterprise layer
Application layer
Platform
ERP, MES, SCMS
Internet,Cloud
computing
Blockchain
sublayer
Manufacturing resources
 IoT
Blockchain network
* ERP - Enterprise Reource Planning, MES - Manufacturing Execution System,
  SCMS - Supply Chain Management System
Fig. 2.  General	blo c kc hain-suppo r t ed	manufacturing	mo del
Smart contracts are employed to write complex 
data on the blockchain network and to enable the 
self-execution of automated procedures. Issued 
smart contracts on the blockchain are containing 
information on the available time for processing, 
processing capabilities, and expected compensation 
for utilization of the manufacturing resources [39]. 
The personalized manufacturing tasks/demands 
are published on the smart contracts and each 
manufacturing service matching between the 
demander and provider is recorded as a transaction in 
the blockchain [38]. Furthermore, smart contracts are 
used to provide membership registration, certification 
authority, and security and privacy verification [36]. 
The captured manufacturing data from the sensors/
controllers is directly written on the smart contract and 
consumers who are calling for manufacturing services 
can verify the capabilities or past actions of providers 
on the blockchain network [38]. The data written on 
the transparent and immutable blockchain is signed 
by the public key of the source, which increases data 
reliability and trustfulness.
The differences in the concepts presented in 
the literature on the implementation of blockchain 
technology in manufacturing systems are mainly 
twofold, namely: the level at which blockchain 
technology connects to the manufacturing system is 
different, and different manufacturing processes are 
being executed using blockchain technology. In the 
case of cloud manufacturing, blockchain technology 
is used only as a storage layer where interactions 
between providers and consumers are recorded, and 

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)10, 585-609
589 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
the cloud platform still ensures that data is published 
and classified in the cloud [40]. The manufacturing 
resources are packed in services, which are then posted 
on the platform and are not necessarily written on the 
blockchain. Similarly, individual manufacturers offer 
their services in the case of social manufacturing (Fig. 
3), where the need and supply are advertised in a peer-
to-peer (P2P) way via social networks [38]. In the case 
of Shared manufacturing, individual production units 
and manufacturing resources are directly connected 
to the blockchain network and are also interacting 
with other entities on the blockchain network [41]. 
The direct connection of individual manufacturing 
resources results in a high amount of interactions that 
are written on the blockchain. This in turn means that 
blockchain-supported Shared manufacturing systems 
are more dependent on the performance properties of 
the blockchain technology.
Manufacturing 
process
tx
Blockchain network
Order pool
MSME 
DAPP
Smart contract
tx
tx
Blockchain ledger
Manufacturing 
community
Allocating order
Sharing 
order
Mapping
Verification 
dataset
Fig. 3.  The concept of blockchain-supported social manufacturing, 
adapted from [42]
2  SCALABILITY SOLUTIONS IN BLOCKCHAIN TECHNOLOGY
Applications built on blockchain technology have 
in the past encountered limitations of blockchain 
technology [43]. The main limitations of scalability 
have severely affected the performance of the 
applications on the blockchain network and have 
degraded the user experience. Scalability limitation 
is the main reason why blockchain technology is 
not more widely adopted in real-world applications 
[9]. For this reason, general-purpose solutions 
for increasing scalability have appeared, which 
are supposed to solve the scalability problem of 
blockchain technology. General-purpose solutions 
represent network organization mechanisms, methods 
of recording data in the blockchain, and topological 
upgrades of the blockchain network. For specific 
applications, the implementation of these solutions is 
adapted and upgraded according to the characteristics 
of the applications.
2.1  Scalability Limitations
Nodes in a blockchain network are communicating 
to update distributed database of data records in a 
coordinated manner. To update distributed database, 
each node must store the whole data by itself. New data 
emerging at one point of the network must propagate 
through the network and nodes must agree to update 
records. These properties of the blockchain network 
are the main reason for the scalability limitations 
of blockchain technology. The major scalability 
limitations (Table 2) of the blockchain technology are 
recognized in transaction throughput, storage size, and 
read throughput [30].
Table 2.  Scalability limitations of the blockchain technology
Scalability 
limitations
Reason Consequences
Transaction 
through-
put
Information propagation, 
network synchronization
Congestion of the blockchain 
network, pending time 
increases, transaction fee 
increases
Storage 
size
The constant growth of 
the distributed database
Large storage requirements, 
fewer nodes are able 
(blockchain) to run a  full 
node 
Read 
through-
put
Lack of full nodes in the 
network, high amount of 
requests from users
Longer waiting time for 
requested data
Transaction throughput is the main scalability 
limitation of blockchain technology as it affects all 
the users of blockchain technology [29]. Due to the 
technological limitations of information propagation 
in the network, blockchain networks cannot confirm 
new transactions faster in a decentralized way. If 
we increase the size of the data, being sent through 
the network the information propagates slower 
through the network [44]. In addition, the larger the 
network the longer it takes for the information to 
propagate through the network. Blockchain networks 
intentionally increase the period of new transaction 
confirmation to enable better synchronization of the 
nodes in the network and thus reduces opportunities 
for malicious acts [24]. The consequences of limited 
transaction throughput are congestion of the 
blockchain networks. When the number of pending 
transactions exceeds the maximum block size, some of 
the transactions will have to wait for another block to 
be included in the blockchain [46]. Such congestion 
of pending transactions for confirmation causes 

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competition between users for the execution priority 
of their transactions. Transaction priority increases by 
higher transaction fees and therefore such blockchain 
congestions results in an overall increase in transaction 
fee on the blockchain network [47].
The storage size limitation of blockchain 
technology emerges due to the constant growth of the 
blockchain and the fact that each node participating 
in the confirmation of transactions must keep a 
complete record of the data on the blockchain [47]. 
All participating nodes must have large storage 
capacities at their disposal to ensure the security of 
the written data on the blockchain. As blockchains 
are growing, fewer and fewer nodes are capable to 
store the whole blockchain on its hardware and the 
decentralization of the network is reducing [48]. 
Consequently, due to the storage size limitations, many 
of the nodes in the network are not storing the whole 
blockchain (are not full nodes) but are storing only 
parts of the blockchain that are relevant to them (light 
nodes) [49]. However, when users who do not run 
blockchain nodes are querying for the data written on 
the blockchain, light nodes cannot provide all the data. 
This is the reason for the read-throughput limitation 
of blockchain technology. The consequences of limited 
read throughput of the blockchain network are longer 
data queries and a longer synchronization period for 
new full nodes joining the network or for nodes who 
missed a message.
Compared with the centralized payment system 
like banks, performance (scalability) cannot be 
improved easily in blockchain, a self-regulating 
system, that needs more consideration to maintain 
decentralization [29].
2.2  Scalability Solutions
In response to the problem of scalability, new solutions 
are being presented that seek to increase the scalability 
of the blockchain network while maintaining the 
same degree of decentralization and security. Most of 
these solutions do not ensure this, but they do enable 
the change of blockchain properties regarding the 
trilemma (increase scalability and reduce security or 
decentralization) according to the requirements of 
the blockchain network users. Solutions that would 
allow for greater scalability while maintaining the 
same level of security and decentralization are mainly 
solutions based on technological leaps (e.g. quantum 
computers). One such example is improving the speed 
of data propagation across the distributed network [50]. 
By allowing nodes to process and transmit messages 
faster in a distributed network, new blocks could be 
confirmed faster or they can be bigger, in an equally 
secure and decentralized blockchain network.
Other scalability solutions thus in most cases 
suggest the use of new innovative methods in the 
implementation of the blockchain network, while with 
such an approach they merely position the blockchain 
network properties elsewhere within the trilemma. 
Some of these solutions even allow for dynamic 
trilemma positioning. The point is that with the help of 
these scalability solutions, the trilemma surface can be 
filled with different properties of blockchain networks, 
and then users can choose the network that has the 
appropriate properties, depending on their needs. This 
work focuses on these types of scalability solutions 
that result in a change of trilemma properties. They 
can be divided into solutions that focus on the first layer 
design of blockchain (L1) and second layer solutions 
(L2) [51]. Table 3 presents the scalability solutions 
presented in the literature.
Table 3.  Scalability solutions in blockchain technology
Solution What How
Layer 
1
Block data TT, SS, RT
Block compression, data 
reduction
Consensus 
mechanism
TT, SS, RT
Improved consensus 
mechanism
Blockchain 
structure
TT Different database structure
Sharding TT, SS, RT Partitioning of the network
Layer 
2
Payment 
channels
T T, S S
Channels offload transactions 
from the blockchain
Sidechains TT, SS, RT
Additional blockchain network 
connected to the main network
Cross-chain TT, SS, RT
Multiple connected blockchain 
networks 
TT - transaction throughput, SS - storage size, RT - read throughput
L1 solutions include optimizing the process 
of block generation, consensus mechanisms, and 
blockchain structure. L2 solutions focus on relieving 
the main blockchain network by performing part of 
the transactions from the blockchain network or by 
transferring part of computationally demanding tasks 
to platforms that are not set on blockchain networks. 
First L1 solutions were optimizing the size of the 
block in the blockchain. Block compression and data 
reduction are used in this kind of scalability solution 
to increase transaction throughput. Segregated witness 
(SegWit) is one implementation of this solution, 
where transactions are split into two segments [52]. 
The unlocking signature is removed from the block 
creating more space for other transactions to be added 
to the same block. V arious solutions related to block 

