Research Paper

Technological Forecasting & Social Change 188 (2023) 122275
Contents lists available at ScienceDirect
Technological Forecasting & Social Change

journal homepage: www.elsevier.com/locate/techfore
Technical challenges of blockchain technology for sustainable

manufacturing paradigm in Industry 4.0 era using a fuzzy decision
support system
Dan Su a, Lijun Zhang b, *, Hua Peng c, Parvaneh Saeidi d, e, Erfan Babaee Tirkolaee f
a
School of Management, Tianjin University of Technology, Tianjin 300384, China
b
School of Management, Hebei Geo University, Shijiazhuang, Hebei 050031, China
c
School of Business, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong 519100, China
d
Facultad de Administración y Negociación, Universidad Tecnológica Indoamérica, Quito, Ecuador
e
Research Center in Business, Society and Technology, ESTec, Universidad Tecnológica Indoamérica, Quito, Ecuador
f
Department of Industrial Engineering, Istinye University, Istanbul, Turkey
A R T I C L E I N F O A B S T R A C T
Keywords: Since 2008, many academics have increasingly paid attention to blockchain technology from different per
Blockchain technology spectives. In general, researchers desire to achieve global blockchain systems within a sustainable manufacturing
Technical challenges domain; however, a number of technical challenges have come to exist in the recent decade, for instance,
Sustainable manufacturing paradigm
consensus algorithms and computing paradigms that can meet the privacy protection requirements of
Industry 4.0
manufacturing systems. Therefore, an integrated decision-making framework called Pythagorean fuzzy-entropy-
Fuzzy decision support system
rank sum-Combined Compromise Solution (PF-entropy-RS-CoCoSo) is developed in this study, including two
main phases. In the first phase, the PF-entropy-RS method is applied to obtain the subjective and objective
weights of criteria to evaluate the technical challenges of transforming blockchain technology for a sustainable
manufacturing paradigm in the Industry 4.0 era. The PF-CoCoSo model is then utilized in the second phase to
assess the preferences of organizations over different technical challenges of the blockchain technology trans
formation for the sustainable manufacturing paradigm in the Industry 4.0 era. An empirical case study is taken to
assess the main technical challenges of blockchain technology transformation for the sustainable manufacturing
paradigm. Furthermore, a comparison analysis and a sensitivity investigation are made to demonstrate the su
periority of the developed framework.
1. Introduction species of craft production and continues to mass production, to multi-

