10.57647/ijm2c.2027.1702.13

Blockchain-Calibrated Fuzzy Network DEA for Integrity-Aware Efficiency Evaluation in Pharmaceutical Supply Chains

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  • Rahim Morammazi Asl1,
  • Fatemeh Mehregan*2,
  • Reza Tayebi Khorami1,
  • Jafar Esmaily1,
  • Mahmood Alizadeh1,
  1. Department of Mathematics, Ahv.C., Islamic Azad University, Ahvaz, Iran
  2. Department of Mathematics, ARV.C., Islamic Azad University, Abadan , Iran

Received: 27-01-2026

Revised: 09-06-2026

Accepted: 09-07-2026

Published Online: 15-07-2026

Copyright (c) 2025 Rahim Morammazi Asl, Fatemeh Mehregan, Reza Tayebi Khorami, Jafar Esmaily, Mahmood Alizadeh (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Morammazi Asl, R., Mehregan, F., Tayebi Khorami, R., Esmaily, J., & Alizadeh, M. (2025). Blockchain-Calibrated Fuzzy Network DEA for Integrity-Aware Efficiency Evaluation in Pharmaceutical Supply Chains. International Journal of Mathematical Modelling & Computations. https://doi.org/10.57647/ijm2c.2027.1702.13
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Abstract

Data-driven efficiency benchmarking of pharmaceutical supply chains can be systematically biased when data integrity varies across firms and routes (e.g., missingness and timestamp drift), because measurement uncertainty becomes endogenous and non-uniform, distorting DEA frontiers and weakening external alignment. We propose a Blockchain-Enhanced Fuzzy Network DEA (Blockchain-FNDEA) framework in which IoT anomaly events are immutably logged on a permissioned blockchain and aggregated into an auditable weighted transparency index . This integrity signal is embedded via a monotone contraction mapping that calibrates the spreads of triangular fuzzy numbers while preserving their mode values so transparency affects only uncertainty widths and does not proxy true operational performance. For each α-cut (α ∈ [0,1]), the resulting linear programs evaluate a three-stage network (procurement–manufacturing–distribution). The approach is applied to 84 DMUs from 28 EFPIA-affiliated firms (2021–2023). Relative to deterministic and static-fuzzy baselines, Blockchain-FNDEA yields higher frontier selectivity (Gini 0.26→0.36; Theil 0.19→0.29) and stronger external alignment with financial outcomes, raising the Spearman rank association between overall efficiency and ROA from ρ=0.412 to ρ=0.740. Stage decomposition identifies downstream distribution as the dominant bottleneck (mean stage-3 efficiency ≈0.771). Field evidence from 12 cold-chain routes (Jan–Jun 2023) separately validates the blockchain–IoT data-integrity infrastructure, reporting lower missingness (−78%), fewer timestamp errors (−93%), reduced product losses (−75%, ≈€256 per shipment, raw mean difference from Table 4; DID-adjusted estimate: −€248.3 per shipment, Supplementary Table S1), and faster data recovery (45.0→0.2 min). These results support the use of auditable integrity signals to calibrate fuzzy uncertainty rather than merely shrinking fuzzy bounds uniformly, with stable rankings across α-levels.

Keywords

  • Blockchain,
  • Fuzzy Network DEA,
  • Data Integrity,
  • Internet of Things,
  • Pharmaceutical Supply Chain,
  • Performance Evaluation
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References

