Evaluation of Blockchain Technology for Supply Chains using an Integrated Fuzzy Cognitive Map-QFD Methodology

Year 2024, Volume: 10 Issue: 2, 252 - 271, 25.06.2024

Ayça Maden , Emre Alptekin

https://doi.org/10.28979/jarnas.1337409

Abstract

The rapid advancement of technology has made it imperative for supply chains to adapt to the changing landscape. Blockchain technology holds immense potential to transform supply chain processes, but the challenge lies in identifying the most suitable blockchain characteristics to meet the various performance indicators of a supply chain. To overcome this challenge, this study aims to prioritize the most critical blockchain characteristics in a supply chain. The study adopts a two-stage Quality Function Deployment (QFD) methodology to rank blockchain characteristics based on supply chain and software requirements. The methodology evaluates the supply chain performance indicators using the Supply Chain Operations Reference (SCOR) model and software needs using the International Organization for Standardization (ISO) software quality characteristics. After determining the problematic SCOR and ISO software-related metrics, the study utilizes the QFD Stage 1 to obtain the weights of ISO software characteristics and employs the Fuzzy Cognitive Map (FCM) to determine the most crucial blockchain characteristics for QFD Stage 2. The results of this study show that the top priorities for blockchain characteristics in a supply chain are smart contract functionality, privacy, transaction per second, tokenization, security, permissioned network, scalability, cost, modularity, and licensing, in order of importance.