variety production, and then to a small batch of flexible production.
Recent years have witnessed the development of different strategies With the increase in market complexity and personalized demands, the
in the manufacturing sector (Kang et al., 2021; Li et al., 2020). Alter quick update of the information and communication technologies
natively, industrial value formation is resulting in a paradigm shift in (ICTs), e.g., “autonomous robots, simulation”, “big data and analytics”,
manufacturing due to different factors such as the increase of global “additive manufacturing and augmented reality”, “cybersecurity”,
resource restraints and market heterogeneity, big variations in individ “cloud computing”, “real-time location system”, and “radio frequency
ual demands, and sustainability-related issues, which are supported by identification (RFID)”, as well as the sustainability-related concerns in
many evolving technologies in the information and communication the development of societies have driven the innovation of the
domain (e.g., the internet, big data, blockchain, cyber-physical system, manufacturing paradigm. Manufacturing in recent years has moved to
cloud computing, and internet of things (IoT)). In fact, manufacturing ward some novel concepts such as intelligence, personalization, inter
has a leading role in the economic development of nations and the connection, globalization, real-time, greenization, and servitization.
creation of social value (Haraguchi et al., 2017; Marconi et al., 2016). Such transformation is being driven mostly by national strategies,
The evolution of the manufacturing paradigm began from a single technological progress, and social value creation. Furthermore, as
* Corresponding author.
E-mail address: zljj14619@hgu.edu.cn (L. Zhang).
https://doi.org/10.1016/j.techfore.2022.122275
Received 23 May 2022; Received in revised form 14 December 2022; Accepted 14 December 2022
Available online 9 January 2023
0040-1625/© 2023 Elsevier Inc. All rights reserved.
D. Su et al. Technological Forecasting & Social Change 188 (2023) 122275
manufacturing resources are being increasingly consumed and an contemporary manufacturing paradigms can be observed in different
imbalance has appeared between manufacturing resources and capac new social media. For these paradigms, there is a lack of secure cyber
ities, manufacturing firms have encountered a big problem: the shortage tools for identifying, maintaining, and evolving group consensus. Such
of (or idle) resources. As a result, to properly update the manufacturing deficiencies lead to many problems concerning trust, confidentiality,
paradigm, the resources in the manufacturing sector are required to be and cybersecurity, which need to be handled appropriately. In addition,
well-optimized. Unceasing innovations have initiated the current shift the combination of the above-noted challenges with the mass individ
that has occurred in the value creation processes in the manufacturing ualization requirements of products considerably complicates the sus
paradigm (Siemieniuch et al., 2015). tainability pursuit in manufacturing activities (Mourtzis and Doukas,
Manufacturing organizations the world over are facing difficulties in 2012). Tampered, illegitimate, or incorrect data can result in inappro
embracing the challenges brought about by recent societal, technolog priate conclusions and considerably threaten the future value-added and
ical, and economic innovations (Sharma et al., 2020). In order to tackle cross-linked manufacturing networks.
the challenges brought about by sudden technological disruptions, or For the first time, in 2008, Satoshi Nakamoto suggested the idea of
ganizations need to have a clear strategic vision with their capabilities blockchain technology (Nakamoto, 2008). Blockchain refers to a
for reorganizing their value chains in a responsive manner (Schumacher distributed data structure where users share data through a peer-to-peer
et al., 2016). Organizations can gain a competitive advantage by uti network. Blockchain comprises a chain of blocks recording the trans
lizing sustainable manufacturing techniques for environment-friendly actions carried out by contracting parties. The details of the transaction
products and operations (Jackson et al., 2016). For environmentally are saved on a publicly-accessible ledger which is termed ‘distributed
sustainable decision-making in manufacturing, digital technologies ledger’ (Giungato et al., 2017). After the generation and validation of a
form a fundamental component as they help optimize resource usage, transaction/block by certain members of the network, the block will be
reducing the harmful impact on the environment (de Sousa Jabbour added to the network and connected to formerly-constructed blocks (Fu
et al., 2018; Dubey et al., 2019). To incorporate sustainability in et al., 2018). Individuals, algorithms, machines, and organizations can
manufacturing operations, collective success in areas such as verify the transactions conducted in the network. This technology can
manufacturing planning and control, workforce capability, and perfor remove the intermediates from a network and connect various parties to
mance measurement are required (Ngai et al., 2013). each other directly; this can finally result in the reduction of transaction
In the contemporary era, sustainability is a highly critical subject and costs and human errors (Esmaeilian et al., 2020). If data are stored in
an engineering challenge (Leng et al., 2020). To make sure of the sus shared databases instead of central ones, the risk of data loss could be
tainability of manufacturing systems in the future, there is a need for the significantly reduced, and also information security and transparency
development of smart technologies. Among all technologies, blockchain could be substantially enhanced (Esmaeilian et al., 2020). Smart con
can be recognized widely as the development of information technology tracts can remove the requirement for any intermediary and decrease
(IT) for realizing sustainability in industries/businesses of the following transaction costs (Leng et al., 2021). This type of contract can improve
generations (Esmaeilian et al., 2020; Teh et al., 2020; Tiscini et al., information privacy and security since all the transactions are required
2020). Several researchers have investigated sustainable manufacturing to be confirmed with the essential legal agreements and also need the
in Industry 4.0, which is supported by blockchain technology focusing verification of the members, which is done on the basis of the trans
on commercial, technical, operational, and organizational aspects. The action validation rules that have been predefined in the smart contracts
17 Sustainable Development Goals (SDGs) established by the United (Sundarakani et al., 2021).
Nations can be generally classified into three dimensions: economic, Blockchain essentially combines different features, e.g., decentral
social, and environmental (Delanka-Pedige et al., 2021; Montiel et al., ized structure, smart contracting, consensus algorithm, storage mecha
2021). Manufacturing is generally known as harmful to the environ nism, distributed notes, and asymmetric encryption; this way, it ensures
ment; however, it positively contributes to human beings’ requirements that the network it creates is secure, transparent, and visible. This
for comfort and a decent living level. Sustainability has the capacity to technology keeps all transactions and data confidential, integrated, and
reduce system risk/uncertainty, save energy, meet individualized con accessible. It plays the role of a shared, distributed, open ledger that aids
sumers’ demands, address social responsibilities, and enhance the in storing/recording data and transactions, which are supported by a
resource productivity of the manufacturing sector (Ghobakhloo, 2018). cryptographic value (Choi, 2020) through a peer-to-peer network
Different progressive manufacturing models (which include sustainable (Chang et al., 2019; Choi et al., 2019). When the records are added to the
processes and systems used for the generation of products/services with blockchain, they will not be editable without changing the former re
higher sustainability) have been developed by different researchers; cords, which must be done with the consent of all, or the majority of, the
these models include peer production, social manufacturing, crowd involved parties. This mechanism causes it to be of high safety to busi
manufacturing, and open production. Sustainability of the ness operations. This has many implications in different fields, such as
manufacturing sector is in line with “Goal 9: Industry, innovation and the design of smart contracts in order to detect financial fraud or
infrastructure” and “Goal 12: Responsible consumption and production” securely share medical records between healthcare professionals.
in SDGs. In sustainable manufacturing, the key goal is initially economic The successful establishment of Industry 4.0 approaches in industrial
sustainability (Tanco et al., 2021; Zhang et al., 2021). companies requires a structured decision support system. The main
In addition, the sustainability of manufacturing plays a crucial part objective of such a structured system is the case-oriented analysis and
in sustainably developing the global society since it addresses world assessment of available Industry 4.0 approaches to choose the most
wide challenges, e.g., the requirement for renewable sources of power suitable ones for an individual company. To address all the requirements
and green buildings (Moldavska and Welo, 2017). In the aforementioned with respect to the technical challenges of the blockchain technology for
sustainable manufacturing visions, the key trend indeed lies in the novel the sustainable manufacturing paradigm, a fuzzy decision support sys
characteristic of the crowded or clustered or decentralized in tem is developed in this study.
terconnections of socialized manufacturing resources and open- The currently-used environmental sustainability solutions are not
architecture products, which suggests an essential reorganization of publicly accessible. The data gathered are less exposed to monitoring
the cross-enterprise manufacturing network. For the sustainability of the processes; thus, there is a probability of collecting falsified data. This
manufacturing sector, manufacturers need to share products’ lifecycle challenge could result in different problems, for instance, the lack of
information and also keep collaboration in an inherently trustless trust and the data island effect. Several systems have been proposed in
manufacturing network (Jiang et al., 2016). On the other hand, many the literature to address these problems effectively, which is supported
business negotiations and offline contract signing are consuming and by blockchain technology. This technology also plays an important role
wasting countless resources during the products’ lifecycles. The in developing environmental sustainability in the long run, which could
2
lead to positive impacts on the alleviation of climate change. Though, blockchain technology to be implemented in sustainable
the application of blockchain to environmental sustainability problems manufacturing.
is still in its infancy. Two reasons, i.e., the manufacturing systems’ V. The PF-entropy-RS approach is utilized to evaluate and rank the
complexity and the deficiency of optimization, have caused the perfor technical challenges of blockchain technology transformation for
mance of implemented manufacturing systems to be much lower than the sustainable manufacturing paradigm in the Industry 4.0 era.
designed; this situation results in the emission of carbon. The latest VI. To present the sensitivity and comparison analyses to validate the
developments that have occurred in fields such as fog computing and integrated PF-entropy-RS-CoCoSo approach.
edge computing have provided new motivations for reconsidering the
ways blockchain could be effectively applied to manufacturing systems. The remainder of the paper is provided based on the following sec
A decentralized peer-to-peer (P2P) communication mode is employed in tions. Section 2 presents the literature review and related works to the
blockchain to effectively process the information between machines, technical challenges of blockchain technology transformation for the
where this technology can significantly improve process flexibility and sustainable manufacturing paradigm in the Industry 4.0 era with ap
social sustainability. On the other hand, because of the significance of plications. Section 3 provides the proposed PF-entropy-RS-CoCoSo
sustainability in the manufacturing sector, many scholars have become method. Section 4 presents the experimental outcomes of the study,
interested in the investigation of the ways blockchain may contribute to the case study, sensitivity analysis, and comparison. Finally, Section 5
the sustainable manufacturing paradigm in the age of Industry 4.0. discusses the conclusion of the study.
However, to implement blockchain technology in the manufacturing
sector, there are different technical challenges; therefore, to our best 2. Literature review
knowledge, there are a few studies evaluating the technical challenges of
blockchain technology in sustainable manufacturing. Thus, this work 2.1. Blockchain technology and sustainable manufacturing system
aims to identify, evaluate, and analyze the main technical challenges of
blockchain technology in sustainable manufacturing. This study tries to To date, blockchain computing has evolved through three stages
answer the following research questions: (Zhao et al., 2016; Zheng et al., 2022). Blockchain 1.0 was a technology
for digital currency programming. The first digital currency application
i. What are the main technical challenges of blockchain technology in of blockchain was Bitcoin (Sun et al., 2022). Then, blockchain 2.0
the field of sustainable manufacturing paradigms in the Industry 4.0 established programmable smart contracts. On this platform, anyone is
era? able to upload a program and make it executable by itself. Blockchain
ii. How can blockchain features enhance the sustainable manufacturing 2.0 makes sure that the uploaded program can execute the preset logic in
paradigm in the Industry 4.0 era? a credible and automatic way through self-limitation and security
encryption (Leng et al., 2020). After that, blockchain 3.0 emerged,
This work contributes to the existing body of knowledge on sus which refers to programmable social governance (also referred to as
tainable manufacturing by examining the implications of blockchain digital society). It can accelerate the formation of communities with
technology and evaluating the technical challenges the manufacturing higher sustainability. Blockchain makes the stage ready for the syn
companies may face when implementing this new technology. The chronization and convenience of data tracking, which could result in
findings of this research could be considered a foundation for further decreasing the supervision costs of society. In the long term, it is possible
discussions and research by both researchers and practitioners working that the decentralized computing model of blockchain could entirely
in this domain. In addition, this paper provides directions for future reform human societies by providing better cooperation and
research on blockchain technology and its application in sustainable governance.
manufacturing. The available trust determination solutions make connections be
On the other hand, the new methodology of Pythagorean fuzzy- tween providers and demanders and facilitate information accessibility
entropy-rank sum (PF-entropy-RS) weight-finding technique to for both of them regarding their capacities and requirements. Prosumers
compute the criteria weights or significance degrees of criteria. The have trust in such centralized platforms for the provision of verified
combined compromise solution (CoCoSo) method is a new elegant services and the reduction of the costs that are generally accompanied
approach to handling multiple-criteria decision-making (MCDM) prob by outsourcing/crowdsourcing parts to manufacturers (Leng et al.,
lems. Thus, in this study, we have developed a new MCDM approach 2014). Nevertheless, sustainable manufacturing is typically decentral
using the PF-entropy-RS and PF-CoCoSo methods and further imple ized and conducted by various production units and individuals; in this
mented it to the evaluation of the technical challenges of the blockchain context, each production unit can be considered as an isolated infor
technology transformation for the sustainable manufacturing paradigm mation island (Matt et al., 2015). The activities such as conceptualizing,
in the Industry 4.0 era. The primary outcomes of the developed work are designing, manufacturing, and assembling the products have become
given as follows: increasingly complex due to the increase of the socialized manufacturing
resources and the enabling technologies taking part in this progress.
I. To conduct a survey approach using the current literature review Sharing information about the products’ lifecycles is altering intellectual
and interview experts to identify the technical challenges of property protection and is causing the roles of manufacturers to be
blockchain technology to implement in sustainable changed significantly in the network (Lee et al., 2019). It is not easy to
manufacturing. guarantee the production cycle and quality due to the low coordination
II. To demonstrate a comprehensive framework to evaluate the capabilities of upstream and downstream in the manufacturing com
technical challenges of the blockchain technology transformation munities (Leng et al., 2019). Manufacturers cannot find the fault source
for the sustainable manufacturing paradigm in the Industry 4.0 easily and quickly once quality issues are found since such faults could
era. be attributed to a single node or cohesion between the nodes.
III. To introduce a new decision-making approach on PFSs to analyze It is also not easy to manage the information in sustainable
the technical challenges of blockchain technology to implement manufacturing since there is a need for dependable and real-time data to
in sustainable manufacturing. evade risks, fraud, and poor performance (Saberi et al., 2019; Sundar
IV. To propose an integrated decision-making approach using the PF- akani et al., 2021). It is highly required to enhance the traceability,
entropy-RS and PF-CoCoSo models under PFSs to rank the orga reliability, and authenticity of data through better verifiability and
nizations and analyze and assess the technical challenges of enhanced systems of information sharing (Saberi et al., 2019). Block
chain supports a fundamental database structure through the
3
integration of hash chains and data blocks. Being integrated with applications of blockchain technology to businesses.
timestamp technology causes the existence-proof to become reliable. Yermack (2017) attempted to examine the ways blockchain tech
Blockchain comprises a consensus protocol, which helps the system to be nology can potentially influence corporate governance. According to his
continuously updated. Once a fresh data transaction is added to a block results, investors (i.e., shareholders) can enhance their profits through
of the chain, all the data copies possessed in other distributed nodes will improved liquidity and lower costs, two consequences of the use of
be updated in a synchronous way. The blockchain structure is decen blockchain technology. Ryan and Donohue (2017) provided a compre
tralized; this characterization supports the process of verifying the hensive description of the applications of blockchain technology to
transactions directly between stakeholders and removes the require trading securities. In addition, they discussed the limitations of the ap
ment for any other intermediary for this purpose (Esmaeilian et al., plications of this technology and provided some guidelines for corporate
2020). Through blockchain technology, the firms within the supply lawyers that give assistance to companies tending to transact securities
chain network would be able to share, access, and verify information in a through a blockchain platform. A new model, called Emission Trading
completely secure way since the data and transactions are supported by Scheme (ETS), was designed by Khaqqi et al. (2018) under the Industry
advanced cryptography (Chen and Bellavitis, 2020). This protection 4.0 framework. ETS uses blockchain technology and smart devices to
mechanism causes a reduction in the risk of losing and changing infor enhance the compliance measures of ETS policy. Zareiyan and Korjani
mation and inhibits human-induced errors in transactions (Sundarakani (2018) suggested a decentralized solution based on blockchain, called
et al., 2021). Blockchain also positively impacts the trustworthiness of 3D-Chain, with the aim of providing a global manufacturing ecosystem
supply chain transactions, time of supply chain activities, supply chain for designers, manufacturers, and consumers to have effective in
operations, and decision-making efficiency (Ko et al., 2018). teractions with no restrictions in the era of Industry 4.0. In another study
Blockchain provides a reliable and strong mechanism through which (Leng et al., 2020), the authors proposed the concept of the social
users can distribute and store record history on the internet (Guaita manufacturing mode to address the increasing production personaliza
Martínez et al., 2022). It makes a sequential link among data blocks in tion requirements and socialized manufacturing resources.
chronological order to make sure that the distributed ledger will not be Angrish et al. (2018) presented three smart contract representations
tampered with or cryptographically forged (Al-Jaroodi and Mohamed, for the purpose of modeling the relationships among different partici
2019). It provides manufacturers with securely-shared ledgers free from pants, which are established in a secondary contract index, consistent
any intermediary (Leng et al., 2020). Blockchain provides a ground- with the logic of “purchasing-supply” in real-world industrial produc
breaking transparent, and decentralized mechanism for doing trans tion activities. Apart from the popular shared economy businesses such
actions. Such transparency and traceability could enhance the as Uber and Airbnb, in the digital economy, there are numerous other
manufacturing networks’ sustainability and, at the same time, avoid the opportunities for the creation of many sharing applications. A scalable
intervention of third parties who are incapable of adding any value cross-enterprise framework based on blockchain was suggested by Li
(Abeyratne and Monfared, 2016). Researchers and practitioners have et al. (2018) to securely share manufacturing resources/knowledge in
not completely recognized the sustainability advantages of blockchain open ecosystems in a way to aid manufacturers in providing services of
for disrupting the manufacturing sector (Queiroz et al., 2020). high flexibility, efficiency, and quality. Gong (2018) designed a
It should be noted that an intact blockchain applicable to sustainable collaborative-crowdsourcing product fulfillment model for the accom
manufacturing comprises four components: the machine’s digital twin, modation of the decentralized and collaborative product manufacturing
blockchain agent node, key-value database, and blockchain view man processes for open design and manufacturing. In another research, two
ager. The blockchain-based digital twin has interactions with other types of blockchain networks, i.e., private and public, were employed by
digital twins, such as the manufacturing execution systems in cyber Barenji et al. (2018) for manufacturing service providers on cellular
space (Aslam et al., 2021). The blockchain agent node is hosted at the manufacturing. More specifically, the public blockchain was employed
client to help the machine easily interact with the blockchain. Finally, for the service provider level, while the private one was employed for
the blockchain view manager is a visualization tool typically applied to the workshop level, which is connected to the machine level to receive
trace the transactions on the blockchain system to be completely read and collect data. A blockchain-enabled promotion asset exchange model
able for humans. In addition, the blockchain can arrange for an interface was proposed by Şeref and Gökhan (2018) to collect more detailed in
for other cyber systems, and users can have some manipulations on the formation from manufacturers for the removal of the usability bottle
blockchain to provide more application services. necks in conventional customer loyalty programs. They integrated the
smart contract and token mechanism into their proposed model for the
2.2. Related works digitalization of the transaction processes, thereby improvement of the
usability for users. In regard to the opaque product distribution and low
This section reviews research conducted on the application of distribution margin, Yoo and Won (2018) introduced a system of smart
blockchain to the sustainable manufacturing paradigm in the Industry contracts for the price-tracking portion of customer relationship man
4.0 era. In this sense, Bonvillian (2013) reviewed the advanced agement. It was designed so that it could enhance the transparency of
manufacturing policies and paradigms for innovation with an emphasis the product distribution and, this way, encourage manufacturers not to
on innovative manufacturing, particularly technological innovation, as chase exorbitant profits. To extend this discussion, Mondragon et al.
a major factor in economic growth. In another study, Abeyratne and (2018) conducted a specific examination of the trade of composite ma
Monfared (2016) presented the use of blockchain in supply chains in the terials in the manufacturing industry with the use of a blockchain
manufacturing industry. They discussed the positive impacts that platform, highlighting the point that the product quality could be
blockchain can have on the supply chains’ transparency as well as enhanced using the blockchain technology.
product quality. Liu et al. (2017) suggested a production credit mech A blockchain-secured cognitive manufacturing architecture was
anism based on blockchain to normalize and regulate inter-enterprise introduced in the study of Chung et al. (2019); they used a sidechain-
collaborations in a social manufacturing paradigm. Korpela et al. based distributed consensus algorithm for the improvement of the
(2017) investigated the ways to digitalize supply chain systems by fault tolerance capacity in smart devices. Lee et al. (2019) designed a
means of blockchain technology. According to Yermack (2017), block conceptual framework using blockchain to extend the functionality of a
chain technology has positive impacts on governance; it allows share cyber-physical manufacturing system. Geiger et al. (2019) proposed a
holders to monitor the managers more easily. Accordingly, this study tamper-proof blockchain-based framework to keep track of distributed
evaluates the ways manufacturing companies can adopt blockchain operations to lower the cycle time for product manufacturing as much as
technology under managerial delegation game situations. Mougayar possible. Narayanaswami et al. (2019) designed a reference software
(2016), in his book, provided a detailed discussion on the potential architecture based on blockchain technology to provide visibility and
4
document provenance and allow permissioned data access to simplify to transfer and verify the business partners’ trustworthiness. West
the automation of numerous high-volume tasks (for instance, payments, erkamp et al. (2020) introduced a non-fungible digital token system on
reconciliations, and settlements) in modern supply chains. (Papakostas the basis of blockchain in order to locate each batch of products,
et al., 2019) designed a conceptual blockchain application for the including the components of the products. Catalini and Gans (2020)
management of product information in the development processes. reviewed the mechanisms used in blockchain technology to save costs.
Hybrid secure information architecture was designed by Adhikari and In addition, they presented more specificity around the vague arguments
Winslett (2019), combining cloud storage and blockchain computing for for the cost savings of blockchain technology by separating the block
data management purposes. Li et al. (2019) introduced a mold design chain technology’s cost reduction effects into networking costs and
knowledge-sharing platform by combining blockchain and a private verification costs. Hallam et al. (2021) proposed a network payment
cloud in another study. In their platform, the private cloud stores the system based on blockchain technology, which could be applied to se
mold redesign knowledge privately for each manufacturer, whereas the curities settlement.
blockchain securely records the knowledge transactions. Zhang et al. Technological challenges have a clear dominance on the findings of
(2019) suggested a decentralized knowledge-sharing framework by the above-reviews studies. A number of researchers have stated that
combining blockchain with edge computing. In this system, edge further research is needed to identify the benefits of blockchain imple
computing is responsible for providing smart services for the fulfillment mentation in public services, and these benefits need to be higher than
of the decentralization requirements, whereas blockchain is used to the costs of the development and running of the system. A number of
guarantee the tamper-proofing of knowledge sharing. highlighted challenges are typically observed in blockchain technology,
For the provision of personalized, integrated, and on-demand auto for instance, usability, computational efficiency, scalability, interoper
motive services, Sharma et al. (2019) made a combination of a ability, and storage size. Remember that this technology is still imma
blockchain-based distributed business framework and a miner node se ture; this characteristic is a fundamental reason for all challenges arising
lection algorithm. Xu et al. (2019) introduced a traceability system in the process of blockchain adoption. Immaturity can be a common
based on blockchain, called origin chain, to restructure the currently- problem in all cases related to the adoption of new technologies.
used software by replacing the centralized database with blockchain Nonetheless, the literature reveals significant progress that can result in
in a way to arrange for transparent tamper-proofing capability with high overcoming such challenges.
accessibility and smart regulatory-compliance with respect to product Accordingly, this study identified 24 technical challenges of block
provenance tracing. A blockchain-based automated business process chain technology transformation for the sustainable manufacturing
management (BPM) solution was suggested by Viriyasitavat et al. (2020) paradigm (Fig. 1) that including retrieval of encrypted data (C1),
Twinning blockchain with other systems (C24)
Balance the complexity and security (C23)
Consensus efficiency (C22) Manufacturing