  1. World Health Organization. Substandard and falsified medical products [Fact sheet]. 2024 [cited 2026 Jul 13]. Available from: https://www.who.int/news-room/fact-sheets/detail/substandard-and-falsified-medical-products
  2. Mangala N, Naveen DR, Reddy BE, Buyya R, Venugopal KR, Iyengar SS, et al. Secure pharmaceutical supply chain using blockchain in IoT cloud systems. Internet of Things. 2024;26:101215. https://doi.org/10.1016/j.iot.2024.101215
  3. Abdallah S, Nizamuddin N. Blockchain-based solution for Pharma supply chain industry. Computers & Industrial Engineering. 2023;177:108997. https://doi.org/10.1016/j.cie.2023.108997
  4. Aivazi MA, Rasekh HR, Peikanpour M, Peiravian F, Esmaeili S, Zarei L. The COVID-19 pandemic and the resilience of the pharmaceutical supply chain: lessons from past experiences and strategies for the future. Iranian Journal of Pharmaceutical Research: IJPR. 2024;23(1):e152723. https://doi.org/10.5812/ijpr-152723
  5. World Health Organization. WHO global surveillance and monitoring system for substandard and falsified medical products. 2017 [cited 2026 Jul 13]. Available from: https://www.who.int/publications/i/item/9789241513425
  6. European Medicines Agency, Heads of Medicines Agencies. HMA-EMA joint report on COVID-19 lessons learned (EMA/269282/2023). 2023 [cited 2026 Jul 13]. Available from: https://www.ema.europa.eu/en/documents/report/covid-19-lessons-learned-joint-report-response-public-health-emergency_en.pdf
  7. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research. 1978;2(6):429–44. https://doi.org/10.1016/0377-2217(78)90138-8
  8. Färe R, Grosskopf S. Network DEA. Socio-Economic Planning Sciences. 2000;34(1):35–49. https://doi.org/10.1016/S0038-0121(99)00012-9
  9. Kao C. Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research. 2009;192(3):949–62. https://doi.org/10.1016/j.ejor.2007.10.008
  10. Hatami-Marbini A, Emrouznejad A, Tavana M. A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making. European Journal of Operational Research. 2011;214(3):457–72. https://doi.org/10.1016/j.ejor.2011.02.016
  11. Emrouznejad A, Tavana M, editors. Performance measurement with fuzzy data envelopment analysis. Berlin: Springer; 2014. https://doi.org/10.1007/978-3-642-41372-8
  12. Pourbabagol H, Amiri M, Taghavifard MT, Hanafizadeh P. A new fuzzy DEA network based on possibility and necessity measures for agile supply chain performance evaluation: A case study. Expert Systems with Applications. 2023;220:119552. https://doi.org/10.1016/j.eswa.2023.119552
  13. Keshavarz Ghorabaee M, Amiri M, Zavadskas EK, Antucheviciene J. Supplier evaluation and selection in fuzzy environments: A review of MADM approaches. Economic Research-Ekonomska Istraživanja. 2017;30(1):1073–118. https://doi.org/10.1080/1331677X.2017.1314828
  14. Tavana M, Khalili-Damghani K, Santos-Arteaga FJ, Hosseini A. A fuzzy multi-objective multi-period network DEA model for efficiency measurement in oil refineries. Computers & Industrial Engineering. 2019;135:143–55. https://doi.org/10.1016/j.cie.2019.05.033
  15. Ben-Daya M, Hassini E, Bahroun Z. Internet of things and supply chain management: A literature review. International Journal of Production Research. 2019;57(15-16):4719–42. https://doi.org/10.1080/00207543.2017.1402140
  16. Saberi S, Kouhizadeh M, Sarkis J, Shen L. Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research. 2019;57(7):2117–35. https://doi.org/10.1080/00207543.2018.1533261
  17. Kamble SS, Gunasekaran A, Sharma R. Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management. 2020;52:101967. https://doi.org/10.1016/j.ijinfomgt.2019.05.023
  18. Toorajipour R, Sohrabpour V, Nazarpour A, Oghazi P, Fischl M. Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research. 2021;122:502–17. https://doi.org/10.1016/j.jbusres.2020.09.009
  19. Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science. 1984;30(9):1078–92. https://doi.org/10.1287/mnsc.30.9.1078
  20. Azadi M, Yousefi S, Farzipoor Saen R, Shabanpour H, Jabeen F. Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis. Journal of Business Research. 2023;154:113357. https://doi.org/10.1016/j.jbusres.2022.113357