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591 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
compression have been also proposed (e.g. Txilm 
[53]). The idea is to reduce some redundant data of a 
block that has been already stored in the Mempool of 
receivers [29].
L1 scalability solutions also optimize consensus 
mechanisms in the blockchain network. Many different 
mechanisms have been proposed and the main 
difference is usually regarding the process of candidate 
selection for block creation or in the process of block 
acceptance in the blockchain. The first consensus 
mechanism proposed in the bitcoin network was proof 
of work (PoW) [54]. Compared to pow, an example of 
alternative consensus mechanism proof of stake (PoS) 
avoids the computational overhead in the process of 
candidate selection for block creation. The basic idea 
of PoS is that nodes with more currencies in the system 
are less likely harm the system and therefore candidate 
selection is done based on the owned funds in the 
blockchain network [55]. Delegated PoS extends the 
idea, that stake is used for voting of delegation nodes 
that are fulfilling technological requirements (enough 
storage and computational power) [56]. Only delegated 
nodes are participating in transaction confirmation 
and only they are storing the whole blockchain. 
Therefore, communicational overhead in the network 
is reduced as well as storage requirements are justified. 
Practical Byzantine fault tolerance (PBFT) is another 
voting-based consensus mechanism [57]. It reduces 
the complexity of consensus to the polynomial level 
but requires more communication overhead. Due to 
the overhead, the PBFT works efficiently only when 
the number of nodes in the distributed network is 
small. For that reason, usually only selected nodes in 
the network are a part of the transaction confirmation 
process.
Transactions in a traditional blockchain network 
are written in blocks that are organized in a single 
chain. To enable concurrent block generation a 
different blockchain structure has been proposed. 
An example of a different blockchain structure is the 
directed acyclic graph (DAG). In the case of DAG, 
several blocks can be connected to a previous block. 
This results in a parallel creation of blocks which 
increases transaction throughput. As opposed to 
traditional blockchain technology where dedicated 
validators must exist to generate and order blocks, 
transaction ordering in DAG is done asynchronously 
by the account owner in charge of the ordering. A 
transaction is valid if the majority votes are in favor of 
that transaction. Storage limitations are not omitted by 
this kind of scalability solution [58].
The idea of the sharding scalability solution is 
to divide the blockchain network into several smaller 
networks that process transactions internally, however, 
all shards are connected in a larger network [7]. 
V alidators in each shard only need to process a small 
part of arriving transactions and different shards can 
process transactions in parallel. This results in higher 
transaction throughput, transaction confirmators are 
relieved of storing the whole blockchain and reading 
throughput is increased [59]. On the other side, cross-
shard transactions cause communication overhead and 
increase confirmation latency.
First L2 solutions appeared in the form of payment 
channels, in which a temporary off-chain trading 
channel is established. Any number of transactions 
between participants is performed via the private 
channel. If participants want to close the payment 
channel at any point, they can broadcast the most 
recent signed transaction message to the blockchain 
network to finalize their transfer of funds [51]. 
Therefore, multiple transactions can be executed on 
parallel channels, and only final states are written 
on the blockchain, which results in better transaction 
throughput and reduces storage requirements.
Another L2 solution is sidechains, which are 
separate blockchain networks that are pegged to the 
main blockchain (mainchain). Funds can be freely 
transferred from the mainchain to the sidechain 
and vice versa. The first concept of sidechains was 
proposed in 2014 [60]. The concept defined a general 
notion of a 2-way peg and described two operational 
modes of interactions between pegged chains – 
asynchronous and synchronous. The asynchronous 
mode assumes that the mainchain is agnostic to all 
sidechains, but it is necessary to rely on sidechain 
validators in the process of validating transfer 
transactions between chains. One of the sidechain 
solutions that enables asynchronous mode is Plasma 
[61] on the Ethereum network, which acts as the 
mainchain. ZK-Rollup is another technology, which 
enables the construction of sidechains. ZK-Rollups 
bundle hundreds of transfers on the sidechain into 
one transaction on the mainchain by employing the 
cryptographic tool of zero-knowledge proofs [62]. 
Synchronous mode assumes that the mainchain and 
sidechain are aware of each other’s existence and can 
directly verify the validity of transfer transactions 
between chains. This concept is further explored in 
cross-chain scalability solutions where two separate 
blockchain networks can be connected with cross-
chain transactions, however, both networks can operate 
even if the connection is cut between blockchains. One 
implementation of the cross-chain scalability solutions 
is the Polkadot ecosystem [63]. Both of the scalability 
solutions are improving transactional throughput, 

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592 R o žman,	N.	–	Corn,	M.	–	Šk ulj,	G.	–	Diaci,	J.	–	Po držaj,	P .
connection with the limitation of the scalability of 
blockchain technology. Further identification of 
whether the concept is designed specifically for any 
of the specific paradigms of smart manufacturing 
(e.g. cloud manufacturing) or just for general 
manufacturing systems was made. Then assessments 
were made to what extent scalability limitations and 
the proposed solution was described in each of the 
screened papers. The evaluation criteria divided 
literature into three categories.
1. category: Articles that merely identify the 
scalability limitations of the blockchain technology 
and suggest one of the general-purpose solutions as 
potential (citing other literature) in connection with 
the proposed concept.
2. category: Papers that in addition to stating the 
solution, also explain in more detail why this selection 
is justified according to the proposed concept. 
The implementation of a scalability solution in a 
manufacturing environment is more clearly defined 
(e.g. discussing how manufacturing-specific properties 
affect the integration of the scalability solution in the 
concept).
3. category: Literature that describes in detail 
how this solution would be included in the proposed 
concept, and may even further adapt the proposed 
scalability solution according to the proposed concept. 
The papers in the third category extend general-
purpose scalability solutions with manufacturing-
specific functionalities.
Table 4.  Literature classification according to the scalability 
solution
Scalability solutions In manufacturing
Layer 
1
Block data [66], [67]
Consensus 
mechanism
[68], [69], [70], [71], [72], [73], 
[74], [75], [67], [76]
Blockchain 
structure
[77], [78], [79], [80], [81], [53], 
[82]
Sharding [83], [84], [85]
Layer 
2
Payment channels [86], [87]
Sidechains [41], [88]
Cross-chain
[89], [90], [91], [92], [93], [94], 
[69], [70]
Off-chain [71], [95], [96], [97], [98], [99]
3.2  Selected Literature
According to the described methodology, 36 papers 
were found in the literature that addresses the problem 
of scalability in blockchain-supported manufacturing 
storage limitations, and read throughput by dividing 
blockchain network into smaller networks, which 
enables better information propagation. In addition, 
less strict requirements are posed to nodes who 
participate in the transaction confirmation process.
Off-chain solutions exploit the possibility to 
relay computational or storage tasks to capacities 
or networks that are not organized as a blockchain 
network. A multi-party computation network 
or distributed data storage network such as is 
InterPlanetary File System (IPFS) [64] can be 
employed as an off-chain solution. A distributed hash 
table (DHT) is used to store blockchain raw data on an 
off-chain data storage while the hash of the raw data is 
stored on the blockchain. There are different protocols, 
which define what part of the computation is done 
off-chain and how computation is organized. Truebit 
is an example of a system that outsources complex 
computing tasks to an off-chain market [65].
3  SCALABILITY SOLUTIONS IN BLOCKCHAIN-SUPPORTED 
MANUFACTURING
Given the features of blockchain-supported 
manufacturing systems, it seems inevitable that such 
large global systems will eventually encounter a 
limit to the scalability of blockchain technology. 
Scalability solutions enable blockchain technology 
to be a trust-ensuring solution among users in large-
scale systems. Due to the high volume of the presented 
scalability solutions in the literature, this paper 
focuses on the implementations of scalability solutions 
in the presented blockchain-supported manufacturing 
concepts in literature. This section discusses in detail 
how are general-purpose scalability solutions applied 
and modified for specific problems in the field of 
manufacturing.
3.1  Methodology
A literature search was conducted in the Google 
Scholar database, where a broad range of literature 
on blockchain-supported smart manufacturing can 
be identified. The searches were conducted using the 
following keywords in all possible combinations: 
blockchain, manufacture (ing), scalability, and 
scalability trilemma. Review articles were omitted 
from the analysis. The focus was on works that are 
aware of the limitations of scalability of blockchain 
technology (works that do not address scalability were 
omitted).
The selected literature was arranged according 
to the type of scalability solutions mentioned in 