company 1
Data mining efficiency (C21)
Lifecycle, performance expectancy (C20)
Responsibilities and roles (C19)
Risk management and fraud detection (C18) Manufacturing

company 2
Standardization (C17)
Quantum attacks (C16)
Effective self-adaptive adulteration (C15)

Manufacturing
Middleware solutions (C14) company 3
Functional completeness (C13)
System resiliency (C12)
Black box effect and inefficiency (C11)

Manufacturing
Multi-chain synchronization/integration (C10) company 4
Integrating the multi-platform services (C9)
Privacy protection (C8)
Computing on encrypted black data (C7) Manufacturing

company 5
Balance the cost (C6)
Massive erroneous incorrect and useless data (C5)
Decentralized intelligence (C4)
New business framework and model (C3)
Scalability and tolerance of fault (C2)
Retrieval of encrypted data (C1)
Fig. 1. Technical challenges of blockchain technology transformation for the sustainable manufacturing paradigm.
5
scalability and tolerance of fault (C2), new business framework and developed.
model (C3), decentralized intelligence (C4), massive erroneous incorrect
and useless data (C5), balance the cost (C6), computing on encrypted 3.2. Preliminaries
black data (C7), privacy protection (C8), integrating the multi-platform
services (C9), multi-chain synchronization/integration (C10), black box Here, we show some ideas related to the PFSs.
effect and inefficiency (C11), system resiliency (C12), functional
completeness (C13), middleware solutions (C14), effective self-adaptive Definition 1. (Yager, 2013, 2014). A PFS ‘X’ in a discourse set U is
adulteration (C15), quantum attacks (C16), standardization (C17), risk defined as,
management and fraud detection (C18), lifecycle responsibilities and X = {〈ui , X(μX (ui ) , νX (ui ) ) 〉 | ui ∈ U }, (1)
roles (C19), performance expectancy (C20), data mining efficiency (C21),
consensus efficiency (C22), balance the complexity and security (C23) where μX : U → [0, 1] and νX : U → [0, 1] designate the BD and ND of the
and twinning blockchain with other systems (C24). element ui ∈ U to X, under the constraint that 0 ≤ (μX(ui))2 + (νX(ui))2 ≤
1. For each ui ∈ U, the “hesitancy degree (HD)” is specified by πX (ui ) =
3. Methodology √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
1 − μ2X (ui ) − ν2X (ui ) . For easiness, Zhang and Xu (2014) indicated PFN
This section describes the proposed fuzzy decision support system in
by η = (μη, νη), which fulfills μη, νη ∈ [0, 1] and 0 ≤ μ2η + ν2η ≤ 1..
this study. First, a history of “intuitionistic fuzzy sets (IFSs)” is reviewed
which is then followed by preliminaries. Next, the PF-entropy-RS- Definition 2. (Peng and Yang, 2016). Let η = (μη, νη) be the PFN. The
CoCoSo approach is elaborated. score value and accuracy degree of η are termed as
1( ( ) )
3.1. History and development of intuitionistic fuzzy sets S(η) = (μη )2 − (νη )2 + 1 , (2)
2
The IFSs were first proposed by (Atanassov, 1996) and are defined by ℏ(η) = (μη )2 + (νη )2 . (3)
two different degrees: “belongingness degree (BD)” and “non-belong
ingness degree (ND)”, and hold the condition that the sum of the BD and
ND is ≤1. On the other hand, real conditions may arise in cases of
decision-making problems where “decision experts (DEs)” assign a value Definition 3. (Yager, 2013, 2014). Let η = (μη, νη), η1 = (μη1, νη1), and
of 0.8 if an alternative meets the attribute and a value of 0.5 if it dis η2 = (μη2, νη2) be PFNs. Then, the different operations on PFNs are given
satisfies the attribute. In this situation, 0.8 + 0.5 > 1, and IFS is not able by
to address this condition properly (Yager, 2014; Zhang and Xu, 2014). In ηc = (νη , μη ),
an attempt to cope with this difficulty, Yager (2013, 2014) pioneered the
“Pythagorean fuzzy sets (PFSs)”. PFSs are capable of satisfying the (√̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ )
η1 ⊕ η2 = μ2η1 + μ2η2 − μ2η1 μ2η2 , νη1 νη2 ,
constraint that the square sum of BD and ND is ≤ 1. As a result, PFSs
have higher effectiveness compared to IFSs in the description of the ( √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅)
nature of uncertainties. Because of the exclusive advantages of the η1 ⊗ η2 = μη1 μη2 , ν2η1 + ν2η2 − ν2η1 ν2η2 ,
“Pythagorean fuzzy numbers (PFNs)”, Zhang and Xu (2014) presented
their basic operations for the purpose of solving the group decision- (√̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
( )λ
)
making concerns. In recent years, Rani et al. (2019) suggested a λη = 1 − 1 − μ2η , (νη )λ , λ > 0,
“multi-criteria decision-making (MCDM)” approach for the evaluation
( )
of the ways to select renewable energy sources in the context of India. √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
( )λ
In recent years, Yazdani et al. (2019a, 2019b) designed a novel ηλ = (μη )λ , 1 − 1 − ν2η , λ > 0.
MCDM method called “(CoCoSo)”. In fact, it was an integration of the
various compromise algorithms with different aggregation procedures
in an attempt to achieve a compromise solution. Deleting or adding al
Definition 4. (Zhang and Xu, 2014). Let η1 = (μη1, νη1), and η2 =
ternatives has lower impacts on the final ranking results attained by
(μη2, νη2) be PFNs, then a discrimination measure between η1 and η2 is
CoCoSo compared to those of the models such as the “visekriterijumska
given as follows:
optimizacija i kompromisno resenje (VIKOR)”, “technique for order
preference by similarity to ideal solution (TOPSIS)”, and other MCDM 1 (⃒⃒ 2 ⃒ ⃒
⃒ ⃒
⃒ ⃒
⃒ ⃒
⃒)
⃒
D(η1 , η2 ) = ⃒μη1 − μ2η2 ⃒ + ⃒ν2η1 − ν2η2 ⃒ + ⃒π2η1 − π2η2 ⃒ . (4)
models (Yazdani et al., 2019a). Deveci et al. (2021) developed an 2
extended version of CoCoSo using the logarithmic method and the
Power Heronian function to prioritize the real-time traffic management
methods. Torkayesh et al. (2021) introduced a framework by means of
the “best-worst method (BWM)” and “level-based weight assessment 3.3. The PF-entropy-RS-CoCoSo approach
(LBWA)”. Their study was aimed at determining the weights of health
care indicators. They also used CoCoSo to assess healthcare perfor This section develops a PF-entropy-RS-CoCoSo method under the
mances in a number of countries based on pre-determined indicator PFSs setting for treating decision-making problems. The calculation
weights. For the subjective weighting model, a procedure of rank sum procedure of the proposed method is given by.
(RS) method was given by Stillwell et al. (1981) to help the decision Step 1: Generate a PF-decision matrix (PF-DM)
maker give their ranking values for selected criteria. Until now, no one A set of ℓ “Decision Experts (DEs)”A = {a1, a2, …, aℓ} express the sets
has developed an integrated PF-entropy-RS weighting and CoCoSo- of m optionsO = {o1, o2, …, om} and n criteria C = {c1, c2, …, cn},
based method under PFSs, setting for the evaluation of the technical respectively. Considering the vagueness of behavior, deficiency of data,
challenges of the blockchain technology transformation for a sustainable and imprecise information related to the options, the DEs offer PFNs to
manufacturing paradigm in the Industry 4.0 era. To take the flexibility evaluate his/her decision on option oi concerning a criterion cj. Let ℤ(k)
and efficacy of PFSs, the paper aims to introduce an innovative = (ξ(k)
ij )m×n, i = 1, 2, …, m, j = 1, 2, …, n be the suggested decision matrix
discrimination measure and discuss its elegant properties. Based on it, a by DEs, where ξ(k) ij referes to the evaluation of an option oi over a cri
CoCoSo framework for evaluating the MCDM problem on PFSs has been terion cj in the form of PFN given by kth expert.
6
Step 2: Compute the weights of DEs In A-PF-DM, the decision-maker wants to utilize both subjective and
To determine the DEs’ weights, firstly, the importance degrees of the objective weights, for the following integrated weighted equation is
DEs are assumed as Linguistic Terms (LTs) and then expressed by PFNs. given.
To compute the kth DE, let Ak = (μk, νk, πk) be the PFN. Now, the expert
wj = γwoj + (1 − γ)wsj (10)
weight is obtained by
(( 2 ) )
μ − ν2k + 1 l − rk + 1 where γ ∈ [0, 1] is an objective factor of A-PF-DM weights. Here, woj
ϖk = ℓ k + ℓ , k = 1, 2, …, ℓ. (5) denotes the objective weight and wsj represents the subjective weight,
∑ 2 ∑
((μk − ν2k ) + 1 ) (l − rk + 1) respectively.
k=1 k=1
∑ℓ Step 5: Create the “normalized A-q-ROF-DM (NA-q-ROF-DM)”

Here, ϖ k ≥ 0 and k=1 ϖ k = 1..Step 3: Aggregate all PF-DMs The NAPF-DM ℝ = [ςij]m×n is created from A = (ξij)m×n, and is given
To construct the “aggregated PF-decision matrix (A-PF-DM)”, the by
“PF-weighted averaging (PFWA)” operator is used, and then A = (ξij)m×n, { ( )
( ) ξ = μ , νij , for benefit criterion,
where ςij = μij , νij = ( ij )c ij( ) (11)
( ) ξij = νij , νij , for cost criterion.
( ) (ℓ)
ξij = μij , νij = PFWAϖ ξ(1) (2)
ij , ξij , …, ξij
Step 6: Assess the weighted sum and power weight comparability
(√√
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ) measures
√ ∏ ℓ ( ) ℓ
∏
= √ 1− 1 − μk 2 ϖk
, (νk )ϖk
. (6) The “weighted sum measure (WSM)” ℂ(1) i and “weighted product
k=1 k=1 measure (WPM)” ℂ(2)
i for each option are estimated as
n
Step 4: Proposed PF-subjective and objective weighting approach. α(1)
i = ⊕ wj ςij . (12)
Let w = (w1, w2, …, wn)T be the weight of the criterion set with
j=1
∑n
j=1 wj = 1 and wj ∈ [0, 1]. Now, we estimate the criteria weight by n
α(2) = ⊗ wj ςij . (13)
combining the objective and subjective weights as follows: i
j=1
Case I. Determination of objective weights by entropy method. Step 7: Relative compromise scores of each option
The following appraisal degrees for the aim of assessing the options’
Now, to find the criteria weights, the entropy model is extended
relative compromise scores, and given by
under the PFS environment as
( ) ( )
m (
∑ ( )) S α(1)
i + S α(2)
i
1 − E ξij (1)
βi = ∑ m ( ( ) ( ) ), (14)
woj = (i=1m ), (7) S α(1) + S α(2)
∑
n ∑( ( )) i=1
i i
1 − E ξij
j=1 i=1
where,
[{ ( ( ))} { ( ( ))} ]
( ) 1 ∑n
ν2ij + 1 − μ2ij μ2ij + 1 − ν2ij
E ξij = 1 − exp − I[μ2 ≥ν2 ] + 1 − exp − I[μ2 <ν2 ] (8)
n (1 − exp( − 1/2) ) i=1 2 ij ij 2 ij ij
( ) ( )
signifies the entropy measure of E(ξij) adopted by Rani et al. (2020c).
S α(1)
i S α(2)
i
Case II. Determine the subjective weights by the PF-RS method. β(2)
i = ( )+ ( ), (15)
minS α(1)
i minS α(2)
i
The subjective weighting system here enables us to reflect on the
i i
thoughts and intrinsic values of decision-makers. In the decision-making ( ) ( )

