A Sustainable Healthcare Supply Chain Model Based on Big Data Analytics, Lean Operations, and Integration

Document Type : Research Paper

Authors

1 MSc, Department of Operations Management and Information Technology, Faculty of Management, Kharazmi University, Tehran, Iran.

2 Assistant Prof., Department of Operations Management and Information Technology, Faculty of Management, Kharazmi University, Tehran, Iran.

10.22059/imj.2025.388084.1008218

Abstract

Objective: In recent years, big data analytics (BDA) technologies have garnered increasing attention from researchers. However, limited empirical research has explored the benefits of BDA in supply chain integration and lean operations and its influence on sustainable performance in the healthcare sector. To address this gap, the research aims to design and present a conceptual model to investigate the relationships among supply chain integration, lean operations, sustainable supply chain performance, and BDA capabilities.
Methods: This research adopts a survey-based approach, using an online questionnaire to collect data from 104 public and private hospitals in Iran. Data analysis was conducted using structural equation modeling (SEM) via the Partial Least Squares Method (PLS-SEM). 
Results: The results revealed that BDA capabilities directly improve sustainable supply chain performance. Moreover, lean operations and supply chain integration mediate between BDA capabilities and sustainable performance. It was also found that BDA capabilities enhance both lean operations and supply chain integration, with supply chain integration directly impacting lean operations. These findings suggest that BDA capabilities can be leveraged as a key enabler to strengthen lean operations, improve supply chain integration, and achieve sustainable supply chain performance.
Conclusion: While some literature has addressed various aspects of supply chain digitalization, no prior research has specifically examined the potential impacts of BDA on sustainable and lean supply chain performance within the healthcare sector. The results offer meaningful contributions for academic researchers interested in the topic, business professionals specializing in digital supply chain management and sustainable operations, healthcare organizations, and any stakeholders seeking to better understand the influence of BDA on sustainable operations and overall business performance.