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593 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
and suggests one of the scalability solutions. Table 
4 shows the reviewed literature, which is classified 
according to the type of considered scalability solution 
in connection with the manufacturing system. First 
L1 scalability solutions in blockchain-supported 
manufacturing are presented and analyzed. Most 
concepts are proposing the improvement of the 
consensus mechanism and blockchain structure, where 
mainly alternatives to the PoW consensus mechanism 
are modified for specific use in manufacturing, and 
the DAG structure in connection with IoT devices is 
discussed. Then L2 scalability solutions are presented 
and analyzed. Most authors are proposing off-chain 
solutions and cross-chain solutions where the IPFS 
approach is the most common one and different relay 
protocols for cross-chain interactions are second.
Fig. 4 shows a comparison between the emergence 
of literature in the field of scalable solutions in 
blockchain technology and the field of blockchain-
supported manufacturing over time. The figure shows 
that the literature in the field of blockchain-supported 
manufacturing does not exactly follow the trend in the 
field of blockchain technology. There can be several 
reasons for this. It may mean that researchers in the 
field of smart manufacturing are unaware of the 
importance of the scalability limitation of blockchain 
technology. One of the explanations may be that the 
solution used in the global manufacturing system has 
not yet been implemented and that this limitation of 
scalability has not yet been observed in the existing 
manufacturing system. In the case of Bitcoin, which 
is the world’s first blockchain network, scalability 
limitations have been identified after several years of 
operation.
Another possible explanation is that researchers 
in the field of smart manufacturing simply assume 
that the operation of blockchain technology will be 
perfected in the future and that such systems will come 
to life when the technology is ready for it. Until then 
they leave the solution to the scalability problem 
to researchers in the field of blockchain. However, 
because of the trilemma, we see that this limitation 
will always exist and it is necessary to explore what 
properties of the blockchain network regarding the 
trilemma are beneficial for the manufacturing system.
The reviewed literature was also divided into three 
categories according to the criteria described above. 
Fig. 5 shows the proportions of literature according 
to the assessed extent of addressing the problem. 
Almost half of the reviewed literature belongs to 
the second category, and the least literature belongs 
to the first category. The latter is surprising, as the 
word blockchain has been of interest to publications 
in recent years, and as a result, quite a bit of lower-
quality literature has been published. Thus, the 
literature prevails, in which the authors have a 
good understanding of the problem of scalability 
of blockchain technology and, consequently, the 
concepts are well described. However, most of the 
papers struggle to present manufacturing-specific 
solutions and are sometimes exploiting the concept 
of blockchain-supported manufacturing to present 
general-purpose scalability solutions. Fig. 5 also 
shows a graph showing how the extent of addressing 
the problem in the reviewed literature changes over 
time. There is a growing number of literature in 
time that describes the use of scalability solutions in 
blockchain-supported manufacturing in dept and with 
a lot of effort.
a) 
b) 
Fig. 4.  Emergence of literature in time; a) scalability solutions in 
blockchain-supported manufacturing; and b) scalability solutions in 
blockchain technology, adapted from [30]
3.3  Reviewed Solutions
In the next subsections, reviewed literature is presented 
in detail for each group of scalability solutions and 
manufacturing-specific properties are highlighted for 
each solution.

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a) 
b) 
Fig. 5.  a) Proportions of reviewed literature quality; and b) extent 
of addressing scalability problem in time 
3.3.1  Block Data
In the case of the L1 solutions related to block data 
optimization, two solutions related to manufacturing 
have been presented so far. The first proposes to 
upgrade the Merkle Patricia tree (MPT) specifically 
for IIoT devices in SMS [66]. The new approach 
optimizes the storage mode of the blockchain and 
accelerates the speed of the data query. The solution 
also supports thread-safe and parallel data operations, 
speed up block verification or construction, and 
further improves the transaction throughput. A tree 
structure called the concurrent Merkle-Patricia tree, 
which supports concurrent insertion and lock-free 
search, is a general scalability solution that can be 
applied to any blockchain-based system with many 
blockchain queries and high data volume scenarios. 
In the paper, the solution is used to expand traditional 
blockchain so that, via limited hash computing, it 
can rapidly locate the manufacturing equipment or 
products from intelligent manufacturing systems. The 
second concept is trying to optimize the blockchain 
settings (block size, block interval, selection of the 
block producers) using the deep reinforced learning 
(DRL) technique [67]. A deep reinforcement learning 
approach is adopted to handle the dynamic and large-
dimensional characteristics of the IIoT systems. For 
optimization, this solution takes into account the 
trade-off defined by the scalability trilemma. The 
authors proposed metrics that, in their opinion, should 
characterize the blockchain network’s scalability, 
decentralization, and security. The presented design 
of a modulable blockchain where properties of the 
blockchain are adjusted using DRL is well adapted to 
the dynamics of the manufacturing systems. However, 
the presented solution is general purpose and can be 
used also on other blockchain-based applications 
(e.g. any blockchain network can apply presented a 
framework to improve performance).
3.3.2  Consensus Mechanism
The above-described concept also optimizes the choice 
of consensus mechanism, so it is also included in the 
group of solutions that use the improved consensus 
mechanism as a scalability solution. Another solution 
has been presented in the literature that similarly 
optimizes the consensus algorithm of blockchain-
supported manufacturing using deep reinforcement 
learning [75]. This solution is only optimizing 
the selection process of the validator in terms of 
performance and the trilemma is not taken into account 
in terms of decentralization and security. The proposed 
deep reinforcement consensus mechanism is trained 
by a DRL training set and is adapted to the smart 
manufacturing business model. Applying the DRL-
optimized consensus mechanism to manufacturing 
based on an IoT environment generates simpler 
operations, faster response, and higher accuracy and 
security than the traditional consensus mechanism. 
However, the presented approach did not take into 
account any specific properties of manufacturing 
systems that would affect the design of the consensus 
mechanism. This approach can be used in any kind of 
blockchain-based application where optimization of 
blockchain scalability is necessary.
Most of the other concepts that include consensus 
mechanism solutions, suggest the use of an alternative 
consensus mechanism as implemented in the case of 
the bitcoin network PoW. A blockchain architecture 
that uses a dynamic PoW consensus with a block 
checkpoint mechanism for IIoT was proposed [72]. 
The dynamic PoW consensus mechanism upgrades 
the traditional PoW mechanism by introducing a 
sliding window algorithm, which defines how many 
preceding blocks need to be included in the newly 
mined block. The authors also discussed the security 
aspect of the proposed more scalable consensus 
mechanism. The consensus offers different mining 
difficulty levels for different transaction arrival rates 
of IIoT devices, while the checkpoints define how 
to generate the next block hash. Instead of constant 
mining difficulty, the solution introduces four different 
levels (Table 5) where each level is triggered for 
a certain rate of incoming communication traffic 
generated by the IIoT devices. The authors have shown 