ϑ S α(1) + (1 − ϑ)S α(2)
process, the decision-maker’s opinion of each alternative with depen (3)
βi = (
i
)
i
( ). (16)
dent criteria is very important when selecting the best choice for the ϑmaxS α(1)
i + (1 − ϑ)maxS α(2)
i
given problem. In this critical situation, the decision-maker gives sub
i i
jective weight (Stillwell et al., 1981; Narayanamoorthy et al., 2020). Here, ϑ is the decision mechanism coefficient, and ϑ ∈ [0, 1].
Here, the procedure of the rank sum weight method gives to help the Generally, we take ϑ = 0.5..
decision maker to give their ranking values for selected criteria. The Step 8: Estimate the overall compromise degree of each option
formula of this method is given as follows: The compromise degree βi is computed to define the importance
rating of each option as
n − rt + 1
wsj = ∑ ), (9)
1 ( (1) ) ( )13
n ( /
n − rj + 1 (17)
(2) (3) (1) (2) (3)
βi = β + βi + βi + βi βi βi .
j=1 3 i
The options are sorted by decreasing the compromise degree (βi) of
where wsj represents the weights for each criteria j and n signifies the
the options.
number of criteria, rj denotes the rank of each criterion, j = 1,2,3, …,n.
Case III. Integrated weights using the objective and subjective
weights:
7
4. Results and discussion Table 2

Weight of DEs to the technical challenges of blockchain technology to imple
4.1. Case study ment in the sustainable manufacturing.
DEs LVs PFNs Score Rank Weights
To identify and evaluate the technical challenges of transforming a1 H (0.70, 0.45, 0.554) 0.6437 3 0.2074
blockchain technology for a sustainable manufacturing paradigm in the a2 VVH (0.85, 0.30, 0.433) 0.8162 1 0.3778
Industry 4.0 era, this study has conducted a survey approach using the a3 VH (0.80, 0.35, 0.487) 0.7588 2 0.2949
current the state of the art literature and interview with experts. To a4 MH (0.60, 0.55, 0.581) 0.5287 4 0.1199
identify, evaluate, and analyze the main technical challenges, this study
used different rounds of data collection with experts and decision-
makers in the areas of blockchain technology, manufacturing, and in Table 3
dustry 4.0. In this regard, in the first round of data collection, a The LDM by DEs for technical challenges of blockchain technology to implement
comprehensive study was done to collect the main technical challenges in the sustainable manufacturing.
of transforming blockchain technology into a sustainable manufacturing o1 o2 o3 o4 o5
paradigm in the industry 4.0 era. In the results of this round of study, we c1 (MH,L,VL, (MH,ML,L, (H,M,M, (MH,M,H,L) (VH,VH,H,
have identified 45 technical challenges using an interview with experts M) ML) MH) M)
and a current literature review. In the second stage, we designed a c2 (L,ML,M, (ML,VL,VL, (VH,M,MH, (M,MH,M, (VH,MH,
questionnaire based on these 45 technical challenges and invited 25 MH) M) M) ML) ML,M)
c3 (H,VH,H, (H,H,VH,M) (ML,ML,H, (M,VH,MH, (VH,M,VH,
experts from academic and industry aspects in the areas of blockchain,
M) MH) M) M)
the manufacturing industry, and industry 4.0. These 25 experts had >10 c4 (ML,MH,H, (VH,M,H, (ML,ML,MH, (ML,MH,M, (H,ML,VL,
years of experience in the field of study. To invite these selected experts M) M) H) ML) M)
in the primary round, we have sent a greeting invitation that included c5 (M,MH,H, (ML,H,H, (MH,ML,M, (ML,M,VH, (VVH,H,
the main aims of this study invitation to participate in our data collec M) MH) H) H) VH,M)
c6 (VH,MH,M, (M,VL,ML, (VH,H,M, (H,M,MH, (H,MH,ML,
tion survey. The invitation letter was first sent to each expert on the MH) M) MH) ML) MH)
sampling frame. The invitation was sent enclosed in the first draft that c7 (ML,MH,L, (L,ML,L,M) (ML,MH,M, (MH,M,ML, (H,VL,ML,
presented the study objectives and goals. Once the experts agreed with M) M) H) M)
the participant, we sent them the final version of the questionnaire, c8 (H,VH,VH, (M,VVH, (ML,M,MH, (M,MH,ML, (H,L,VL,
M) VH,M) ML) MH) MH)
which included 45 technical challenges. For this round of data collec
c9 (H,MH,H, (M,VH,H, (M,ML,H, (ML,VH, (VVH,H,
tion, we have given one month time to the experts to answer the ques M) MH) ML) MH,M) MH,M)
tions. After two rounds of reminders, we collected 15 questionnaires. c10 (ML,VH,H, (L,MH,H,M) (M,MH,ML, (MH,ML, (VH,M,VL,
According to the results of this round of data collection, experts selected MH) H) MH,H) M)
24 critical technical challenges to evaluate and analyze by decision- c11 (ML,ML, L, (MH,ML,L, (MH,VH,M, (H,M,MH, (MH,MH,M,
M) ML) MH) H) M)
makers. In the next data collection round, we invited eight decision-
c12 (ML,L,ML, (ML,MH, (H,VH,M, (VH,M,MH, (H,M,MH,
makers to evaluate and analyze these 24 important technical chal M) ML,M) MH) M) H)
lenges from the last round of data collection. To analyze, these 24 c13 (MH,M,H, (M,MH,H, (M,MH,ML, (MH,ML,M, (H,M,VL,
important technical challenges, an integrated decision-making frame MH) MH) M) MH) ML)
c14 (M,M,H, (M,MH,MH, (ML,VH,MH, (VH,MH,M, (VVH,H,
work called PF-entropy-RS-CoCoSo is developed in this study, including
MH) M) M) H) ML,H)
two main phases. In the first phase, the PF-entropy-RS approach is c15 (VH,MH,M, (ML,VVH,H, (M,ML,VVH, (ML,VH, (H,M,M,
applied to obtain the subjective and objective weights of criteria to M) MH) H) MH,MH) ML)
evaluate the technical challenges of transforming blockchain technology c16 (ML,L,L, (M,ML,VL, (MH,MH, (M,VH,H, (M,ML,L,L)
into a sustainable manufacturing paradigm in the industry 4.0 era. The ML) M) VH,M) MH)
c17 (ML,ML,M, (L,L,ML,M) (MH,ML,M, (ML,M,MH, (H,M,VL,
PF-CoCoSo approach is then utilized in the second phase to assess the
MH) H) H) VL)
preferences of organizations over different technical challenges of the c18 (MH,VH,H, (MH,VH,H, (M,VL,ML, (VL,ML,ML, (VVH,H,
blockchain technology transformation for the sustainable MH) M) M) M) ML,M)
manufacturing paradigm in the industry 4.0 era. An empirical case study c19 (ML,M,H, (MH,VVH, (ML,MH,M, (MH,MH, (L,VL,M,
MH) H,M) M) ML,H) ML)
from manufacturing companies is taken to assess the main technical
c20 (MH,M,H, (M,M,MH, (ML,VL,M, (VL,M,ML, (H,L,VL,
challenges of blockchain technology transformation for the sustainable MH) MH) MH) L) ML)
manufacturing paradigm. The implementation of the PF-entropy-RS- c21 (M,ML,H, (M,M,ML, (M,VL,ML, (VL,ML,MH, (H,M,VL,
CoCoSo method is discussed as follows: M) MH) H) L) VL)
Steps 1–3: Table 1 shows the importance ratings of the DEs and c22 (M,H,L, (MH,L,ML, (VVH,MH, (MH,L,H,L) (H,MH,L,
MH) M) ML,M) ML)
challenges in the form of LVs and then converted into PFNs. Table 2
c23 (MH,L,M, (H,ML,VH, (H,MH,L, (VH,H,M, (VH,H,M,
presents the DEs weight based on Table 1 and Eq. (5). Table 2 defines the H) H) ML) ML) MH)
c24 (VH,MH, (H,VL,ML, (VH,H,ML, (VH,ML,H, (H,L,VL,L)
VL,H) MH) ML) MH)
Table 1
Ratings of alternatives over criteria and DEs regarding the LVs.
“linguistic decision matrix (LDM)” of DEs to assess the MCDM problem.
LVs PFNs
From Eq. (6) and Table 3, an A-PF-DM A = (ξij)m×n is created and
Absolutely high (AH) (0.95, 0.20, 0.240) provided in Table 4.
Very very high (VVH) (0.85, 0.30, 0.433)
Step 4. Calculate the objective weights using the PF-entropy-based
Very high (VH) (0.80, 0.35, 0.487)
High (H) (0.70, 0.45, 0.554) procedure of each technical challenge of blockchain technology to be
Moderate high (MH) (0.60, 0.55, 0.581) implemented in sustainable manufacturing using Eqs. (7)–(8). The
Moderate (M) (0.50, 0.60, 0.624) resultant values are given in Fig. 1.
Moderate low (ML) (0.40, 0.70, 0.592) woj = (0.0442, 0.0433, 0.0476, 0.0360, 0.0408, 0.0382, 0.0415,
Low (L) (0.30, 0.75, 0.589)
Very low (VL) (0.20, 0.85, 0.487)
0.0489, 0.0431, 0.0317, 0.0416, 0.0420, 0.0340, 0.0363, 0.0410,
Absolutely low (AL) (0.10, 0.95, 0.296) 0.0552, 0.0410, 0.0545, 0.0409, 0.0436, 0.0404, 0.0345, 0.0393,
8
Table 4 0.0404).
The A-PF-DM for technical challenges of blockchain technology to implement in The subjective weights are calculated using Eq. (9), i.e., the PF-rank
the sustainable manufacturing. sum weight procedure of each technical challenge of blockchain tech
o1 o2 o3 o4 o5 nology to be implemented in sustainable manufacturing. The resultant
c1 (0.400, (0.433, (0.566, (0.581, (0.751,
values are illustrated in Table 5 and represented in Fig. 2.
0.710, 0.680, 0.559, 0.556, 0.402, Using the algorithm proposed for the PF-entropy-RS method, we
0.579) 0.593) 0.605) 0.595) 0.523) have to combine the PF-entropy for objective weighting and PF-rank
c2 (0.448, (0.306, (0.621, (0.533, (0.611, sum weight for subjective weighting by using Eq. (10). The integrated
0.659, 0.783, 0.523, 0.591, 0.543,
weight for τ = 0.5 is shown in Fig. 2 and given as follows:
0.604) 0.542) 0.584) 0.605) 0.576)
c3 (0.729, (0.720, (0.546, (0.676, (0.694, wj = (0.0471, 0.0333, 0.0421, 0.0230, 0.0221, 0.0574, 0.0524,
0.424, 0.433, 0.597, 0.477, 0.458, 0.0394, 0.0432, 0.0425, 0.0242, 0.0377, 0.0270, 0.0265, 0.0555,
0.537) 0.543) 0.588) 0.561) 0.556) 0.0643, 0.0538, 0.0439, 0.0438, 0.0518, 0.0402, 0.0573, 0.0480,
c4 (0.595, (0.652, (0.519, (0.517, (0.472, 0.0269).
0.551, 0.493, 0.618, 0.611, 0.664,
0.585) 0.576) 0.590) 0.600) 0.580)
Here, Fig. 2 depicts the weights of diverse technical challenges of the
c5 (0.608, (0.647, (0.526, (0.640, (0.758, blockchain technology transformation for the sustainable
0.533, 0.505, 0.603, 0.511, 0.398, manufacturing paradigm in the Industry 4.0 era with respect to the goal.
0.588) 0.571) 0.599) 0.574) 0.516) The quantum attacks (c16) with the weight value of 0.0643 has come out
c6 (0.636, (0.386, (0.674, (0.575, (0.582,
to be the most important technical challenge of blockchain technology
0.514, 0.716, 0.476, 0.561, 0.566,
0.576) 0.582) 0.565) 0.596) 0.584) to implement in sustainable manufacturing. Balance the cost (c6) with
c7 (0.484, (0.371, (0.526, (0.532, (0.463, the weight value of 0.0574 is the second most important technical
0.640, 0.711, 0.599, 0.596, 0.675, challenge of blockchain technology to be implemented in sustainable
0.596) 0.597) 0.603) 0.601) 0.575) manufacturing. Consensus efficiency (c22) has the third rank with a
c8 (0.760, (0.765, (0.507, (0.532, (0.463,
weight value of 0.0573, Effective self-adaptive adulteration (c15) has the
0.393, 0.394, 0.615, 0.601, 0.674,
0.518) 0.510) 0.604) 0.596) 0.575) fourth rank with a weight value of 0.0555, Standardization (c17) with a
c9 (0.647, (0.709, (0.540, (0.667, (0.705, weight value of 0.0538 has the fifth most important technical challenge
0.502, 0.445, 0.595, 0.493, 0.454, of blockchain technology, and the others are considered crucial tech
0.574) 0.547) 0.595) 0.559) 0.545)
nical challenges of the blockchain technology transformation for the
c10 (0.700, (0.586, (0.550, (0.557, (0.554,
0.459, 0.559, 0.587, 0.588, 0.595, sustainable manufacturing paradigm in the Industry 4.0 era.
0.546) 0.587) 0.594) 0.587) 0.582) Step 5: Since all criteria are beneficial-type criteria, thus, there is no
c11 (0.389, (0.433, (0.679, (0.608, (0.563, need to transform aggregated PF-DM into normalized A-PF-DM.
0.701, 0.680, 0.476, 0.532, 0.570, Steps 6–8: Using Eqs. (12) and (13), the WPM and WSM ratings are
0.597) 0.593) 0.560) 0.590) 0.599)
estimated for diverse companies under various different technical
c12 (0.382, (0.502, (0.697, (0.621, (0.608,
0.705, 0.627, 0.456, 0.523, 0.532, challenges in sustainable manufacturing. With the use of Eqs. (14)–(17),
0.597) 0.595) 0.554) 0.584) 0.590) the outcomes of the PF-entropy-RS-CoCoSo method are obtained and are
c13 (0.604, (0.618, (0.519, (0.507, (0.495, mentioned in Table 6. Corresponding to the compromise degree (βi), the
0.536, 0.528, 0.608, 0.618, 0.638,
prioritization of companies over the various different technical chal
0.590) 0.583) 0.601) 0.601) 0.590)
c14 (0.586, (0.571, (0.667, (0.649, (0.692,
lenges in sustainable manufacturing is o4 ≻ o3 ≻ o5 ≻ o1 ≻ o2, and thus,
0.545, 0.566, 0.493, 0.502, 0.471, the company-IV (o4) is the ideal option over different technical chal
0.599) 0.595) 0.559) 0.572) 0.547) lenges of the blockchain technology transformation for the sustainable
c15 (0.627, (0.734, (0.665, (0.675, (0.546, manufacturing paradigm in the Industry 4.0 era.
0.519, 0.433, 0.501, 0.487, 0.576,
0.581) 0.523) 0.554) 0.554) 0.609)
c16 (0.337, (0.398, (0.670, (0.709, (0.390, 4.2. Sensitivity investigation
0.733, 0.705, 0.486, 0.445, 0.698,
0.591) 0.588) 0.560) 0.547) 0.601) This subsection shows the sensitivity investigation associated with
c17 (0.462, (0.363, (0.526, (0.548, (0.483,
the parameter ϑ. The variation of ϑ is a useful issue helping to evaluate
0.650, 0.715, 0.603, 0.583, 0.653,
0.603) 0.597) 0.599) 0.599) 0.583) the sensitivity level of the approach, changing from subordinate UDs to
c18 (0.720, (0.714, (0.386, (0.385, (0.674, subordinate preferences. In addition, changing the values of ϑ is applied
0.437, 0.442, 0.716, 0.715, 0.488, to the investigation of the sensitivity of the proposed method to the
0.539) 0.544) 0.582) 0.583) 0.554) eminence of attribute weights.
c19 (0.572, (0.745, (0.526, (0.570, (0.362,
Table 7 and Fig. 3 represent the sensitivity analysis of the alterna
0.563, 0.417, 0.599, 0.577, 0.730,
0.596) 0.521) 0.603) 0.585) 0.579) tives for diverse values of the utility parameter ϑ. Based on the assess
c20 (0.604, (0.546, (0.416, (0.406, (0.434, ments, we obtain similar preferences o4 ≻ o3 ≻ o5 ≻ o1 ≻ o2 for ϑ = 0.0 to
0.536, 0.579, 0.699, 0.693, 0.694, ϑ = 1.0, which implies o4 is at the top of the ranking for each value of ϑ,
0.590) 0.606) 0.581) 0.596) 0.575)
while o2 has the last rank for each value of ϑ to the technical challenges
c21 (0.549, (0.489, (0.433, (0.442, (0.500,
0.584, 0.621, 0.692, 0.684, 0.620, of the blockchain technology transformation for the sustainable
0.598) 0.612) 0.578) 0.580) 0.605) manufacturing paradigm in the Industry 4.0 era. Therefore, it is
c22 (0.571, (0.439, (0.637, (0.536, (0.507, observable that the developed method possesses adequate stability with
0.569, 0.671, 0.526, 0.605, 0.615, numerous parameter values. As shown clearly in Table 7, the developed
0.591) 0.598) 0.563) 0.589) 0.604)
PF-entropy-RS-CoCoSo methodology is capable of generating stable and,
c23 (0.505, (0.667, (0.548, (0.674, (0.674,
0.619, 0.494, 0.595, 0.476, 0.476, at the same time, flexible preference results in a variety of utility pa
0.602) 0.558) 0.588) 0.565) 0.565) rameters. This property is of high importance for MCDM procedures and
c24 (0.614, (0.478, (0.645, (0.641, (0.424, decision-making reality.
0.556, 0.668, 0.513, 0.517, 0.700,
0.560) 0.570) 0.566) 0.567) 0.574)
4.3. Comparison with existing methods
In the current part of the study, we present a comparative study
9
Table 5
Weights of technical challenges of blockchain technology to implement in sustainable manufacturing using the RS method.
( )
Challenges a1 a2 a3 a4 Aggregated PFNs Crisp values S* ̃ξkj Rank of challenges Weight wsj
c1 MH M M MH (0.537, 0.583, 0.610) 0.474 10 0.0500