Keywords

References

Alshahrani, S., Rahman, S., & Chan, C. (2018). Hospital-supplier integration and hospital performance: evidence from Saudi Arabia. The international journal of logistics management, 29(1), 22–45.‏ https://doi.org/10.1108/IJLM-12-2016-0287
Benzidia, S., Bentahar, O., Husson, J., & Makaoui, N. (2024). Big data analytics capability in healthcare operations and supply chain management: The role of green process innovation. Annals of Operations Research, 333(2), 1077-1101.‏ https://doi.org/10.1007/s10479-022-05157-6
Bag, S., Yadav, G., Wood, L. C., Dhamija, P., & Joshi, S. (2020). Industry 4.0 and the circular economy: Resource melioration in logistics. Resources Policy, 68, 101776.‏ https://doi.org/10.1016/j.resourpol.2020.101776
Bagozzi, R. P., Yi, Y., & Nassen, K. D. (1998). Representation of measurement error in marketing variables: Review of approaches and extension to three-facet designs. Journal of Econometrics, 89(1-2), 393–421.‏ https://doi.org/10.1016/S0304-4076(98)00068-2
Bergenwall, A. L., Chen, C., & White, R. E. (2012). TPS's process design in American automotive plants and its effects on the triple bottom line and sustainability. International Journal of Production Economics, 140(1), 374–384.‏ https://doi.org/10.1016/j.ijpe.2012.04.016
Belhadi, A., Kamble, S. S., Zkik, K., Cherrafi, A., & Touriki, F. E. (2020). The integrated effect of Big Data Analytics, Lean Six Sigma, and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa. Journal of Cleaner Production, 252, 119903.‏ https://doi.org/10.1016/j.jclepro.2019.119903
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological forecasting and social change, 165, 120557.‏ https://doi.org/10.1016/j.techfore.2020.120557
Bicheno, J., & Holweg, M. (2008). The Lean toolbox: The essential guide to Lean transformation. Picsie Books.‏ https://orca.cardiff.ac.uk/id/eprint/25628
Castaldi, M., Sugano, D., Kreps, K., Cassidy, A., & Kaban, J. (2016). Lean philosophy and the public hospital. Perioperative care and operating room management, 3, 25–28. https://doi.org/10.1016/j.pcorm.2016.05.006
Castillo, V. E., Mollenkopf, D. A., Bell, J. E., & Bozdogan, H. (2018). Supply chain integrity: A key to sustainable supply chain management. Journal of Business Logistics, 39(1), 38–56. https://doi.org/10.1111/jbl.12176
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward a new theory. International journal of physical distribution & logistics management, 38(5), 360–387.‏ https://doi.org/10.1108/09600030810882816
Chen, D. Q., Preston, D. S., & Xia, W. (2013). Enhancing hospital supply chain performance: A relational view and empirical test. Journal of Operations Management, 31(6), 391–408.‏ https://doi.org/10.1016/j.jom.2013.07.012
Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of Operations Management, 22(2), 119–150.‏ https://doi.org/10.1016/j.jom.2003.12.007
De Vries, J., & Huijsman, R. (2011). Supply chain management in health services: an overview. Supply chain management: An international journal, 16(3), 159-165.‏ https://doi.org/10.1108/13598541111127146
De Souza, L. B. (2009). Trends and approaches in lean healthcare. Leadership in health services, 22(2), 121–139.‏ https://doi.org/10.1108/17511870910953788
Demirdöğen, G., Işık, Z., & Arayici, Y. (2020). Lean management framework for healthcare facilities integrating BIM, BEPS, and big data analytics. Sustainability, 12(17), 7061.‏ https://doi.org/10.3390/su12177061
Dobrzykowski, D. D., & Tarafdar, M. (2015). Understanding information exchange in healthcare operations: Evidence from hospitals and patients. Journal of Operations Management, 36, 201-214.‏ https://doi.org/10.1016/j.jom.2014.12.003
Drupsteen, J., van der Vaart, T., & Pieter van Donk, D. (2013). Integrative practices in hospitals and their impact on patient flow. International Journal of Operations & Production Management, 33(7), 912-933.‏ https://doi.org/10.1108/IJOPM-12-2011-0487
Davidson, R. K., Antunes, W., Madslien, E. H., Belenguer, J., Gerevini, M., Torroba Pérez, T., & Prugger, R. (2017). From food defence to food supply chain integrity. British Food Journal, 119(1), 52–66. https://doi.org/10.1108/BFJ-04-2016-0138
Drupsteen, J., van der Vaart, T., & Van Donk, D. P. (2016). Operational antecedents of integrated patient planning in hospitals. International Journal of Operations & Production Management, 36(8), 879-900.‏ https://doi.org/10.1108/IJOPM-05-2014-0237
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: integrating institutional theory, resource‐based view, and big data culture. British Journal of Management, 30(2), 341–361.‏ https://doi.org/10.1111/1467-8551.12355
Duque-Uribe, V., Sarache, W., & Gutiérrez, E. V. (2019). Sustainable supply chain management practices and sustainable performance in hospitals: a systematic review and integrative. Sustainability, 11(21), 5949. https://doi.org/10.3390/su11215949