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595 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
that for low arrival rates, the security is high, while 
for high arrival rates, the throughput is high, which 
makes the proposed protocol scalable to meet the high 
concurrency and security requirements of blockchain-
supported IoT manufacturing networks.
Table 5. Dif f iculty	 le v els	 in	 the	 DPo W	 co nsensus	 mec hanism,	
adapted from [72]
Tx arrival rate Difficulty Size (bits) Target hash
1 High Level 01 4 SHA256[0:1]
2 Medium-high Level 02 8 SHA256[0:2]
3 Medium-low Level 03 12 SHA256[0:3]
4 Low Level 04 16 SHA256[0:4]
The proposed concept of a decentralized 
manufacturing network addressed a comparison of 
performance between PoW and the proof-of-authority 
(PoA) consensus mechanisms [68]. The proposed PoA 
consensus mechanism implements a general-purpose 
PoA consensus mechanism named Clique. The 
authors emphasize that a different type of consensus 
mechanism is appropriate for different uses. They 
highlight the better decentralization and security 
features of the PoW mechanism compared to bad 
scalability as it is in line with the scalability trilemma. 
The authors discuss that the PoW mechanism would 
be more appropriate in the case of provenance tracking 
across a large supply chain, which requires higher 
trust and security. However, if a blockchain system 
is implemented for the verification of machine states 
such as states of the computer numerical control 
(CNC) machine, the PoA will be sufficient. The 
proposed solution does not provide any additional 
manufacturing-specific insights on how different 
consensus mechanism affects the integration into the 
manufacturing system.
A consortium PoA consensus mechanism is 
proposed in the case of the smart contract platform for 
the machine servitization blockchain network [71]. The 
proposed system of three-dimensional (3D) printing 
servitization is not public and it is a system provided 
by a known platform provider to known machine 
providers. Therefore, there is no need for a public 
network. The employed PoA consensus mechanism 
is a general PoA mechanism (not manufacturing 
specific) where only a set of known trusted entities 
is allowed to set up confirmation nodes. It supports 
arbitrary block times and sizes, increasing network 
performance and decreasing transaction latency. A 
free transaction environment is an important feature 
of PoA that is especially valuable for manufacturing 
use cases. This means that the gas price is set to zero, 
and the blockchain network imposes no transaction 
costs. Another benefit of free transactions is that the 
native cryptocurrency can now be easily used for 
manufacturing service payments. The currency can 
serve as a voucher (namely an Ethereum Request for 
Comment (ERC) token without smart contracts), and 
its value can be mapped to FIA T at a fixed price. PoA 
consensus mechanism was also selected in the case 
of the proposed architecture for fast certification of 
manufacturing data, compatible with current industrial 
landscapes [76]. A general-purpose PoA consensus 
mechanism is employed due to the permissioned 
nature of the certification system. There are two types 
of nodes, namely administrator nodes and mining 
nodes. Administrator nodes are reserved for the 
sovereign entity that fundamentally holds legislative 
power over the network. Mining nodes are responsible 
for creating (mine) blocks and can be deployed across 
multiple cells in a production line, different production 
lines, departments, factories, or even organizations.
A consensus mechanism that supports a 
permissioned blockchain network was proposed 
to satisfy scalability requirements regarding the 
ISA95 compliance of SMS [69]. The ISA95-CTS 
and SMS ecosystem constitute a broad scope of 
devices and systems with varying computational 
capabilities where scalability becomes a crucial 
design requirement. The reference architecture design 
specification requires 300 ms maximum latency 
with a throughput of 6000 to 8000 transactions per 
second (TPS). These requirements can be met with a 
permissioned blockchain network employing a trivial 
consensus protocol Raft, which is a general-purpose 
blockchain consensus mechanism. A similar proposal 
of permissioned blockchain architecture was presented 
regarding the manufacturing blockchain of things 
concept. The authors have suggested the use of a 
simple crash fault-tolerant (CFT) consensus protocol 
to achieve higher throughput and lower latency [74]. 
The proposed CFT protocol is also a general-purpose 
consensus mechanism that can be employed in 
blockchain networks for arbitrary use cases.
Two concepts in the literature suggest the use 
of the practical Byzantine fault tolerance (PBFT) 
consensus mechanism, stating that it has better 
performance than the traditional blockchain network 
(e.g. Ethereum) [70], and [73]. However, the proposed 
concepts are not considering the deterioration of 
privacy and decentralization in the system according to 
the scalability trilemma. PBFT consensus mechanism 
is a general-purpose blockchain consensus mechanism 
that is already employed in other general blockchain 
networks (e.g. Hyperledger). None of the concepts are 

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extending the solution according to the requirements 
of manufacturing systems. For example, the authors 
recognize that stability of the throughput (fluctuation) 
is one of the problems of the PBFT mechanism, which 
can severely affect the processes in manufacturing 
planning that require synchronization. However, the 
proposed solutions do not provide any solution which 
would mitigate this manufacturing-specific problem.
3.3.3  Blockchain Structure
All the concepts that implement blockchain structure 
scalability solutions with blockchain-supported 
manufacturing are based on the use of DAG structure, 
with the implementation on the IOTA network being 
the most prominent. In four cases [77], [79], [53], 
and [78], this technology is only mentioned as a way 
to improve the scalability limitations of blockchain 
technology in connection with manufacturing. In all 
four concepts of blockchain-supported manufacturing 
is a physical layer directly connected to the blockchain 
network and the authors are expecting a high number 
of activities and interactions between entities that 
would require high transactional throughput. One 
of the concepts presents the implementation of the 
proposed architecture using STM32 IoT devices. The 
scalability evaluation of the implemented system 
suggests improved scalability and that the proposed 
architecture is feasible in SMS [82]. However, all 
of the above concepts do not provide any specific 
details on how a general-purpose DAG blockchain 
structure would be implemented in connection with 
the manufacturing system.
Some authors have described in more detail 
the implementation of the masked authentication 
messaging (MAM) protocol defined by the IOT A 
network in the case of communication between 
IIoT devices in SMS [80] and [81]. In the case of 
blockchain-supported cloud manufacturing the 
MAM, communication protocol is employed to realize 
flexible data access management [81]. The restricted 
mode of privacy and encryption is proposed for 
data sharing to control the visibility and access of a 
channel. Besides connections between entities in form 
of contracts (smart contracts), the DAG blockchain 
structure provides an infrastructure for an additional 
data layer. The data layer provides information for 
participants in the system with variable frequencies 
and does not require previous agreements or data 
access. MAM communication channel can regularly 
deliver information about relevant key performance 
indicators of cloud manufacturing, by the previously 
required constraints. In the system architecture 
of smart factories, which requires real-time data 
collection, the DAG structured blockchain is employed 
for a distributed traceability system and the MAM 
protocol enables a communication channel [79]. Each 
device in the smart factory is capable of publishing its 
messages and broadcasting them to other machines. 
MAM protocol is modified to enable different types 
of complex tree-like workflows of manufacturing 
processes. The modified protocol enables channels 
to merge and backtrace paths, which is important 
as multiple assembly lines merge leads to channel 
switching issues and source traceback is important for 
traceability.
In none of the above describes concepts the 
authors do not consider the limitation of the trilemma. 
Security problems and poor decentralization of the 
IOTA network [100] have already been identified in the 
literature, which may suggest that this technology is 
not optimal in the case of global SMS.
3.3.4  Sharding
Concepts that include sharding as a scalability 
solution describe the implementation of the sharding 
mechanism in SMS in much more detail. One of 
the solutions is not implementing a strict sharding 
mechanism but scalability is improved in a similar 
matter. A clustering algorithm has been used to group 
the overlay network participants into clusters and 
that results in low latency, and higher throughput 
[83]. The participating nodes of the clusters also 
select a cluster header as well as a co-leader of 
the cluster in the case the cluster header becomes 
malicious or leaves the network due to a sudden loss 
of connectivity. These cluster heads are responsible 
to maintain and manage the blockchain-supported 
overlay network of the manufacturing system. The 
industrial production line is equipped with randomly 
placed IIoT-enabled machines and devices. Such 
an unstructured environment creates overlapping 
network topology, which is not suitable. The proposed 
algorithm in a distributed manner discovers network 
nodes by exploiting local network topology knowledge 
and forms clusters in random geometric graphs. This 
facilitates covering the whole network with a minimum 
number of nodes and further reduces processing and 
packet overhead on IoT devices.
Another concept proposes a sharding hashgraph 
consensus mechanism (Fig. 6) and introduces a 
node evaluation mechanism based on the state of 
the node, which is applied to divide a large number 
of nodes into many shards dynamically [85]. This 
mechanism comprehensively considers the node’s 