c2 M M M L (0.482, 0.616, 0.623) 0.426 18 0.0233
c3 M MH L M (0.502, 0.620, 0.603) 0.434 14 0.0367
c4 MH L ML M (0.439, 0.671, 0.598) 0.371 22 0.0100
c5 L ML L ML (0.354, 0.725, 0.591) 0.300 24 0.0033
c6 M H MH M (0.618, 0.525, 0.586) 0.553 2 0.0767
c7 ML M H L (0.546, 0.565, 0.619) 0.490 6 0.0633
c8 MH M L MH (0.496, 0.623, 0.605) 0.429 16 0.0300
c9 ML M MH ML (0.507, 0.615, 0.604) 0.439 12 0.0433
c10 H M ML MH (0.546, 0.585, 0.600) 0.477 9 0.0533
c11 L VL MH ML (0.413, 0.712, 0.568) 0.332 23 0.0067
c12 ML M MH L (0.500, 0.620, 0.604) 0.433 15 0.0333
c13 H ML L MH (0.499, 0.633, 0.591) 0.424 19 0.0200
c14 H L ML MH (0.494, 0.637, 0.592) 0.419 20 0.0167
c15 MH H L M (0.580, 0.565, 0.588) 0.509 4 0.0700
c16 ML H M M (0.580, 0.556, 0.596) 0.514 3 0.0733
c17 ML H M L (0.566, 0.571, 0.595) 0.497 5 0.0667
c18 L M MH ML (0.495, 0.624, 0.605) 0.428 17 0.0333
c19 M ML H L (0.534, 0.600, 0.596) 0.462 11 0.0467
c20 MH M MH M (0.554, 0.574, 0.602) 0.489 7 0.0600
c21 M ML M H (0.503, 0.614, 0.608) 0.438 13 0.0400
c22 H H M L (0.622, 0.521, 0.584) 0.558 1 0.0800
c23 M MH M MH (0.554, 0.575, 0.602) 0.488 8 0.0567
c24 M ML L H (0.457, 0.656, 0.601) 0.389 21 0.0133
Fig. 2. Weight of technical challenges of blockchain technology to implement in the sustainable manufacturing.
Table 6
Outcome of the developed method for different options.
( ) ( )
Options α(1)
i α(2)
i (1)
S αi
(2)
S αi β(1)
i β(2)
i β(3)
i βi Ranking
o1 (0.571, 0.574, 0.587) (0.534, 0.600, 0.596) 0.4981 0.4629 0.1956 2.0319 0.9401 1.7761 4
o2 (0.578, 0.574, 0.580) (0.521, 0.611, 0.596) 0.5024 0.4486 0.1935 2.0087 0.9304 1.7567 5
o3 (0.584, 0.563, 0.585) (0.562, 0.578, 0.591) 0.5122 0.4909 0.2042 2.1228 0.9814 1.8548 2
o4 (0.592, 0.554, 0.585) (0.570, 0.570, 0.591) 0.5219 0.5002 0.2080 2.1631 1.0000 1.8900 1
o5 (0.580, 0.569, 0.583) (0.544, 0.596, 0.591) 0.5061 0.4702 0.1987 2.0645 0.9552 1.8046 3
10
Table 7 n [ (⃒ ⃒ ⃒ ⃒ ⃒ ⃒ )]
Ranking results of the PF-entropy-RS-CoCoSo method with different values of ϑ. 1∑ ⃒ ⃒ ⃒ ⃒ ⃒ ⃒
D(oi ,ϕ+ ) = wj ⃒μ2ξij − μ2ϕ+ ⃒+ ⃒ν2ξij − ν2ϕ+ ⃒+ ⃒π2ξij − π2ϕ+ ⃒ ,i = 1,2,…,m.
2 j=1 j j j
ϑ O1 O2 O3 O4 O5
(20)
0.0 1.7673 1.7368 1.8548 1.8900 1.7957
0.1 1.7691 1.7408 1.8548 1.8900 1.7975
0.2 1.7709 1.7449 1.8548 1.8900 1.7993 and the degree of distance D(oi, ϕ− ) among the options oi and the PF-NIS
0.3 1.7726 1.7489 1.8548 1.8900 1.8011 ϕ− is given as follows:
0.4 1.7743 1.7528 1.8548 1.8900 1.8028
1∑ n [ (⃒ ⃒ ⃒ ⃒ ⃒ ⃒ )]
0.5 1.7761 1.7567 1.8548 1.8900 1.8046 ⃒ ⃒ ⃒ ⃒ ⃒ ⃒
D(oi ,ϕ− ) = wj ⃒μ2ξij − μ2ϕ− ⃒+ ⃒ν2ξij − ν2ϕ− ⃒+ ⃒π2ξij − π2ϕ− ⃒ , i = 1,2,…,m.
0.6 1.7778 1.7606 1.8548 1.8900 1.8063 2 j=1 j j j
0.7 1.7794 1.7644 1.8548 1.8900 1.8081