Keywords

Blockchain, fuzzy cognitive map, fuzzy QFD, supply chain management

References

• Y. D. Hwang, Y. C. Lin, J. Lyu Jr, The performance evaluation of SCOR sourcing process—The case study of Taiwan’s TFT-LCD industry, International Journal of Production Economics 115 (2) (2008) 411–423.
• I. Bashir, Mastering Blockchain, Packt Publishing, Birmingham, 2017.
• J. Mendling, I. Weber, W. V. D. Aalst, J. V. Brocke, C. Cabanillas, F. Daniel, S. Debois, C. D. Ciccio, M. Dumas, S. Dustdar, A. Gal, L. García-Bañuelos, G. Governatori, R. Hull, M. L. Rosa, H. Leopold, F. Leymann, J. Recker, M. Reichert, H. A. Reijers, S. Rinderle-Ma, A. Solti, M. Rosemann, S. Schulte, M. P. Singh, T. Slaats, M. Staples, B. Weber, M. Weidlich, M. Weske, X. Xu, L. Zhu, Blockchains for business process management-challenges and opportunities, ACM Transactions on Management Information Systems (TMIS) 9 (1) (2018) 1–16.
• F. Milani, L. Garcia-Banuelos, Blockchain and principles of business process re-engineering for process innovation (2018) 15 pages, https://doi.org/10.48550/arXiv.1806.03054.
• H. Nakai, N. Tsuda, K. Honda, H. Washizaki, Y. Fukazawa, A SQuaRE-based software quality evaluation framework and its case study, in: A. Alphones, R. Gupta, M. Ong (Eds.), IEEE Region 10 Conference (TENCON), Singapore, 2016, pp. 3704–3707.
• ISO/IEC 25010, Systems and software engineering – Systems and software quality requirements and evaluation (SQuaRE) – System and software quality models, 2011.
• ISO/IEC 25012, Systems and software quality requirements and evaluation (SQuaRE) – Data quality model, 2008.
• C. Bai, J. Sarkis, A supply chain transparency and sustainability technology appraisal model for blockchain technology, International Journal of Production Research 58 (7) (2020) 2142–2162.
• S. Yousefi, B. M. Tosarkani, An analytical approach for evaluating the impact of blockchain technology on sustainable supply chain performance, International Journal of Production Economics 246 (2022) 108429.
• V. S. Yadav, A. R. Singh, R. D. Raut, N. Cheikhrouhou, Blockchain drivers to achieve sustainable food security in the Indian context, Annals of Operations Research 327 (2023) 211–249.
• K. Zkik, A. Belhadi, S. A. Rehman Khan, S. S. Kamble, M. Oudani, F. E. Touriki, Exploration of barriers and enablers of blockchain adoption for sustainable performance: implications for e-enabled agriculture supply chains, International Journal of Logistics Research and Applications 26 (11) (2023) 1498–1535.
• F. Zhang, W. Song, Sustainability risk assessment of blockchain adoption in the sustainable supply chain: An integrated method, Computers & Industrial Engineering 171 (2022) 108378.
• S. Stranieri, F. Riccardi, M. P. Meuwissen, C. Soregaroli, Exploring the impact of blockchain on the performance of agri-food supply chains, Food Control 119 (2021) 107495.
• N. Kshetri, 1 Blockchain’s roles in meeting key supply chain management objectives, International Journal of Information Management 39 (2018) 80–89.
• Q. Zhu, M. Kouhizadeh, Blockchain technology, supply chain information, and strategic product deletion management, IEEE Engineering Management Review 47 (1) (2019) 36–44.
• S. S. Kamble, A. Gunasekaran, R. Sharma, Modeling the blockchain enabled traceability in agriculture supply chain, International Journal of Information Management 52 (2020) 101967.
• K. Korpela, J. Hallikas, T. Dahlberg, Digital supply chain transformation toward blockchain integration, Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii, 2017, 4182–4191.
• I. Erol, I. M. Ar, I. Peker, Scrutinizing blockchain applicability in sustainable supply chains through an integrated fuzzy multi-criteria decision-making framework, Applied Soft Computing 116 (2022) 108331.
• X. Xu, I. Weber, M. Staples, L. Zhu, J. Bosch, L. Bass, C. Pautasso, P. Rimba, A taxonomy of blockchain-based systems for architecture design, 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, 2017, 243–252.
• K. Karuppiah, B. Sankaranarayanan, S. M. Ali, A decision-aid model for evaluating challenges to blockchain adoption in supply chains, International Journal of Logistics Research and Applications 26 (3) (2023) 257–278.
• I. Erol, I. M. Ar, I. Peker, C. Searcy, Alleviating the impact of the Barriers to circular economy adoption through blockchain: An investigation using an integrated MCDM-based QFD with hesitant fuzzy linguistic term sets, Computers & Industrial Engineering 165 (2022) 107962.
• A. A. Mukherjee, R. K. Singh, R. Mishra, S. Bag, Application of blockchain technology for sustainability development in agricultural supply chain: Justification framework, Operations Management Research 15 (2022) 46-61.