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597 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
geographic location, credit score, network status, and 
CPU resources and divides all nodes in the network 
into multiple shards. The nodes in each shard are 
geographically close, the network status is generally 
good, and the credit rating is similar. Each full node 
is responsible for a group of lightweight IIoT devices 
(sensor network) in a certain area, receiving all data 
uploaded by the sensor network and storing these 
data. The simulation results demonstrate that the 
proposed algorithm demonstrates better scalability 
performance in comparison to the PBFT consensus 
mechanism. Both of the described sharding solutions 
are considering the low capabilities of the IIoT 
devices in manufacturing networks, which are the end 
users in the blockchain network, and are proposing 
solutions that would increase the scalability of the 
blockchain network specific for blockchain-supported 
manufacturing.
Fig. 6.  The IIoT data management system architecture based on 
blockchain and sharding, adapted from [85]
However, the effectiveness of sharding is still 
challenging due to the uneven distribution of malicious 
nodes, Fig. 7. In IIoT user data and computing tasks 
are generally transferred to edge nodes (EN) from 
user equipment (UE) using distributed units (DU). A 
UE is set in a harsh environment for a long time and 
it is impossible to ensure that the task to be migrated 
for each device has not been tampered with. The 
blockchain technology approach prevents tampering 
with computing tasks across the entire network. The 
blockchain nodes are composed of all UE and DU. 
The UE layer consists of ordinary nodes, and the DU 
layer contains consensus nodes. Potential malicious 
consensus nodes can tamper with the data, causing 
excessive computing overhead and even paralysis of 
the IIoT.
The authors in their paper assume that each 
attacked UE will send the wrong calculation task to 
the DU. Therefore, a many-objective optimization 
algorithm based on the dynamic reward and penalty 
mechanism has been proposed to optimize the shard 
validation validity model [84]. The dynamic reward 
and penalty mechanism dynamically combine the 
diversity function and the convergence function to 
increase the selection pressure and make the population 
closer to the real Pareto frontier (PF). At the same 
time, the weights of the two functions are dynamically 
set to classify individuals in the population, thereby 
making individuals with different performances evolve 
iteratively. The proposed optimization algorithm is 
general-purpose and can be applied to any sharding 
solution in the general-purpose blockchain network. 
However, the possibility for malicious nodes seems to 
be greater in the case of IIoT networks due to a large 
number of devices with limited capabilities and the 
high impact (the whole manufacturing system can be 
obstructed) of such malicious attacks. The presented 
sharding solutions mention the limitation of the 
scalability trilemma but do not provide any discussion 
or analysis of how the proposed concepts would affect 
the relationship between scalability, decentralization, 
and security in blockchain-supported manufacturing.
Fig. 7.  Sharding scheme leads to the aggregation of malicious 
nodes, adapted from [84]
3.3.5  Payment Channels
Payment channels in blockchain-supported 
manufacturing are poorly addressed. Only in two 
concepts, are payment channels proposed as one of 
the possible scalability solutions. In the proposed 
blockchain protocol for manufacturing and supply 
chain management of integrated circuits, the authors 
have recognized the scalability limitations of 
blockchain technology and they have also proposed 

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possible scalability solutions to be implemented in the 
concept [86]. The implementation of the state channel 
was described in three parts. First, a part of the 
blockchain would be locked and updates can be made 
if a specific set of participants agrees to it. Second, the 
participants update themselves by constructing and 
signing transactions without submitting them to the 
blockchain network. Third, participants would submit 
the final state to the blockchain network after the 
interactions are finished to close the channel.
Payment channels in the form of a lightning 
network were proposed similarly in the blockchain 
model for industrial internet [87]. For low transaction 
speed, a lightning network could process high-
frequency but small-sum transactions in an off-chain 
way. However, none of the above works provides any 
kind of detailed explanation of how this scalability 
solution would be connected to the manufacturing 
systems. The lack of interest in this L2 solution is 
probably because other L2 solutions provide better 
functionality and interoperability of the system. Both 
of the papers addressing payment channels were written 
in 2018 when other L2 solutions were still being 
explored and developed.
3.3.6  Sidechains
In contrast to the payment channels, the literature 
discusses the L2 solution of sidechains and 
blockchain-supported manufacturing in more detail. 
Both of the concepts are proposed for a specific type 
of SMS, namely Cognitive manufacturing and Shared 
manufacturing. The first paper proposes a topic mining 
process in blockchain-network-based cognitive 
manufacturing [88]. The proposed method exploits 
the highly universal Fourier transform algorithm to 
analyze the context information of equipment and 
human body motion based on a variety of sensor input 
information in the cognitive manufacturing process. 
Because of its primitive management, a cognitive 
manufacturing process can have problems including the 
absence of efficient statistical information, negligence 
of supervision, and inconsistency of physical and 
soft data. The conventional physical Cognitive 
manufacturing system has unclear information in 
the manufacturing process, whereas the proposed 
cognitive manufacturing process supports end-to-
end trace based on the transaction data saved in the 
blockchain in order to prevent data loss in each step. 
The blockchain-supported cognitive manufacturing 
process exploits the information exchange of the 
data collected in real-time to analyze a variety of 
data related to the traceability system, extension 
infrastructure in each base, and worker’s work system. 
Such an approach in manufacturing results in the 
collection of massive amounts of data. Because of 
the structural problem of the blockchain (scalability 
limitations), it is difficult to include massive data on 
the general-purpose blockchain. Therefore, sidechains 
are used to store a large amount of data collected by 
smart devices in the system. The mapping between 
the specific data on the sidechain and the mainchain 
is done by writing the hash of the block on the 
mainchain. However, the proposed solution employs 
general-purpose sidechains and does not provide any 
specific reason why another scalability solution is 
not viable to provide better scalability in the case of 
blockchain-supported cognitive manufacturing.
Cross-chain 
transfer
Prosumer
PSS
CSS
RSS
user
Demand
ID, funds
Supply Block n+2 Block n+1 Block n
Mainchain
Block n+2 Block n+1 Block n
Sidechain
Physical 
layer
Cyber 
layer
Decision 
layer
Fig. 8.  Sidechain solution in blockchain-supported manufacturing, 
adapted from [42]
The second paper presents a scalable framework 
for blockchain-supported Shared manufacturing (Fig. 
8) that preserves the transparency and immutability 
characteristics of transaction records, which is critical 
to building trust between entities in blockchain-
supported systems [41]. The concept proposes that 
authentications of the manufacturing resources in 
the system are done on the mainchain and all the 
interactions between providers and consumers of the 
manufacturing services are done on the sidechains. The 
authors further discuss what cross-chain technology 
should be used to relay data from sidechains to the 
mainchain, and a hybrid solution is proposed. It allows 
a more configurable setup of sidechain networks, 

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599 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
which means that it can provide greater scalability 
and interoperability according to users’ needs. When 
users determine that they need a blockchain network 
with different properties, they can open a new network 
(sidechain) with custom settings or connect an existing 
network with a preferred setting. There are two types 
of connection between blockchains in the proposed 
concept, namely information and financial. Financial 
is provided by the general-purpose sidechains 
scalability solutions. The information connection is 
provided by the appropriate implementation of the 
proposed protocol using smart contracts both on the 
sidechains and on the mainchain. Upon entering the 
system on the mainchain, prosumers create a digital 
identity with which they then carry out transactions 
on various blockchain networks. Thus, prosumers 
can track other prosumers throughout the tree of 
sidechains and obtain information about their business 
history, and use this information when deciding to do 
business with a particular prosumer. The authors also 
discuss that sidechain networks would be organized 
by manufacturers in the system who have computer 
hardware and resources to support this kind of system 
that benefits them. They compare implementations of 
the proposed framework on the main public blockchain 
network and sidechain network, which results in better 
scalability by the sidechain solution.
3.3.7  Cross-Chain
Cross-chain scalability solutions present variable 
cross-chain protocols to relay data between two 
blockchain networks and many of them were connected 
to blockchain-supported manufacturing. A concept for 
enabling the traceability of manufacturing processes 
was presented, where manufacturing products are 
represented by non-fungible tokens on the blockchain 
network [89]. In order to reduce the overhead involved, 
a lightweight contract is implemented as an alternative 
to the more general ERC-721 compliant version. The 
authors are further aware of the scalability limitations 
of blockchain technology, therefore, they propose 
the use of scalability solutions. The tests on the 
implemented system showed that varying batch sizes 
of manufacturing products influence the system’s 
scalability in terms of throughput, ledger size, and 
potential gas costs. Consequently, small batch sizes, 
as required for tracing single goods, negatively affect 
the system’s performance. The authors propose cross-
chain solutions over L1 solutions because cross-
chain solutions provide better interoperability, which 
is required in the case of the proposed concept. The 
authors argue that the intersection of industries 
and bidirectional dependencies requires different 
environments that should be connected. However, 
only general-purpose existing cross-chain scalability 
solutions are proposed, without further explanation of 
how the proposed concept should be integrated with 
cross-chain solutions.
Manual asset exchange as a cross-chain protocol 
is selected in the concept of the infrastructure of 
decentralized collaborative manufacturing [93]. To 
satisfy high-performance requirements, the matching 
layer relies on atomic swaps to enable automatic 
pricing. The protocol allows the trade of arbitrary 
crypto-assets, like cryptocurrencies (value tokens) and 
ERC-20 tokens (capacity tokens) through different 
blockchain networks. Free capacities of manufacturing 
machines in the system correspond to a number of 
capacity tokens on the token layer. However, the 
cross-chain scalability solution in this concept is an 
existing general-purpose solution for transferring 
funds over any general-purpose blockchain network. 
No additional insights are provided on how 
manufacturing-specific properties of collaborative 
manufacturing affect the integration of the proposed 
scalability solution into the system.
Fig. 9.  Cr o ss-c hain	arc hit ecture	f o r	manufacturing	supply	c hain	
system, adapted from [90]
Another concept presents a design scheme of an 
integrated platform for information exchange services 
provided by participants in a manufacturing supply 
chain based on blockchain technology [90]. In order 
to reduce the scalability limitations of blockchain 
technology and due to the requirements of the global 
manufacturing supply chain, cross-chain architecture 
is proposed. An interaction chain is defined as the 
coordinator in the system and the notary scheme 
is selected as the cross-chain protocol between 
different blockchain networks. An interaction chain 
is managed by authorities and is organized like other 
blockchain networks. The nodes in the interaction 
chain are notarization nodes that are ensuring cross-