0.8 1.7811 1.7681 1.8548 1.8900 1.8098 (21)
0.9 1.7828 1.7719 1.8548 1.8900 1.8115
1.0 1.7844 1.7756 1.8549 1.8900 1.8131
Step 7: Compute the relative closeness index (CI)
D(oi , ϕ− )
ℂ(oi ) = , i = 1, 2, …, m. (22)
between the proposed and existing PF-TOPSIS model (Zhang and Xu, D(oi , ϕ+ ) + D(oi , ϕ− )
2014) and PF-Weighted Aggregates Sum Product Assessment (WASPAS) Based on the values of CI, the most suitable candidate and the pri
(Rani et al., 2020) for solving MCDM problems under PFSs context as oritization order of all alternatives are determined. The maximum value
follows: of ℂ(ok) determines the most appropriate choice.
Next, we implement the PF-TOPSIS in the above-mentioned case
4.3.1. PF-TOPSIS model study. For this, firstly, we have computed the PF-PIS and PF-NIS by
Steps 1–4: Follow the steps of the PF-TOPSIS method means of Eqs. (18)–(19), and then we have.
Step 5: Calculate the discriminations of each alternative from “PF- ϕ+={(0.751, 0.402, 0.523), (0.621, 0.523, 0.584), (0.729, 0.424,
positive ideal solution (PIS)” and “PF-negative-ideal solution (NIS)”. 0.537), (0.652, 0.493, 0.576), (0.758, 0.398, 0.516), (0.674, 0.476,
In this method, calculating the PF-PIS and PF-NIS values of each 0.565), (0.532, 0.596, 0.601), (0.765, 0.394, 0.510), (0.709, 0.445,
criterion is a key concern for DMs. Let ϕ+ and ϕ− be the PF-PIS and PF- 0.547), (0.700, 0.459, 0.546), (0.679, 0.476, 0.560), (0.697, 0.456,
NIS, respectively, which are computed with the use of Eqs. (20) and (21) 0.554), (0.618, 0.528, 0.583), (0.692, 0.471, 0.547), (0.734, 0.433,
as follows: 0.523), (0.709, 0.445, 0.547), (0.548, 0.583, 0.599), (0.720, 0.437,
{
max μij , for benefit criterion tb 0.539), (0.745, 0.417, 0.521), (0.604, 0.536, 0.590), (0.549, 0.584,
ϕ+ = i
for i = 1, 2, …, m; j = 1, 2, …, n, 0.598), (0.637, 0.526, 0.563), (0.674, 0.476, 0.565), (0.645, 0.513,
min νij , for cost criterion tn
i 0.566)}.
(18) ϕ− ={(0.400, 0.710, 0.579), (0.306, 0.783, 0.542), (0.546, 0.597,
{ 0.588), (0.472, 0.664, 0.580), (0.526, 0.603, 0.599), (0.386, 0.716,
min μij , for benefit criterion tb
ϕ− = i
for i = 1, 2, …, m; j = 1, 2, …, n.
max νij , for cost criterion tn
i
Table 8
(19) Ranking results of the PF-TOPSIS model.
Step 6: Derive the degrees of distances of options from PF-PIS and PF- Options D(oi, ϕ+) D(oi, ϕ− ) ℂ(oi) Ranking
NIS. o1 0.129 0.137 0.514 3
In accordance with Eq. (4), estimate the degree of distance D(oi, ϕ+) o2 0.141 0.125 0.470 4
among the option oi and the PF-PIS ϕ+. o3 0.129 0.142 0.524 2
o4 0.119 0.149 0.557 1
o5 0.143 0.125 0.466 5
Fig. 3. Sensitivity outcomes of the compromise degree over the utility parameter ϑ.
11
0.582), (0.371, 0.711, 0.597), (0.463, 0.674, 0.575), (0.540, 0.595, PIS and PF-NIS could be considered two benchmarks against which
0.595), (0.550, 0.587, 0.594), (0.389, 0.701, 0.597), (0.382, 0.705, the performance of the alternatives on each attribute could be
0.597), (0.495, 0.638, 0.590), (0.571, 0.566, 0.595), (0.546, 0.576, assessed. Remember that for the two above-mentioned benchmarks
0.609), (0.337, 0.733, 0.591), (0.363, 0.715, 0.597), (0.385, 0.715, it is too unrealistic to be achieved practically. On the other hand, it
0.583), (0.362, 0.730, 0.579), (0.406, 0.693, 0.596), (0.433, 0.692, should be noted that the proposed PF-entropy-RS-CoCoSo uses the
0.578), (0.439, 0.671, 0.598), (0.505, 0.619, 0.602), (0.424, 0.700, aggregated compromise algorithm with various aggregation strate
0.574)}. gies in order to achieve a compromise solution. The final ranking
Using Eqs. (20)–(22), the overall computational results and prefer results obtained by the CoCoSo method based on the aggregation of
ence order of the options for the technical challenges of blockchain simple additive weighting (SAW) and weighted product measure
technology to implement in sustainable manufacturing are presented in (WPM) models could be highly reliable and realistic where the DEs
Table 8. Hence, the desirable company option is o4 to the technical could not only know about the best and worst performance of al
challenges of blockchain technology to implement in sustainable ternatives on the defined attributes, but also compare their
manufacturing. The priority order of options is o4 ≻ o3 ≻ o1 ≻ o2 ≻ o5. to performances.
the evaluation of the technical challenges of blockchain technology to
implementation in sustainable manufacturing. 5. Conclusion and outlook
4.3.2. PF-WASPAS model The capability of blockchain technology for recording transactions
The PF-WASPAS method is applied to treat the MCDM problem. The on distributed ledgers can be taken into account as offering new pros
description of PF-WASPAS is given by. pects for the sustainable manufacturing paradigm in the improvement of
Steps 1–6: The same as the aforementioned model transparency, establishment of trust in the public sector, and prevention
Step 7: Obtain the UD of the WASPAS model in the following of fraud. On the other hand, the literature lacks research into blockchain
expression adoption and application in the context of sustainable manufacturing.
Despite the numerous benefits of blockchain technology for sustainable
αi = λ α(1) (2)
i + (1 − λ) αi , i = 1, 2, …, m, (23) manufacturing, the review of the literature revealed the presence of
different challenges in this regard. To analyze, rank and evaluate the
where λ denotes the decision strategy parameter, where λ ∈ [0, 1].
technical challenges of the blockchain technology transformation for the
Step 8: Based on UD ℂi, prioritize the options.
sustainable manufacturing paradigm in the Industry 4.0 era, this study
The UD of WASPAS for each organization for the evaluation of the
introduced an integrated decision-making method using PFSs. In this
technical challenges of blockchain technology to implement in sustain
regard, a fuzzy decision support system based on PF-entropy-RS and PF-
able manufacturing is obtained by Eq. (23) and mentioned in Table 9.
CoCoSo methods called the PF-entropy-RS-CoCoSo is introduced to
The priority order of options is o4 ≻ o3 ≻ o5 ≻ o1 ≻ o2. Thus, the
evaluate the main technical challenges of blockchain technology to
company-IV (o4) option is the best one to evaluate the technical chal
implement in sustainable manufacturing. To rank the technical chal
lenges of the blockchain technology transformation for the sustainable
lenges of the blockchain technology transformation for the sustainable
manufacturing paradigm in the Industry 4.0 era.
manufacturing paradigm in the Industry 4.0 era, the PF-entropy-RS
As a whole, the benefits of the PF-entropy-RS-CoCoSo method over
approach is used to estimate the preference order of different organi
the extant method are given as follows (see Fig. 4):
zations to evaluate the technical challenges of blockchain technology to
be implemented in the sustainable manufacturing; the PF-CoCoSo
I. In the developed method, the subjective weights of attributes are
method is used. To validation of the results of this study, a compari
obtained by the PF-RS method, and the objective weights of criteria
son using the PF-TOPSIS, PF-WSM, PF-WPM, and PF-WASPAS methods
are computed by entropy-based method, whereas in PF-WASPAS,
is conducted.
only objective weights of criteria are obtained by entropy and
The results of the analysis confirmed that the most important diffi
divergence measure-based weighting procedure, and in PF-TOPSIS,
culties in the process of applying blockchain technology to sustainable
the criteria weights are chosen arbitrarily.
manufacturing are attributed to technical challenges. Though, it is not
II. In (Zhang and Xu, 2014), the distance is calculated between the
clear at what level such issues require to be enhanced. As a result, future
overall attribute value of an alternative oi and the PF-IS and the PF-
research should develop blockchain technology standards in which the
AIS to describe the CI of each option on the given attributes. The PF-
design variables are prudently specified based on the manufacturing
firms’ requirements. Furthermore, blockchain, as new technology, is still
Table 9 surrounded by a myriad of propaganda. In the case of issues such as cost-
The UD of options for evaluating the technical challenges of blockchain tech effectiveness, usability, and reliability, there is still a lack of clear
nology to be implemented in the sustainable manufacturing. guidance to evaluate whether blockchain is an appropriate solution to
Options WSM WPM UD (αi) Ranking the problems of sustainable manufacturing systems. For that reason,
α(1)
( )
α(2)
( ) there is a need for an approach to the evaluation of the appropriateness
i (1) i α(2)
of blockchain in this context. Such an approach needs to be based on the
S αi S i
o1 (0.571, 0.4981 (0.534, 0.4629 0.4805 4 definite properties of blockchain-based applications, and also, the public
0.574, 0.600, processes in which these applications could be applied need to be clearly
0.587) 0.596) understood. This will result in the establishment of some design prin
o2 (0.578, 0.5024 (0.521, 0.4486 0.4755 5
0.574, 0.611,
ciples for blockchain applications, which can well consider the techno
0.580) 0.596) logical, contextual, and organizational aspects of these processes.
o3 (0.584, 0.5122 (0.562, 0.4909 0.5016 2 In addition, the literature lacks a general application platform where
0.563, 0.578, issues such as scalability, security, flexibility, reliability, and interop
0.585) 0.591)
erability of blockchain technology for sustainable manufacturing ap
o4 (0.592, 0.5219 (0.570, 0.5002 0.5111 1
0.554, 0.570, plications are taken into account. This shortage has raised the need for
0.585) 0.591) an appropriate design solution at the architecture level concerning the
o5 (0.580, 0.5061 (0.544, 0.4702 0.4882 3 definite requirements of the manufacturing processes. Furthermore,
0.569, 0.596, manufacturing firms require guidance to find the proper solutions to
0.583) 0.591)
their problems to unlock the value of blockchain technology. Therefore,
12
Fig. 4. Comparison of UD of each company with various methods.
we assert that there is a need for the development of reference archi References
tecture to make available architectural guidance for those who work in
manufacturing contexts. In addition, the adoption of blockchain could Abeyratne, S.A., Monfared, R.P., 2016. Blockchain ready manufacturing supply chain
using distributed ledger. Int. J. Res. Eng. Technol. Anal. Strat. Manag. 5 (9), 1–11.
result in organizational transformation, for example, changes to strate Adhikari, A., Winslett, M., 2019. A Hybrid Architecture for Secure Management of
gies, processes, structure, and culture. Such transformation needs the Manufacturing Data in Industry 4.0. 2019 IEEE International Conference on
commitment and cooperation of organizational members to empower Pervasive Computing and Communications Workshops (PerCom Workshops), 11-15
March 2019.
manufacturing firms to enhance the level of their performance or Al-Jaroodi, J., Mohamed, N., 2019. Blockchain in industries: a survey. IEEE Access 7,
efficiency. 36500–36515. https://doi.org/10.1109/ACCESS.2019.2903554.
Furthermore, there is a need for a systemic approach to the socio- Angrish, A., Craver, B., Hasan, M., Starly, B., 2018. A case study for blockchain in
manufacturing: “FabRec”: a prototype for peer-to-peer network of manufacturing
technical implications of the adoption of blockchain-based applica nodes. Procedia Manuf. 26, 1180–1192. https://doi.org/10.1016/j.
tions in the manufacturing sector. With the extensively spread of promfg.2018.07.154.
blockchain technologies, it is necessary to provide an appropriate legal Aslam, J., Saleem, A., Khan, N.T., Kim, Y.B., 2021. Factors influencing blockchain
adoption in supply chain management practices: a study based on the oil industry.
framework in which blockchain could be properly used. Though, any
J. Innov. Knowl. 6 (2), 124–134. https://doi.org/10.1016/j.jik.2021.01.002.
change to governance arrangements and legal frameworks needs to be Atanassov, K.T., 1996. Intuitionistic fuzzy sets. In: Intuitionistic Fuzzy Sets. Springer,
carefully inspected, particularly in an uncertain continually-changing pp. 1–137.
environment. To this end, there is a need for processes through which Barenji, A.V., Li, Z., Wang, W.M., 2018. Blockchain Cloud Manufacturing: Shop Floor
and Machine Level. Smart SysTech 2018; European Conference on Smart Objects,
multiple actors require to get involved in order to reach a balance be Systems and Technologies, 11-15 March 2019.
tween regulatory approaches on the one hand and the opportunities Bonvillian, W.B., 2013. In: Advanced Manufacturing Policies and Paradigms for
offered by the state-of-the-art blockchain technology for the improve Innovation, 342(6163), pp. 1173–1175. https://doi.org/10.1126/science.1242210.
Catalini, C., Gans, J.S., 2020. Some simple economics of the blockchain. Commun. ACM
ment of manufacturing on the other hand. 63 (7), 80–90. https://doi.org/10.1145/3359552.
Chang, S.E., Chen, Y.-C., Lu, M.-F., 2019. Supply chain re-engineering using blockchain
Author statement technology: a case of smart contract based tracking process. Technol. Forecast. Soc.
Chang. 144, 1–11. https://doi.org/10.1016/j.techfore.2019.03.015.
Chen, Y., Bellavitis, C., 2020. Blockchain disruption and decentralized finance: the rise of
All authors who meet authorship criteria are listed as authors, and all decentralized business models. J. Bus. Ventur. Insights 13, e00151. https://doi.org/
authors certify that they have participated sufficiently in the work to 10.1016/j.jbvi.2019.e00151.
Choi, T.-M., 2020. Creating all-win by blockchain technology in supply chains: impacts
take public responsibility for the content, including participation in the of agents’ risk attitudes towards cryptocurrency. J. Oper. Res. Soc. 1–16 https://doi.
concept, design, analysis, writing, or revision of the manuscript. org/10.1080/01605682.2020.1800419.
Furthermore, each author certifies that this manuscript has not been and Choi, T.-M., Wen, X., Sun, X., Chung, S.-H., 2019. The mean-variance approach for global
supply chain risk analysis with air logistics in the blockchain technology era.
will not be submitted to or published in any other publication before its
Transport Res E-Log 127, 178–191. https://doi.org/10.1016/j.tre.2019.05.007.
appearance in the Journal of Technological Forecasting and Social Chung, K., Yoo, H., Choe, D., Jung, H., 2019. Blockchain network based topic mining
Change. process for cognitive manufacturing. Wirel. Pers. Commun. 105 (2), 583–597.
https://doi.org/10.1007/s11277-018-5979-8.
de Sousa Jabbour, A.B.L., Jabbour, C.J.C., Foropon, C., Godinho Filho, M., 2018. When
Data availability titans meet – can industry 4.0 revolutionise the environmentally-sustainable
manufacturing wave? The role of critical success factors. Technol. Forecast. Soc.
Data will be made available on request. Chang. 132, 18–25. https://doi.org/10.1016/j.techfore.2018.01.017.
Delanka-Pedige, H.M.K., Munasinghe-Arachchige, S.P., Abeysiriwardana-Arachchige, I.S.
A., Nirmalakhandan, N., 2021. Wastewater infrastructure for sustainable cities:
assessment based on UN sustainable development goals (SDGs). Int J Sust Dev World
28 (3), 203–209. https://doi.org/10.1080/13504509.2020.1795006.
13
Deveci, M., Pamucar, D., Gokasar, I., 2021. Fuzzy Power Heronian function based Matt, D.T., Rauch, E., Dallasega, P., 2015. Trends towards distributed manufacturing
CoCoSo method for the advantage prioritization of autonomous vehicles in real-time systems and modern forms for their design. Procedia CIRP 33, 185–190. https://doi.
traffic management. Sustain. Cities Soc. 69, 102846. org/10.1016/j.procir.2015.06.034.
Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Luo, Z., Wamba, S.F., Moldavska, A., Welo, T., 2017. The concept of sustainable manufacturing and its
Roubaud, D., 2019. Can big data and predictive analytics improve social and definitions: a content-analysis based literature review. J. Clean. Prod. 166, 744–755.
environmental sustainability? Technol. Forecast. Soc. Chang. 144, 534–545. https:// https://doi.org/10.1016/j.jclepro.2017.08.006.
doi.org/10.1016/j.techfore.2017.06.020. Mondragon, A.E.C., Mondragon, C.E.C., Coronado, E.S., 2018. Exploring the applicability
Esmaeilian, B., Sarkis, J., Lewis, K., Behdad, S., 2020. Blockchain for the future of of blockchain technology to enhance manufacturing supply chains in the composite
sustainable supply chain management in industry 4.0. Resour. Conserv. Recycl. 163, materials industry, 13-17 April 2018. In: 2018 IEEE International Conference on