Farhadi, F., Taghizadeh Yazdi, M. R., Momeni, M., & Sajadi, S. M. (2018). Providing Sustainable Supply Chain Agility Model in the Brick Industry of Isfahan Province. Industrial Management Journal, 10(3), 335-352.‏ (in Persian) https://doi.org/10.22059/imj.2018.261444.1007459
Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71.‏ https://doi.org/10.1016/j.jom.2009.06.001
Fayyaz, A., Liu, C., Xu, Y., Khan, F., & Ahmed, S. (2024). Untangling the cumulative impact of big data analytics, green lean six sigma, and sustainable supply chain management on the economic performance of manufacturing organisations. Production Planning & Control, 1–18.‏ https://doi.org/10.1080/09537287.2024.2348517
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.‏ https://doi.org/10.1177/002224378101800104
Garrison, L. P. (2013). Universal health coverage—big thinking versus big data. Value in Health, 16(1), S1-S3.‏ http://dx.doi.org/10.1016/j.jval.2012.10.016
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.‏ https://doi.org/10.1016/j.jbusres.2016.08.004
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed, a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.‏ https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the academy of marketing science, 45, 616-632.‏ https://doi.org/10.1007/s11747-017-0517-x
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.‏ https://doi.org/10.1108/EBR-11-2018-0203
Hulland, J., Baumgartner, H., & Smith, K. M. (2018). Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46, 92–108.‏ https://doi.org/10.1007/s11747-017-0532-y
Izadyar, M., Toloie-Eshlaghy, A., & Seyed Hosseini, S. M. (2020). A model of sustainability performance assessment of LARG supply chain management practices in the automotive supply chain using system dynamics. Industrial Management Journal, 12(1), 111–142.‏ (in Persian) https://doi.org/10.22059/imj.2020.281292.1007594
Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513–538.‏ https://doi.org/10.1108/IJLM-05-2017-0134
Kock, N. (2019). From composites to factors: Bridging the gap between PLS and covariance‐based structural equation modelling. Information Systems Journal, 29(3), 674-706.‏ https://doi.org/10.1111/isj.12228
Kline, R. B. (1998). Software review: Software programs for structural equation modeling: Amos, EQS, and LISREL. Journal of psychoeducational assessment, 16(4), 343–364.‏ https://doi.org/10.1177/073428299801600407
Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International journal of operations & production management, 37(1), 10-36. https://doi.org/10.1108/IJOPM-02-2015-0078
Khan, S. A. R., Zkik, K., Belhadi, A., & Kamble, S. S. (2021). Evaluating barriers and solutions for social sustainability adoption in multi-tier supply chains. International Journal of Production Research, 59(11), 3378–3397.‏ https://doi.org/10.1080/00207543.2021.1876271
Kuo, M. H., Sahama, T., Kushniruk, A. W., Borycki, E. M., & Grunwell, D. K. (2014). Health big data analytics: current perspectives, challenges, and potential solutions. International Journal of Big Data Intelligence, 1(1-2), 114-126.‏ https://doi.org/10.1504/IJBDI.2014.063835
Moshtari, M. (2016). Inter‐organizational fit, relationship management capability, and collaborative performance within a humanitarian setting. Production and Operations Management, 25(9), 1542–1557.‏ https://doi.org/10.1111/poms.12568
Mesquita, L. L., Lizarelli, F. L., & Duarte, S. (2025). Big data analytics and lean practices: impact on sustainability performance. Production Planning & Control, 36(3), 333–356.‏ https://doi.org/10.1080/09537287.2023.2267512
Moyano‐Fuentes, J., & Sacristán‐Díaz, M. (2012). Learning on lean: a review of thinking and research. International Journal of Operations & Production Management, 32(5), 551–582.‏ https://doi.org/10.1108/01443571211226498
Novais, L., Maqueira Marin, J. M., & Moyano-Fuentes, J. (2020). Lean production implementation, cloud-supported logistics, and supply chain integration: interrelationships and effects on business performance. The International Journal of Logistics Management, 31(3), 629–663.‏ https://doi.org/10.1108/IJLM-02-2019-0052
Nyaga, G. N., Young, G. J., & Zepeda, E. D. (2015). An analysis of the effects of intra‐and interorganizational arrangements on hospital supply chain efficiency. Journal of Business Logistics, 36(4), 340-354.‏ https://doi.org/10.1111/jbl.12109
Novicka, J. (2025). Unpacking the Role of Big Data Analytics Capability in Sustainable Business Performance: Insights from Digital Sustainability Reporting Readiness in Latvia. Sustainability, 17(8), 3666.‏ https://doi.org/10.3390/su17083666
Narayanamurthy, G., & Gurumurthy, A. (2018). Is the hospital lean? A mathematical model for assessing the implementation of lean thinking in healthcare institutions. Operations Research for Health Care, 18, 84–98. https://doi.org/10.1016/j.orhc.2017.05.002
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108–1118.‏ https://doi.org/10.1016/j.jclepro.2016.03.059