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chain communication and global consensus between 
different networks (Fig. 9). There are four types of 
nodes in the system. Supply chain nodes are taking 
care of executing supply chain services. Gateway 
nodes are in charge of collecting cross-chain 
transactions and transmitting them to the interaction 
chain to be verified. Notarization nodes are the nodes 
implementing notary schemes to validate cross-chain 
transactions. Supervision nodes are operated by 
the authorities and are in charge of monitoring the 
notarization nodes.
A notary scheme is the selected cross-chain 
protocol also in the algorithm for the blockchain-
supported system on multi-chain storage for 
cyber, physical, and social (CPS) under edge cloud 
computing [91]. Blockchain-supported CPS systems 
are generating a huge amount of similar data that 
occupies the storage of the devices in the system. In 
addition, the traditional blockchain approach without 
scalability solutions leads to the mismatch between the 
communication speed of nodes and the requirements 
of high concurrency and high response speed in 
CPS. Data storage and parallel processing become 
the key factors that restrict system performance. The 
algorithm divides nodes of the system into separate 
blockchain networks according to the relationship of 
communication tightness. A partitioning algorithm 
based on node community clustering minimizes time 
for cross-chain communication, resulting in improved 
speed of data processing and reduced communication 
load in the system. A similar approach can be 
employed in other blockchain-supported applications 
where storage and communication limitations are the 
main problem.
The above-presented concepts are well 
presented, however, none of them are discussing 
how the improved scalability of the system will 
affect the property of security and decentralization. 
The trilemma is discussed in a proposed cross-chain 
protocol of interoperable blockchains for collaborative 
manufacturing [92]. Existing cross-chain protocols are 
being extended using a trusted execution environment 
to increase the security of the solution. A relay 
scheme is proposed as a cross-chain communication 
technology. The authors have shown that negligible 
additional communicational overhead emerges due 
to the cross-chain interaction, however, security and 
interoperability are increased.
3.3.8  Off-Chain
Most blockchain-supported manufacturing concepts 
involve huge amounts of captured data and cannot 
be stored all on one network, so data storage on 
a distributed storage system that is not part of the 
blockchain (off-chain) appears to be the main solution 
to this problem. The proposed solutions define which 
data is written on the blockchain and which is off-chain. 
In the case of the industrial blockchain-supported 
framework for product lifecycle management (PLM) 
in Industry 4.0, the authors suggest that some raw data 
is written off-chain, whereas the hash of raw data is on 
the blockchain [70]. PLM aims to seamlessly manage 
all product information, and knowledge generated 
throughout the product lifecycle for achieving business 
competitiveness. The information of PLM is difficult to 
be integrated and shared among the cooperating parties 
due to the amount of data and privacy reasons. The 
authors propose that design schemes and certificates 
are written off-chain, however, manufacturing quality 
information, recall data, and supply chain traceability 
data are written on the blockchain (Table 6). Off-chain 
data is stored in the cloud storage environment, which 
is employed also for data validation, data cleaning, and 
data broadcasting.
A similar way of recording generated data is 
presented in the case of 3D printing as a service 
in a decentralized manufacturing concept where 
important data about the service is written on the 
blockchain network, and additional digital content, 
such as stereolithography (STL) files, is stored off-
chain [71]. The off-chain cloud storage is an add-on 
integrated into the on-chain logic. The authors define 
the data and event models for a hybrid on-chain and 
off-chain decentralized application. On blockchain are 
stored JavaScript object notation (JSON) structures 
with hash values, uniform resource locators (URLs), 
and metadata. URLs are pointing to the 3D printing 
specification in STL files that are stored off-chain.
Table 6.  Data	 in	 the	 indus trial	 blo c kc hain-suppo r t ed	 frame w o r k	 f o r	
PLM, adapted from [70]
Data type Privacy Amount On or off-chain
Design schemes High Medium Off-chain
Quality information Medium High On-chain
Logistics traceability Low High On-chain
Recall data Low High On-chain
Contracts Null Medium On-chain
Certificates Low Low Off-chain
The data sharing framework for IIoT was 
presented in the literature, where the authors propose 
an off-chain procedure for participants to compress 
and encrypt product data before being submitted to 
the blockchain [99]. The concept proposes two types 

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601 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
of transactions named point transactions and data 
transactions. Point transactions are indicating the 
place of the written data on the off-chain and data 
transactions store encrypted data on the blockchain. 
When a product id is processed through a path of 
participants, each of them transfers its product record to 
a dedicated participant for compression. The dedicated 
participant then encrypts the compressed product data 
with a policy and submits it to the blockchain. In the 
off-chain procedure, participants transferring product 
records also submit point transactions to store the off-
chain storage address of the data of id. At the end of 
the procedure, the dedicated participant submits a data 
transaction to carry the encrypted compressed data of 
the id. To access the data of the id, a data user can send 
a request to the blockchain. If a point transaction is 
returned, the user extracts the off-chain address from 
it and interrogates a certain participant according to 
the address. The participant encrypts the data of the 
id with a policy and returns the ciphertext. The user 
then decrypts the ciphertext according to the policy 
to acquire the data of the id. If a data transaction is 
returned, the user directly extracts the encrypted 
product data from it and decrypts it according to the 
policy to acquire the data of id. The evaluation results 
show that compared with the baseline approach, the 
proposed concept achieves a 4 to 9 times improvement 
in storage efficiency and a 5 to 20 times improvement 
in data access efficiency, respectively.
Several solutions suggest using the inter-planetary 
file system (IPFS) as a distributed storage system. In 
the case of traceability and management in additive 
manufacturing systems, IPFS is used to store design 
files, IoT device records and additional product 
specifications [94]. When a customer submits an 
order, the smart contract connects to the product 
manufacturer and the 3D printing workshop. Once the 
product manufacturer and the 3D printing workshop 
confirm accepting the order, the product designer 
is in a heterogeneous environment with many 
actors. Thereby, the IPFS-based enterprise file share 
should be protected from unauthorized users. The 
authors propose OAuth, Security Assertion Markup 
Language, Kerberos-based single sign-on (SSO), and 
authentication schemas to preserve privacy.
Excessive data gathered to satisfy the requirements 
of the ISA95 standard must also be relayed through 
the IPFS to reduce the amount of data being written 
on the blockchain [69]. V arious actors in the system 
can directly read and write to IPFS via P2P network 
protocols, given that relevant access rights are granted 
(Fig. 10). The suggested policy can define thresholds, 
and if a file or transaction content is above the limit, 
then the blockchain operating system makes the actor 
write the file content to IPFS. Next, the file hash 
(acting as the pointer to the original file) is generated 
and inserted as a new block content to the blockchain. 
When an actor needs to access the file, first gets the 
file address on IPFS from the blockchain, and then 
the actor accesses the address location to read the file 
content. The reference architecture attempts to address 
location restrictions by preserving the sensitive data 
on IPFS-based network storage that is geographically 
situated as per regulatory requirements. However, 
to add an extra layer of protection, individual data 
elements are encrypted, and the hashes of the files 
are distributed through the ledger. Large files are 
distributed through IPFS over the P2P network. IPFS 
does not dictate any access control by default. The 
ISA95 enterprise uploads the digital design on the 
IPFS, and the hash of the file is transmitted to the 3D 
printing workshop. All interactions and transactions 
between the stakeholders are stored in the blockchain 
ledger. Due to storage limitations and size restrictions, 
larger files are stored in IPFS and their hash is sent 
to respective participants and stored in the blockchain 
ledger. Once printing is completed, all IoT devices 
and camera records will be uploaded to the IPFS 
and hashed in the blockchain ledger. The hash for the 
control measures recorded during the printing process 
is transmitted to the Attestation and Certification 
Authority accessed via IPFS to verify quality control 
measures.
User
IPFS
Cloud ERP HMI PLC Sensor
Blockchain
Smart 
contract
Agent
File hash
Encryption
* ERP – Enterprise Resource Planning, HMI – Human Machine 
Interface, PLC – Programmable Logic  Controller
Fig. 10.  On-chain and off-chain transaction encryption,  
adapted from [70]
In the case of the blockchain-based service 
architecture for Cloud manufacturing, the authors 
proposed to relay the dynamic elliptic curve certificate 
data on a distributed storage system (IPFS) to increase 