  21. Kao C, Hwang SN. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research. 2008;185(1):418–29. https://doi.org/10.1016/j.ejor.2006.11.041
  22. Sengupta JK. A fuzzy systems approach in data envelopment analysis. Computers & Mathematics with Applications. 1992;24(8-9):259–66. https://doi.org/10.1016/0898-1221(92)90203-T
  23. Lertworasirikul S, Fang SC, Joines JA, Nuttle HLW. Fuzzy data envelopment analysis (DEA): A possibility approach. Fuzzy Sets and Systems. 2003;139(2):379–94. https://doi.org/10.1016/S0165-0114(02)00484-0
  24. Ben-Tal A, Nemirovski A. Robust convex optimization. Mathematics of Operations Research. 1998;23(4):769–805. https://doi.org/10.1287/moor.23.4.769
  25. Ben-Tal A, Nemirovski A. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming. 2000;88:411–24. https://doi.org/10.1007/PL00011380
  26. Bertsimas D, Sim M. The price of robustness. Operations Research. 2004;52(1):35–53. https://doi.org/10.1287/opre.1030.0105
  27. Soyster AL. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research. 1973;21(5):1154–7. https://doi.org/10.1287/opre.21.5.1154
  28. Mulvey JM, Vanderbei RJ, Zenios SA. Robust optimization of large-scale systems. Operations Research. 1995;43(2):264–81. https://doi.org/10.1287/opre.43.2.264
  29. Delage E, Ye Y. Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research. 2010;58(3):595–612. https://doi.org/10.1287/opre.1090.0741
  30. Hastig GM, Sodhi MS. Blockchain for supply chain traceability: Business requirements and critical success factors. Production and Operations Management. 2020;29(4):935–54. https://doi.org/10.1111/poms.13147
  31. Kshetri N. Blockchain and sustainable supply chain management in developing countries. International Journal of Information Management. 2021;60:102376. https://doi.org/10.1016/j.ijinfomgt.2021.102376
  32. Nanda SK, Panda SK, Dash M. Medical supply chain integrated with blockchain and IoT to track the logistics of medical products. Multimedia Tools and Applications. 2023;82(21):32917–39. https://doi.org/10.1007/s11042-023-14846-8
  33. Mackey TK, Nayyar G. A review of existing and emerging digital technologies to combat the global trade in fake medicines. Expert Opinion on Drug Safety. 2017;16(5):587–602. https://doi.org/10.1080/14740338.2017.1313227
  34. Wong WP, Saw PS, Jumtanachai S, Wong LS, Ong HF, Lim CP. Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach. Scientific Reports. 2023;13:22287. https://doi.org/10.1038/s41598-023-49606-z
  35. Razavian SB, Tirkolaee EB, Simic V, Ali SS, Görçün ÖF. Blockchain-enabled healthcare supply chain management: Identification and analysis of barriers and solutions based on improved zero-sum hesitant fuzzy game theory. Engineering Applications of Artificial Intelligence. 2025;154:110991. https://doi.org/10.1016/j.engappai.2025.110991
  36. Choi TM, Wallace SW, Wang Y. Big data analytics in operations management. Production and Operations Management. 2018;27(10):1868–83. https://doi.org/10.1111/poms.12838
  37. Abdelhamid MM, Sliman L, Ben Djemaa R. AI-enhanced blockchain for scalable IoT-based supply chain. Logistics. 2024;8(4):109. https://doi.org/10.3390/logistics8040109
  38. Land KC, Lovell CAK, Thore S. Chance-constrained data envelopment analysis. Managerial and Decision Economics. 1993;14(6):541–54. https://doi.org/10.1002/mde.4090140607
  39. Cooper WW, Park KS, Yu G. IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Management Science. 1999;45(4):597–607. https://doi.org/10.1287/mnsc.45.4.597
  40. Tsionas EG. Combining DEA and stochastic frontier models: An empirical Bayes approach. European Journal of Operational Research. 2001;147(3):499–510. https://doi.org/10.1016/S0377-2217(01)00295-X
  41. Peykani P, Mohammadi E, Emrouznejad A, Pishvaee MS, Rostamy-Malkhalifeh M. Fuzzy data envelopment analysis: An adjustable approach. Expert Systems with Applications. 2022;136:439–52. https://doi.org/10.1016/j.eswa.2019.06.039

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