• C. Ozturk, A. Yildizbasi, Barriers to implementation of blockchain into supply chain management using an integrated multi-criteria decision-making method: A numerical example, Soft Computing 24 (19) (2020) 14771-14789.
• F. Longo, L. Nicoletti, A. Padovano, G. d’Atri, M. Forte, Blockchain-enabled supply chain: An experimental study, Computers & Industrial Engineering 136 (2019) 57–69.
• X. Han, P. Rani, Evaluate the barriers of blockchain technology adoption in sustainable supply chain management in the manufacturing sector using a novel Pythagorean fuzzy-CRITIC-CoCoSo approach, Operations Management Research 15 (3-4) (2022) 725–742.
• A. Budak, V. Çoban, Evaluation of the impact of blockchain technology on supply chain using cognitive maps, Expert Systems with Applications 184 (2021) 115455.
• S. Khan, M. K. Kaushik, R. Kumar, W. Khan, Investigating the barriers of blockchain technology integrated food supply chain: A BWM approach, Benchmarking: An International Journal 30 (3) (2022) 713–735.
• S. Dua, M. G. Sharma, V. Mishra, S. D. Kulkarni, Modelling perceived risk in blockchain-enabled supply chain utilizing fuzzy-AHP, Journal of Global Operations and Strategic Sourcing 16 (1) (2023) 161–177.
• M. Irannezhad, S. Shokouhyar, S. Ahmadi, E. I. Papageorgiou, An integrated FCM-FBWM approach to assess and manage the readiness for blockchain incorporation in the supply chain, Applied Soft Computing 112 (2021) 107832.
• G. Büyüközkan, G. Tüfekçi, D. Uztürk, Evaluating Blockchain requirements for effective digital supply chain management, International Journal of Production Economics 242 (2021) 108309.
• A. Maden, E. Alptekin, Evaluation of factors affecting the decision to adopt blockchain technology: A logistics company case study using Fuzzy DEMATEL, Journal of Intelligent & Fuzzy Systems 39 (5) (2020) 6279–6291.
• I. J. Orji, S. Kusi-Sarpong, S. Huang, D. Vazquez-Brust, Evaluating the factors that influence blockchain adoption in the freight logistics industry, Transportation Research Part E: Logistics and Transportation Review 141 (2020) 102025.
• M. Zarour, M. T. J. Ansari, M. Alenezi, A. K. Sarkar, M. Faizan, A. Agrawal, R. Kumar, R. A. Khan, Evaluating the impact of blockchain models for secure and trustworthy electronic healthcare records, IEEE Access 8 (2020) 157959–157973.
• H. Tang, Y. Shi, P. Dong, Public blockchain evaluation using entropy and TOPSIS, Expert Systems with Applications 117 (2019) 204–210.
• M. Garriga, S. Dalla Palma, M. Arias, A. De Renzis, R. Pareschi, D. Andrew Tamburri, Blockchain and cryptocurrencies: A classification and comparison of architecture drivers, Concurrency and Computation: Practice and Experience 33 (8) (2021) e5992.
• S. Farshidi, S. Jansen, S. España, J. Verkleij, Decision support for blockchain platform selection: Three industry case studies, IEEE Transactions on Engineering Management 67 (4) (2020) 1109–1128.
• D. Mingxiao, M. Xiaofeng, Z. Zhe, W. Xiangwei, C. Qijun, A review on consensus algorithm of blockchain, in: A. Basu (Ed.), IEEE international conference on Systems, Man, and Cybernetics (SMC), Banff, 2017, 2567–2572.
• E. Bellini, Y. Iraqi, E. Damiani, Blockchain-based distributed trust and reputation management systems: A survey, IEEE Access 8 (2020) 21127–21151.
• C. Fan, S. Ghaemi, H. Khazaei, P. Musilek, Performance evaluation of blockchain systems: A systematic survey, IEEE Access 8 (2020) 126927–126950.
• S. K. Lo, X. Xu, Y. K. Chiam, Q. Lu, Evaluating suitability of applying blockchain, in: J. Zhao, B. Xu (Eds.), 22nd International Conference on Engineering of Complex Computer Systems (ICECCS), Fukuoka, 2017, 158–161.
• T. Lyons, L. Courcelas, Blockchain Use Cases in Health Care (2020), https://blockchain-observatory.ec.europa.eu/publications/blockchain-use-cases-healthcare_en, Accessed 3 Aug 2023.
• J. J. Hunhevicz, D. M. Hall, Do you need a blockchain in construction? Use case categories and decision framework for DLT design options, Advanced Engineering Informatics 45 (2020) 101094.
• S. Raval, Decentralized applications: Harnessing Bitcoin’s blockchain technology, O’Reilly Media, Inc, 2016.
• A. Shahaab, B. Lidgey, C. Hewage, I. Khan, Applicability and appropriateness of distributed ledgers consensus protocols in public and private sectors: A systematic review, IEEE Access 7 (2019) 43622-43636.
• S. Voshmgir, Token economy: How blockchains and smart contracts revolutionize the economy, BlockchainHub Berlin, 2019.
• H. Y. Paik, X. Xu, H. D. Bandara, S. U. Lee, S. K. Lo, Analysis of data management in blockchain-based systems: From architecture to governance, IEEE Access 7 (2019) 186091-186107.