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the storage scalability of the proposed architecture 
[95]. Dynamic certificates and elliptic curve integrated 
encryption schemes are proposed to enable blockchain-
based security services for trust establishment. Diffie–
Hellman key exchange is used for the establishment 
of a symmetric key between IoT–Fog–Cloud channel 
to encrypt the manufacturing-related message traffic. 
Non-interactive zero-knowledge proofs are used for 
verification in the security service to ensure anonymity 
and unlinkability on dynamic identities stored in the 
ledger. Additional data used for encryption procedures 
is offloaded to the extended storage off-chain. The 
extended storage record is linked to the ledger record 
with the extended storage key, which is the hash of the 
multiple data elements. The data consists of the key of 
storage, certificate, counter data, auxiliary data for 
private key generation, curve point of zero-knowledge 
proof, and data set. The experiment showed that the 
blockchain storage utilization is significantly reduced 
in the proposed architecture compared to storing all 
data on the blockchain.
To reduce the load and delay of the network, 
the blockchain-based platform for IIoT is designed 
as a light-weighted network architecture (Fig. 11) 
consisting of an on-chain network and an off-chain 
network [96]. Under the conditions of the IoT, the 
computing power of each intelligent device is very 
limited. Compared with the traditional blockchain 
mining nodes, the hash computing capability is even 
less than one-thousandth of the graphics processing 
unit (GPU) system. The off-chain network handles 
problems that cannot be solved by blockchain 
technology, such as storage and complex data 
processing. The off-chain network has a decentralized 
off-chain database, namely DHT, which can be 
accessed by the blockchain. The data is encrypted 
in the blockchain and stored in the DHT, the access 
control protocol is written on the blockchain to ensure 
security, and the off-chain network provides an API 
interface to read the data in the DHT. The proposed 
off-chain network further implements a multi-party 
computation protocol, which allows multiple nodes to 
perform computing tasks on a common problem in a 
secure way. In the use case of the proposed concept 
for manufacturing equipment data sharing, the authors 
discuss that the shared data is organized into a 
standardized readable, and writable data format by big 
data and AI technology on the off-chain network. The 
shared data is also stored in the off-chain database, 
while a summary index is generated and stored in the 
on-chain network. However, the proposed concept 
is employing general-purpose blockchain solutions 
and can be implemented on any other blockchain 
application. The authors do not provide any additional 
insights into what manufacturing-specific data is 
written on or off-chain.
In the case of the architecture for secure 
management of manufacturing data, the off-chain data 
is stored on the cloud storage system [97]. For the high 
volume of shared data, the authors present a hybrid 
approach where a token and conditions on when the 
token can be used are written on the blockchain, and 
when the conditions are met the token grants access 
to particular data on cloud storage. The data itself can 
go on low-cost cloud-based Write Once, Read Many 
(WORM) storage owned by the manufacturer, while 
a token corresponding to the data can be included on 
the blockchain. The token leaks minimal information 
about the manufacturing process, allaying potential 
confidentiality concerns. Upon conditions that can 
also be spelled out in the blockchain, certain parties 
have the contractual right to present the token to the 
manufacturer and be given access to the data.
Fig. 11.  Light-weighted blockchain-based network architecture for 
IIoT, adapted from [96]
The WORM layer includes a blockchain element 
because even if data has been placed on WORM 
storage, there is no guarantee that anyone can find it. 
The WORM-blockchain combination overcomes this 
problem by placing the relevant lookup information on 
the blockchain. The authors, however, do not discuss 
in detail what kind of manufacturing data is stored 
off-chain and do not elaborate on how tokens would be 
implemented (using cryptography or any other tools).
Another concept of blockchain architecture for 
Cloud manufacturing proposes that a large volume 
of manufacturing data is stored in decentralized 
and immutable bid data storage platform like the 
BigchainDB [98]. Compared to IPFS and cloud 
platforms proposed decentralized storage is including 
some parts of blockchain technology such as the 

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603 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
consensus mechanism, however it omits safety 
measures like full replication of the database in each 
node to provide better scalability. Communication 
between layers L1 and L2 is established by a new 
addition to the client middleware as the BigchainDB 
interface layer. This layer is primarily a middleware 
software module that resides within the client 
middleware software architecture and houses event 
subscribers that subscribe to specific transaction 
events. These transaction events are emitted by the 
oracles designed as a part of the smart contracts. Once 
the manufacturing service is executed an ERC-721 
token is created on the L1 network. The oracles on the 
issuance of these tokens in turn emit their own events 
which trigger subscribers in the BigchainDB layer to 
complete the collection of data. The BigchainDB layer 
collects a large volume of metadata on manufacturing 
services. The data includes a cryptographically hashed 
signature of the design file of the final part, detailed 
dimensional metadata of the part, and necessary 
information about both the client and the CMaaS 
platform in terms of their Ethereum identities i.e., 
wallet addresses. Most of this information quite 
naturally would involve data of different types and 
precision. Due to the restrictive nature of the Ethereum 
ecosystem, there is no default support for many 
complex data types like variable length strings which 
have to be used to record modalities like product name 
or description. BigchainDB as an off-chain solution 
provides better scalability and enables the storage of 
complex data types.
3.4  Comparison of the Literature
It is noticeable from the presented literature that a 
few more concepts propose L1 solutions than L2 and 
some authors propose a combination of both types of 
solutions (Fig. 12a). In the case of L1 solutions, the 
concepts in the literature are mainly proposing the 
use of scalability solutions that change the consensus 
mechanism and solutions that change the blockchain 
structure. For the consensus scalability solution, 
this is mainly because using alternative consensus 
mechanisms (e.g. PBFT) can implement a more 
scalable network, which usually has the properties 
of a consortium or permissioned network and that 
corresponds to closed types of SMS organization. 
The authors thus justify the change of properties in 
trilemma by saying that the manufacturing world 
is more closed than the financial one, and there are 
already proven implementations of these mechanisms, 
such as Hyperledger. The blockchain structure 
solutions are mainly presented in concepts that use 
the specifically designed blockchain network IOTA, 
which targets the integration of IoT devices into the 
blockchain network, and there are many of them in the 
case of SMS.
a) 
b) 
Fig. 12.  a) Proportions of proposed L1 and L2 solutions in 
literature; and b) proportions of specific scalability solutions 
proposed in the literature
In the case of L2 solutions, a group of concepts 
stands out that suggests the use of off-chain solutions. 
As already described, most of these concepts 
want to transfer the data records generated by the 
manufacturing system from the blockchain to parallel 
storage infrastructure and consequently relieve 
the blockchain network. Given the amount of data 
generated by SMS, it seems reasonable that not all 
data is stored on such an “energy-intensive” network, 
but it should be noted that more scalable alternatives 
also mean less security of stored data. The reason for 
the highest number of concepts implementing off-
chain solutions is that cryptography enables a simple 
connection between data written off-chain and on the 
blockchain (hash mappings) and that there already 
exist several data storage systems that can provide 
this kind of support (e.g. IPFS). Then, according 
to the number of solutions, the use of cross-chain 
solutions follows. Concepts employing cross-chain 
solutions are similar to the concepts that suggest the 
use of sidechain technology. The reason for a bigger 
number of concepts proposing cross-chain solutions 
instead of sidechain solutions is because cross-chain 
enables better modularity of the blockchain networks 
and additional opportunities to build blockchain 
infrastructure (e.g. existing blockchain networks can 
connect or disconnect from each other). The main 