Putnik, G. D., & Putnik, Z. (2012). Lean vs Agile in the context of complexity management in organizations. The Learning Organization, 19(3), 248-266.‏ https://doi.org/10.1108/09696471211220046
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879–891.‏ https://doi.org/10.3758/BRM.40.3.879
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.‏ https://doi.org/10.1177/014920638601200408
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2, 1–10.‏ https://doi.org/10.1186/2047-2501-2-3
Régis, T. K. O., Santos, L. C., & Gohr, C. F. (2019). A case-based methodology for lean implementation in hospital operations. Journal of health organization and management, 33(6), 656–676. https://doi.org/10.1108/JHOM-09-2018-0267
Shokouhyar, S., Seddigh, M. R., & Panahifar, F. (2020). Impact of big data analytics capabilities on supply chain sustainability: A case study of Iran. World Journal of Science, Technology and Sustainable Development, 17(1), 33–57.‏ https://doi.org/10.1108/WJSTSD-06-2019-0031
Shahhoseini, M. A., Javaheri Shalmani, S. F., Hasangholipor Yasory, T., & Rostami, A. (2019). Evaluating and comparing key indicators of sustainable development performance in the petrochemical industry using SMAA and SMAA-S. Industrial Management Journal, 11(2), 273–302.‏ (in Persian) https://doi.org/10.22059/imj.2019.280703.1007589
Sajan, M.P., & Shalij, P.R. (2021). A multicase study approach in Indian manufacturing SMEs to investigate the effect of Lean manufacturing practices on sustainability performance. International Journal of Lean Six Sigma, 12(3), 579-606.‏ https://doi.org/10.1108/IJLSS-04-2020-0044
Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867.‏ https://doi.org/10.1111/poms.12746
Suifan, T., Alazab, M., & Alhyari, S. (2019). Trade-off among lean, agile, resilient, and green paradigms: an empirical study on the pharmaceutical industry in Jordan using a TOPSIS-entropy method. International Journal of Advanced Operations Management, 11(1-2), 69–101.‏ https://doi.org/10.1504/IJAOM.2019.098493
Tambuskar, D. P., Jain, P., & Narwane, V. S. (2024). An exploration into the factors influencing the implementation of big data analytics in sustainable supply chain management. Kybernetes, 53(5), 1710-1739. https://doi.org/10.1108/K-07-2022-1057
Tetteh, F. K., Nyamekye, B., Attah, J., Williams, E., Awumah, E. K., & Degbe, F. D. (2025a). Understanding when and how supply chain analytics, strategies, and desorptive capacity enhance healthcare supply chain performance. The International Journal of Logistics Management.‏ https://doi.org/10.1108/IJLM-08-2024-0501
Wadmann, S., & Hoeyer, K. (2018). Dangers of the digital fit: Rethinking seamlessness and social sustainability in data-intensive healthcare. Big Data & Society, 5(1), 2053951717752964. https://doi.org/10.1177/2053951717752964 ‏
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3-13.‏ https://doi.org/10.1016/j.techfore.2015.12.019
Wong, C. Y., Boon-Itt, S., & Wong, C. W. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operations Management, 29(6), 604-615.‏ https://doi.org/10.1016/j.jom.2011.01.003
Yang, M. G. M., Hong, P., & Modi, S. B. (2011). Impact of lean manufacturing and environmental management on business performance: An empirical study of manufacturing firms. International Journal of Production Economics, 129(2), 251-261.‏ https://doi.org/10.1016/j.ijpe.2010.10.017
Yu, W., Zhao, G., Liu, Q., & Song, Y. (2021). Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective. Technological Forecasting and Social Change, 163, 120417.‏ https://doi.org/10.1016/j.techfore.2020.120417
Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytics approach. Journal of Cleaner Production, 142, 1085–1097.‏ https://doi.org/10.1016/j.jclepro.2016.03.006
Kuo, M. H., Chrimes, D., Moa, B., & Hu, W. (2015, December). Design and construction of a big data analytics framework for health applications. In 2015, IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) (pp. 631–636). IEEE.‏ https://doi.org/10.1109/SmartCity.2015.140
Zhu, Q., Johnson, S., & Sarkis, J. (2018, January). Lean six sigma and environmental sustainability: a hospital perspective. In Supply Chain Forum: An International Journal (Vol. 19, No. 1, pp. 25–41). Taylor & Francis.‏ https://doi.org/10.1080/16258312.2018.1426339
Singh, T., Poulose, J., & Sharma, V. (2025, March). Data-Driven Sustainability: Revolutionizing Hospital Supply Chains through Big Data Analytics. In Operations Research Forum (Vol. 6, No. 1, pp. 1–32). Springer International Publishing.‏ https://doi.org/10.1007/s43069-025-00425-0
Assaad, A. S., & Sadek Kanaan, S. (2025, June). Supply chain integration based on big data, Internet of Things, marketing intelligence, and knowledge sharing. Study the SNOWA company in Iran. In Supply Chain Forum: An International Journal (pp. 1–17). Taylor & Francis.‏ https://doi.org/10.1080/16258312.2025.2513214
 
Industrial Management Journal
Volume 17, Issue 3
2025
Pages 56-89

Files

Share

How to cite

Statistics