105064 https://doi.org/10.1016/j.resconrec.2020.105064. Applied System Invention (ICASI).
Fu, B., Shu, Z., Liu, X., 2018. In: Blockchain Enhanced Emission Trading Framework in Montiel, I., Cuervo-Cazurra, A., Park, J., Antolín-López, R., Husted, B.W., 2021.
Fashion Apparel Manufacturing Industry, 10(4), p. 1105. https://www.mdpi.com/ Implementing the United Nations’ sustainable development goals in international
2071-1050/10/4/1105. business. J. Int. Bus. Stud. 52 (5), 999–1030. https://doi.org/10.1057/s41267-021-
Geiger, S., Schall, D., Meixner, S., Egger, A., 2019. Process traceability in distributed 00445-y.
manufacturing using blockchains. In: Proceedings of the 34th ACM/SIGAPP Mougayar, W., 2016. The Business Blockchain: Promise, Practice, and Application of the
Symposium on Applied Computing, 8, pp. 417–420. Next Internet Technology. John Wiley & Sons.
Ghobakhloo, M., 2018. The future of manufacturing industry: a strategic roadmap Mourtzis, D., Doukas, M., 2012. Decentralized manufacturing systems review: challenges
toward industry 4.0. J. Manuf. Technol. Manag. 29 (6), 910–936. https://doi.org/ and outlook. Logist. Res. 5 (3), 113–121. https://doi.org/10.1007/s12159-012-
10.1108/JMTM-02-2018-0057. 0085-x.
Giungato, P., Rana, R., Tarabella, A., Tricase, C., 2017. In: Current Trends in Nakamoto, S., 2008. Bitcoin: a peer-to-peer electronic cash system. Decentralized Bus.
Sustainability of Bitcoins and Related Blockchain Technology, 9(12), p. 2214. Rev. 21260.
https://www.mdpi.com/2071-1050/9/12/2214. Narayanamoorthy, S., Annapoorani, V., Kang, D., Baleanu, D., Jeon, J., Kureethara, J.V.,
Gong, X., 2018. Collaborative-crowdsourcing Product Fulfillment for Open Design and Ramya, L., 2020. A novel assessment of bio-medical waste disposal methods using
Manufacturing. Georgia Institute of Technology. Master Thesis. integrating weighting approach and hesitant fuzzy MOOSRA. J. Clean. Prod. 275,
Guaita Martínez, J.M., Carracedo, P., Gorgues Comas, D., Siemens, C.H., 2022. An 122587.
analysis of the blockchain and COVID-19 research landscape using a bibliometric Narayanaswami, C., Nooyi, R., Govindaswamy, S.R., Viswanathan, R., 2019. Blockchain
study. Sustain. Technol. Entrep. 1 (1), 100006 https://doi.org/10.1016/j. anchored supply chain automation. IBM J. Res. Dev. 63 (2/3), 11. https://doi.org/
stae.2022.100006. 10.1147/JRD.2019.2900655, 7: 1 –7.
Hallam, J.C., Mallela, N., Lonsdale, G., Drake, L.C., Valiveti, S.M.K., Aquino, E.P., Ngai, E.W.T., Chau, D.C.K., Poon, J.K.L., To, C.K.M., 2013. Energy and utility
Sant’Anna, L., 2021. U.S. Patent No. 11,037,142. U.S. Patent and Trademark Office, management maturity model for sustainable manufacturing process. Int. J. Prod.
Washington, DC. Econ. 146 (2), 453–464. https://doi.org/10.1016/j.ijpe.2012.12.018.
Haraguchi, N., Cheng, C.F.C., Smeets, E., 2017. The importance of manufacturing in Papakostas, N., Newell, A., Hargaden, V., 2019. A novel paradigm for managing the
economic development: has this changed? World Dev. 93, 293–315. https://doi.org/ product development process utilising blockchain technology principles. CIRP Ann.
10.1016/j.worlddev.2016.12.013. 68 (1), 137–140. https://doi.org/10.1016/j.cirp.2019.04.039.
Jackson, S.A., Gopalakrishna-Remani, V., Mishra, R., Napier, R., 2016. Examining the Peng, X., Yang, Y., 2016. Pythagorean fuzzy choquet integral based MABAC method for
impact of design for environment and the mediating effect of quality management multiple attribute group decision making. Int. J. Intell. Syst. 31 (10), 989–1020.
innovation on firm performance. Int. J. Prod. Econ. 173, 142–152. https://doi.org/ Queiroz, M.M., Telles, R., Bonilla, S.H., 2020. Blockchain and supply chain management
10.1016/j.ijpe.2015.12.009. integration: a systematic review of the literature. Supply Chain Manag. 25 (2),
Jiang, P., Leng, J., Ding, K., Gu, P., Koren, Y., 2016. Social manufacturing as a 241–254. https://doi.org/10.1108/SCM-03-2018-0143.
sustainable paradigm for mass individualization. Proc. Inst. Mech. Eng. B J. Eng. Rani, P., Mishra, A.R., Pardasani, K.R., Mardani, A., Liao, H., Streimikiene, D., 2019.
Manuf. 230 (10), 1961–1968. https://doi.org/10.1177/0954405416666903. A novel VIKOR approach based on entropy and divergence measures of pythagorean
Kang, M., Um, K.-H., Wang, S., Park, K., Colclough, S.N., Park, Y., 2021. Integrating fuzzy sets to evaluate renewable energy technologies in India [Article]. J. Clean.
manufacturing and R&D functions for better quality and product development Prod. 238, 117936 https://doi.org/10.1016/j.jclepro.2019.117936.
performance. J. Manuf. Technol. Manag. https://doi.org/10.1108/JMTM-02-2021- Rani, P., Mishra, A.R., Pardasani, K.R., 2020. A novel WASPAS approach for multi-
0060 ahead-of-print(ahead-of-print). criteria physician selection problem with intuitionistic fuzzy type-2 sets. Soft.
Khaqqi, K.N., Sikorski, J.J., Hadinoto, K., Kraft, M., 2018. Incorporating seller/buyer Comput. 24 (3), 2355–2367. https://doi.org/10.1007/s00500-019-04065-5.
reputation-based system in blockchain-enabled emission trading application. Appl. Ryan, R., Donohue, M., 2017. Securities on blockchain. Bus. Lawyer 73 (1), 85–108.
Energy 209, 8–19. https://doi.org/10.1016/j.apenergy.2017.10.070. https://www.jstor.org/stable/26419192.
Ko, T., Lee, J., Ryu, D., 2018. In: Blockchain Technology and Manufacturing Industry: Saberi, S., Kouhizadeh, M., Sarkis, J., Shen, L., 2019. Blockchain technology and its
Real-Time Transparency and Cost Savings, 10(11), p. 4274. https://www.mdpi. relationships to sustainable supply chain management. Int. J. Prod. Res. 57 (7),
com/2071-1050/10/11/4274. 2117–2135. https://doi.org/10.1080/00207543.2018.1533261.
Korpela, K., Hallikas, J., Dahlberg, T., 2017. Digital supply chain transformation toward Schumacher, A., Erol, S., Sihn, W., 2016. A maturity model for assessing industry 4.0
blockchain integration. In: Proceedings of the 50th Hawaii International Conference readiness and maturity of manufacturing enterprises. Procedia CIRP 52, 161–166.
on System Sciences. https://doi.org/10.1016/j.procir.2016.07.040.
Lee, J., Azamfar, M., Singh, J., 2019. A blockchain enabled cyber-physical system Şeref, B., Gökhan, İ., 2018. Blockchain-based Framework for Customer Loyalty Program.
architecture for industry 4.0 manufacturing systems. Manuf. Lett. 20, 34–39. https:// 2018 3rd International Conference on Computer Science and Engineering (UBMK),
doi.org/10.1016/j.mfglet.2019.05.003. 20-23 Sept. 2018.
Leng, J., Jiang, P., Ding, K., 2014. Implementing of a three-phase integrated decision Sharma, P.K., Kumar, N., Park, J.H., 2019. Blockchain-based distributed framework for
support model for parts machining outsourcing. Int. J. Prod. Res. 52 (12), automotive industry in a Smart City. IEEE Trans. Ind. Inf. 15 (7), 4197–4205.
3614–3636. https://doi.org/10.1080/00207543.2013.879344. https://doi.org/10.1109/TII.2018.2887101.
Leng, J., Jiang, P., Xu, K., Liu, Q., Zhao, J.L., Bian, Y., Shi, R., 2019. Makerchain: a Sharma, R., Jabbour, C.J.C., de Sousa, Lopes, Jabbour, A.B., 2020. Sustainable
blockchain with chemical signature for self-organizing process in social manufacturing and industry 4.0: what we know and what we don’t. J. Enterp. Inf.
manufacturing. J. Clean. Prod. 234, 767–778. https://doi.org/10.1016/j. Manag. 34 (1), 230–266. https://doi.org/10.1108/JEIM-01-2020-0024.
jclepro.2019.06.265. Siemieniuch, C.E., Sinclair, M.A., Henshaw, M.J.D., 2015. Global drivers, sustainable
Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., Liu, C., 2020. Blockchain- manufacturing and systems ergonomics. Appl. Ergon. 51, 104–119. https://doi.org/
empowered sustainable manufacturing and product lifecycle management in 10.1016/j.apergo.2015.04.018.
industry 4.0: a survey. Renew. Sust. Energ. Rev. 132, 110112 https://doi.org/ Stillwell, W.G., Seaver, D.A., Edwards, W., 1981. A comparison of weight approximation
10.1016/j.rser.2020.110112. techniques in multiattribute utility decision making. Organ. Behav. Hum. Perform.
Leng, J., Ye, S., Zhou, M., Zhao, J.L., Liu, Q., Guo, W., Cao, W., Fu, L., 2021. Blockchain- 28 (1), 62–77. https://doi.org/10.1016/0030-5073(81)90015-5.
secured smart manufacturing in industry 4.0: a survey. IEEE Trans. Syst. Man Sun, Y., Shahzad, M., Razzaq, A., 2022. Sustainable organizational performance through
Cybern. Syst. Hum. 51 (1), 237–252. https://doi.org/10.1109/TSMC.2020.3040789. blockchain technology adoption and knowledge management in China. J. Innov.
Li, Z., Liu, X., Wang, W.M., Vatankhah Barenji, A., Huang, G.Q., 2019. CKshare: secured Knowl. 7 (4), 100247 https://doi.org/10.1016/j.jik.2022.100247.
cloud-based knowledge-sharing blockchain for injection mold redesign. Enterp. Inf. Sundarakani, B., Ajaykumar, A., Gunasekaran, A., 2021. Big data driven supply chain
Syst. 13 (1), 1–33. design and applications for blockchain: an action research using case study
Li, Z., Wang, W.M., Liu, G., Liu, L., He, J., Huang, G.Q., 2018. Toward open approach. Omega 102, 102452. https://doi.org/10.1016/j.omega.2021.102452.
manufacturing. Ind. Manag. Data Syst. 118 (1), 303–320. https://doi.org/10.1108/ Tanco, M., Kalemkerian, F., Santos, J., 2021. Main challenges involved in the adoption of
IMDS-04-2017-0142. sustainable manufacturing in uruguayan small and medium sized companies.
Li, K., Zhou, T., Liu, B.-H., 2020. Internet-based intelligent and sustainable J. Clean. Prod. 293, 126139 https://doi.org/10.1016/j.jclepro.2021.126139.
manufacturing: developments and challenges. Int. J. Adv. Manuf. Technol. 108 (5), Teh, D., Khan, T., Corbitt, B., Ong, C.E., 2020. Sustainability strategy and blockchain-
1767–1791. https://doi.org/10.1007/s00170-020-05445-0. enabled life cycle assessment: a focus on materials industry. Environ. Syst. Decis. 40
Liu, J., Jiang, P., Leng, J., 2017. A framework of credit assurance mechanism for (4), 605–622. https://doi.org/10.1007/s10669-020-09761-4.
manufacturing services under social manufacturing context, 20-23 Aug. 2017. In: Tiscini, R., Testarmata, S., Ciaburri, M., Ferrari, E., 2020. The blockchain as a sustainable
2017 13th IEEE Conference on Automation Science and Engineering (CASE). business model innovation. Manag. Decis. 58 (8), 1621–1642. https://doi.org/
Marconi, N., Reis, C.F.D.B., Araújo, E.C.D., 2016. Manufacturing and economic 10.1108/MD-09-2019-1281.
development: the actuality of Kaldor’s first and second laws. Struct. Chang. Econ.
Dyn. 37, 75–89. https://doi.org/10.1016/j.strueco.2015.12.002.
14
Torkayesh, A.E., Pamucar, D., Ecer, F., Chatterjee, P., 2021. An integrated BWM-LBWA- Lijun Zhang was born in Hebei, China, in 1981. From 2000 to
CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio 2005, she studied in Shijiazhuang University of Economics and
Econ. Plan. Sci., 101052 https://doi.org/10.1016/j.seps.2021.101052. received the bachelor’s degree in 2005. Since 2006, she worked
Viriyasitavat, W., Da Xu, L., Bi, Z., Sapsomboon, A., 2020. Blockchain-based business in Hebei Geo University. From 2009 to 2013, she studied in
process management (BPM) framework for service composition in industry 4.0. Capital University of Economics and Business and received her
J. Intell. Manuf. 31 (7), 1737–1748. https://doi.org/10.1007/s10845-018-1422-y. Master’s degree in 2013. From 2017 to 2019, she studied in
Westerkamp, M., Victor, F., Küpper, A., 2020. Tracing manufacturing processes using Wonkwang University in Korea and received her Doctor’s de
blockchain-based token compositions. Digit. Commun. Netw. 6 (2), 167–176. gree in 2019. >10 papers have been published home and
https://doi.org/10.1016/j.dcan.2019.01.007. abroad, one of which has been indexed by SCI. Her research
Xu, X., Lu, Q., Liu, Y., Zhu, L., Yao, H., Vasilakos, A.V., 2019. Designing blockchain-based interests are mainly included blockchain and auditing theory
applications a case study for imported product traceability. Futur. Gener. Comput. and practice. Email: zljj14619.hgu.edu@gmail.com;
Syst. 92, 399–406. https://doi.org/10.1016/j.future.2018.10.010. zljj14619@hgu.edu.cn
Yager, R.R., 2013. Pythagorean fuzzy subsets, 24-28 June 2013. In: 2013 Joint IFSA
World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).
Yager, R.R., 2014. Pythagorean membership grades in multicriteria decision making.
IEEE Trans. Fuzzy Syst. 22 (4), 958–965. https://doi.org/10.1109/
TFUZZ.2013.2278989. Hua Peng was born in Gansu, China,in 1983. From 2003 to
Yazdani, M., Kahraman, C., Zarate, P., Onar, S.C., 2019. A fuzzy multi attribute decision 2007, she studied in Lanzhou Jiaotong University and received
framework with integration of QFD and grey relational analysis. Expert Syst. Appl. her bachelor’s degree in 2007. From 2014 to 2017, she studied
115, 474–485. https://doi.org/10.1016/j.eswa.2018.08.017. in Wuhan University of Technology and received her Master’s
Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., Turskis, Z., 2019. A combined degree in 2017. She has published a total of 7 papers, Obtained
compromise solution (CoCoSo) method for multi-criteria decision-making problems. 2 invention patents and 4 utility model patents, Reached a
Manag. Decis. 57 (9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458. number of school enterprise cooperation projects, Presided
Yermack, D., 2017. Corporate governance and blockchains*. Rev. Financ. 21 (1), 7–31. over 5 vertical subject, Cooperate with >40 enterprises to carry
https://doi.org/10.1093/rof/rfw074. %J Review of Finance. out horizontal projects, Deputy editor in chief 2 books, Guide
Yoo, M., Won, Y., 2018. In: A Study on the Transparent Price Tracing System in Supply students to participate in provincial and municipal competi
Chain Management Based on Blockchain, 10(11), p. 4037. https://www.mdpi. tions and win 4 awards. Her research interests are included
com/2071-1050/10/11/4037. Marketing, enterprise management, supply chain management.
Zareiyan, B., Korjani, M., 2018. Blockchain technology for global decentralized Email: penghua1007@163.com.
manufacturing: challenges and solutions for supply chain in fourth industrial
revolution. Int. J. Adv. Robot. Autom. 3, 1–10.
Zhang, X., Xu, Z., 2014. Extension of TOPSIS to multiple criteria decision making with
pythagorean fuzzy sets. Int. J. Intell. Syst. 29 (12), 1061–1078. https://doi.org/ Parvaneh Saeidi received the Ph.D. degree in Management
10.1002/int.21676. from Universiti Teknologi Malaysia in 2016. Her doctoral
Zhang, H., Li, S., Yan, W., Jiang, Z., Wei, W., 2019. A knowledge sharing framework for research focused on enterprise risk management and intellec
green supply chain management based on blockchain and edge computing. In: tual capital. She currently works as a researcher at the Uni
International Conference on Sustainable Design and Manufacturing. Springer, Singapore, versidad Tecnológica Indoamérica, in the Department of
pp. 413–420. Administrative and Economic Sciences. Her primary research
Zhang, H., Veltri, A., Calvo-Amodio, J., Haapala, K.R., 2021. Making the business case interest is in enterprise risk management and sustainability,
for sustainable manufacturing in small and medium-sized manufacturing enterprises: Fuzzy Systems. Email: Parvanehsaeidi@uti.edu.ec
a systems decision making approach. J. Clean. Prod. 287, 125038 https://doi.org/
10.1016/j.jclepro.2020.125038.
Zhao, J.L., Fan, S., Yan, J., 2016. Overview of business innovations and research
opportunities in blockchain and introduction to the special issue. Financ. Innov. 2
(1), 28. https://doi.org/10.1186/s40854-016-0049-2.
Zheng, K., Zheng, L.J., Gauthier, J., Zhou, L., Xu, Y., Behl, A., Zhang, J.Z., 2022.
Blockchain technology for enterprise credit information sharing in supply chain
finance. J. Innov. Knowl. 7 (4), 100256 https://doi.org/10.1016/j.jik.2022.100256.
Erfan Babaee Tirkolaee is currently an assistant professor of
the Department of Industrial Engineering at Istinye University
Dan Su was born in He Nan, China, in 1986.From 2010 to in Istanbul, Turkey. His research interests mainly include Waste
2013, she studied in He Nan Normal university and received Management, Supply Chain Management and Transportation/
her Master’s degree in 2013. Currently, she studyingfor her Routing Problems, Operations Research, Fuzzy Systems, Solu
Doctor’s degree in School of management, Tianjin university of tion Algorithms, Artificial Intelligence. Email: erfan.babaee
technology. e-mail:ctssudan@126.com @istinye.edu.tr
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Technological Forecasting & Social Change 188 (2023) 122275