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difference regarding user experience is where the 
identity of an individual system user is created. With 
sidechain solutions, this happens on the mainchain, 
and then with that identity, users access other 
sidechains. With cross-chain solutions, users can join 
the system on any chain.
In the selected literature, we observed whether 
the proposed concept is specific to any of the Smart 
Manufacturing paradigms. Different concepts of 
organizing manufacturing systems provide for a 
different number of interactions between entities in 
the system, which means that blockchain network 
scalability can be much more important for some 
concepts than for others. Fig. 13 shows how SMSs 
are defined in the reviewed literature. In most cases, 
the authors do not envisage a specific concept of 
organizing SMS. Only about a quarter of the posts 
deal with specific examples of SMS, these relate to 
CloudMfg, Smart Manufacturing supply chain, and 
other SMS (e.g. SharedMfg). Fig. 13 also shows a 
comparison between the choice of L1 or L2 solutions 
in the case of general SMS and specific SMS. In 
the case of any SMS system, the authors opt for L1 
solutions, while for specific SMS the decision for L2 
solutions prevails.
a) 
b) 
Fig. 13.  a) Proportions of different SMS types implementing 
scalability solutions in literature; and b) comparison between 
general and specific SMS concepts regarding the L1 and L2 
solutions in the literature
4  FUTURE DIRECTIONS AND OPEN ISSUES
The presented literature proposes different concepts 
and solutions however the authors acknowledge that 
studies have limitations and that there exists a research 
gap for future work. Based on the reviewed literature 
the following open research questions (RQ) are 
highlighted:
•	 RQ1: Do proposed solutions meet the 
performance requirements of the manufacturing 
systems?
•	 RQ2: What are the barriers to the real-world 
implementation and adoption of proposed 
solutions in a global manufacturing system?
•	 RQ3: How do scalability limitations of 
blockchain technology affect the performance of 
a manufacturing system?
•	 RQ4: How increased scalability of the system 
affect decentralization and security and how 
reduced trust affects the manufacturing systems?
RQ1 explanation: According to the reviewed 
literature on blockchain-supported manufacturing 
regarding the scalability of blockchain technology, 
most of the authors assume that the scalability of 
blockchain technology will be a problem when 
connecting to large manufacturing systems. All of 
the proposed solutions increase scalability of the 
blockchain-supported manufacturing, however, a 
few of the authors are trying to evaluate if such 
performance results are sufficient for the requirements 
of global manufacturing systems. The scalability 
and compatibility of the proposed platform should 
be further verified and evaluated in a real business 
environment with more nodes [70]. However, it 
is difficult to assess the number of nodes that are 
participating in the global manufacturing system. 
Furthermore, the capabilities of IIoT devices in 
manufacturing systems are improving. Evaluation of 
the concepts in the future should include additional 
devices with specialized hardware designed for 
blockchain-supported systems [82]. In the case of 
optimization of the consensus mechanism selection 
concept [75], the author proposes that in the future 
new consensus mechanism should be developed and 
that it should be adapted for different properties of 
manufacturing systems. Consequently, coordination 
with manufacturing companies is proposed to 
obtain more realistic performance requirements. 
Another challenge in answering this question is a 
representative assessment of different manufacturing 
systems’ performance requirements. The diversity 
of existing systems seems to present a plethora of 
requirements. However, studies that would address 
this question could potentially define the necessary 
levels of blockchain scalability in relation to the type 
of manufacturing system. A benchmark would be 
beneficial for the comparison of different scalability 
solutions and manufacturing systems.

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605 Scalability Solutions in Blockchain-Supported Manufacturing: A Survey 
RQ2 explanation: Given the amount of published 
literature on blockchain-supported manufacturing, it 
is surprising that in reality none of the concepts has 
yet been implemented. Such a platform would make 
it possible to examine the real-world requirements 
of the global manufacturing system for scalability, 
decentralization, and security of blockchain 
technology [101]. In the case of the concept of 
blockchain-supported manufacturing industry supply 
chain management, the authors suggest that they 
would implement in the future a complete blockchain 
cross-chain platform based on the high-level model 
provided by this paper [90]. However, the authors 
acknowledge some of the barriers to the acceptance of 
such implementations by the existing manufacturing 
companies. As it is noted in the industrial 
implementation challenges, a coordinated effort of 
engagement must be initiated with manufacturing 
stakeholders across the industry – designers, job 
shop service providers, machine builders, regulators, 
certifiers, public policy, and corporate law [68]. In 
the future, a thorough study on the barriers to the 
adoption of proposed solutions in manufacturing 
would highlight the reasons why blockchain-supported 
manufacturing is not yet a viable concept in existing 
manufacturing systems.
RQ3 explanation: Currently, no existing research 
addresses how scalability limitations affect the 
performance of a manufacturing system. In the past, 
blockchain networks that have already implemented 
scalability solutions have encountered the same 
limitations. Questions arise as to what would happen 
if the blockchain network could not process all the 
transactions for manufacturing services and this would 
lead to congestion in manufacturing systems [30]. 
How would manufacturing systems react to this, or 
would parallel financial channels be established? In 
addition, there is an increase in the transaction fee 
in the congestion of the blockchain network, which 
could affect the price of the manufacturing service. 
Could transaction fees be higher than the price for 
manufacturing services? Blockchain represents the 
financial level that is appended to the manufacturing 
system in blockchain-supported manufacturing. 
Given that infrastructure needs to be maintained to 
operate, in addition to the usual manufacturing roles, 
financial roles will also emerge. How to ensure that 
the blockchain-supported manufacturing system 
emphasizes the role of the manufacturer and not 
other roles, such as the role of maintaining financial 
infrastructure. Research on this question would reveal 
what limitations in manufacturing emerges due to the 
scalability limitations of blockchain technology.
RQ4 explanation: Most of the concepts 
discussed in the literature largely neglect the role of 
the Scalability trilemma, which defines the properties 
of a blockchain network. Scalability solutions are 
understood as solutions that increase scalability while 
maintaining decentralization and security. However, 
these solutions only increase the scalability of the 
system at the expense of decentralization and security 
(they move the properties of the network to another 
point in the trilemma). The open question thus remains 
with all the proposed concepts of how increased 
scalability of the system affects decentralization 
and security [67]. Does this scalable solution change 
decentralization and security in such a way that such 
an approach would no longer guarantee trust in the 
system [76]? In the literature at the moment, no work 
has yet addressed how the Scalability trilemma of 
blockchain technology is reflected in the behavior 
of the users. Some of the presented solutions allow 
dynamic movement of the system along the trilemma 
following the requirements of users and this decision 
is in their hands. When and why users of the proposed 
systems will decide to move to the blockchain network 
with a different position in the trilemma? Which 
requirements of manufacturing systems dictate the 
different characteristics of blockchain technology 
and whether this technology can fulfill them? The 
analysis of human behavior could potentially 
answer the question, however, to provide statistically 
significant results a lot of participants and iterations 
of the experiment are necessary. Such studies would 
highlight the requirements of manufacturing systems 
regarding the trilemma properties of blockchain 
technology.
5  CONCLUSIONS
The key properties of blockchain technology, which 
highlight the security and trust issues in decentralized 
environments, have brought great attention to 
emerging smart manufacturing concepts. Blockchain 
technology enables the connection of manufacturing 
entities that otherwise compete with each other on a 
global scale in a trustful way. However, this technology 
also has limitations, namely, the main limitation is 
a trade-off between scalability, decentralization, 
and security of the network. Scalability solutions of 
blockchain technology aim to solve the scalability 
problem or to at least provide a variety of possible 
settings of the three main properties in the trade-off.
So far, 36 publications have been published 
on the topic of scalability solutions in blockchain-
supported manufacturing. Different scalability 

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606 R o žman,	N.	–	Corn,	M.	–	Šk ulj,	G.	–	Diaci,	J.	–	Po držaj,	P .
solutions have been used for different specific cases 
of smart manufacturing systems. L1 and L2 scalability 
solutions are employed evenly, with L1 solutions being 
more commonly used for general SMS, while L2 
solutions are more commonly used in specific SMS 
concepts such as Cloud manufacturing. The extent 
of addressing scalability problems in literature and 
the number of proposed concepts that are extending 
general-purpose solutions with manufacturing-specific 
functionalities increases over time. However, there 
are still open issues on this topic, especially the lack 
of analysis of the impact of scalability limitations on 
the operation of blockchain-supported manufacturing 
systems. In addition, most of the literature ignores 
the scalability trilemma, which, despite the proposed 
scalability solutions, remains a constraint that large 
blockchain-supported manufacturing systems will 
encounter sooner or later. Furthermore, there is 
currently no implementation of the proposed concepts 
in the industry, which would confirm the need to 
comply with the limitations of the scalability of 
blockchain technology in manufacturing systems.
6  ACKNOWLEDGEMENTS
This research was funded by the Slovenian Research 
Agency under the funding “Young Researchers” and 
research program Grant P2-0270.
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