• X. Xu, C. Pautasso, L. Zhu, V. Gramoli, A. Ponomarev, A. B. Tran, S. Chen, The blockchain as a software connector, 13th Working IEEE/IFIP Conference on Software Architecture (WICSA), Venice, 2016, 182–191.
• Y. Xiao, N. Zhang, W. Lou, Y. T. Hou, A survey of distributed consensus protocols for blockchain networks, IEEE Communications Surveys & Tutorials 22 (2) (2020) 1432–1465.
• S. M. H. Bamakan, A. Motavali, A. B. Bondarti, A survey of blockchain consensus algorithms performance evaluation criteria, Expert Systems with Applications 154 (2020) 113385.
• J. Zou, B. Ye, L. Qu, Y. Wang, M. A. Orgun, L. Li, A proof-of-trust consensus protocol for enhancing accountability in crowdsourcing services, IEEE Transactions on Services Computing 12 (3) (2018) 429–445.
• S. Nanayakkara, M. N. N. Rodrigo, S. Perera, G. T. Weerasuriya, A. A. Hijazi, A methodology for selection of a Blockchain platform to develop an enterprise system, Journal of Industrial Information Integration 23 (2021) 100215.
• W. Viriyasitavat, D. Hoonsopon, Blockchain characteristics and consensus in modern business processes, Journal of Industrial Information Integration 13 (2019) 32–39.
• B. Xu, D. Luthra, Z. Cole, N. Blakely, EOS: An Architectural, Performance, and Economic Analysis (2018), https://blog.bitmex.com/wp-content/uploads/2018/11/eos-test-report.pdf, Accessed 3 Aug 2023.
• S. J. Alsunaidi, F. A. Alhaidari, A survey of consensus algorithms for blockchain technology, International Conference on Computer and Information Sciences (ICCIS), Aljouf, 2019, 1–6.
• M. Gord, J. Holek, Blockchain for Enterprises (2019), https://www.whatmatrix.com/comparison/Blockchain-for-Enterprise, Accessed 3 Aug 2023.
• APICS. Supply Chain Operations Reference Model (Revision 11.0) (2015), https://docs.huihoo.com/scm/supply-chain-operations-reference-model-r11.0.pdf, Accessed 3 Aug 2023.
• J. C. Cheng, K. H. Law, H. Bjornsson, A. Jones, R. D. Sriram, Modeling and monitoring of construction supply chains, Advanced Engineering Informatics 24 (4) (2010) 435–455.
• J. A. McCall, P. K. Richards, G. F. Walters, Factors in Software Quality (RADC TR-77-369) (1977), https://apps.dtic.mil/sti/pdfs/ADA049014.pdf, Accessed 3 Aug 2023.
• B. W. Boehm, Characteristics of software quality, North-Holland Publishing Company, 1978.
• R. B. Grady, Practical software metrics for project management and process improvement, Prentice-Hall, Englewood Cliffs, 1992.
• R. G. Dromey, Cornering the chimera [software quality], IEEE Software 13 (1) (1996) 33–43.
• C. Temponi, J. Yen, W. A. Tiao, House of quality: A fuzzy logic-based requirements analysis, European Journal of Operational Research 117 (2) (1999) 340–354.
• E. Haktanır, C. Kahraman, A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development, Computers & Industrial Engineering 132 (2019) 361–372.
• S. Ç. Onar, G. Büyüközkan, B. Öztayşi, C. Kahraman, A new hesitant fuzzy QFD approach: An application to computer workstation selection, Applied Soft Computing 46 (2016) 1–16.
• N. O. Erdil, O. M. Arani, Quality function deployment: More than a design tool, International Journal of Quality and Service Sciences 11 (2) (2019) 142–166.
• Z. Iqbal, N. P. Grigg, Enhancing voice of customer prioritisation in QFD by integrating the competitor matrix, International Journal of Productivity and Performance Management 70 (1) (2020) 217–229.
• P. G. Brown, QFD: Echoing the voice of the customer, At&T Technical Journal 70 (2) (1991) 18–32.
• E. I. Papageorgiou, D. K. Iakovidis, Towards the construction of intuitionistic fuzzy cognitive maps for medical decision making, 9th International Conference on Information Technology and Applications in Biomedicine, Cyprus, 2009, 1–4.
• B. Kosko, Fuzzy cognitive maps, International Journal of Man-Machine Studies 24 (1) (1986) 65–75.
• S. Shokouhyar, N. Pahlevani, F. M. M. Sadeghi, Scenario analysis of smart, sustainable supply chain on the basis of a fuzzy cognitive map, Management Research Review 43 (4) (2019) 463–496.
• A. Baykasoğlu, İ. Gölcük, Alpha-cut based fuzzy cognitive maps with applications in decision-making, Computers & Industrial Engineering 152 (2021) 107007.
• A. Jetter, W. Schweinfort, Building scenarios with fuzzy cognitive maps: An exploratory study of solar energy, Futures 43 (1) (2011) 52–66.
• W. Homenda, A. Jastrzebska, W. Pedrycz, Time series modeling with fuzzy cognitive maps: Simplification strategies. IFIP International Conference on Computer Information Systems and Industrial Management, Springer, Berlin, Heidelberg, 2015, 409–420.
• FCMapper, FCMapper - Fuzzy Cognitive Mapping Software Solution, http://www.fcmappers.net/joomla/, Accessed 3 Aug 2023.