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

Technical challenges of blockchain technology for sustainable

1. Introduction species of craft production and continues to mass production, to multi-

Twinning blockchain with other systems (C24)

Balance the complexity and security (C23)

Consensus efficiency (C22) Manufacturing

Lifecycle, performance expectancy (C20)

Responsibilities and roles (C19)

Risk management and fraud detection (C18) Manufacturing

Quantum attacks (C16)

Effective self-adaptive adulteration (C15)

System resiliency (C12)

Black box effect and inefficiency (C11)

Integrating the multi-platform services (C9)

Privacy protection (C8)

Computing on encrypted black data (C7) Manufacturing

Massive erroneous incorrect and useless data (C5)

Decentralized intelligence (C4)

New business framework and model (C3)

Scalability and tolerance of fault (C2)

Retrieval of encrypted data (C1)

∑ℓ Step 5: Create the “normalized A-q-ROF-DM (NA-q-ROF-DM)”

thoughts and intrinsic values of decision-makers. In the decision-making ( ) ( )

4. Results and discussion Table 2

In the current part of the study, we present a comparative study

c1 MH M M MH (0.537, 0.583, 0.610) 0.474 10 0.0500

0.7 1.7794 1.7644 1.8548 1.8900 1.8081

Fig. 4. Comparison of UD of each company with various methods.

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