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Big data-driven supply chain performance measurement system: a review and framework for implementation

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  • Sachin S. Kamble
  • Angappa Gunasekaran

Abstract

Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The PMM improves the firm’s performance by providing open and transparent communication between the various stakeholders of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance of the big data analytics capability and the second category represents 130 measures used for assessing the performance of BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement system is proposed which will guide the managers to have a robust performance measurement system in their organisation.

Suggested Citation

  • Sachin S. Kamble & Angappa Gunasekaran, 2020. "Big data-driven supply chain performance measurement system: a review and framework for implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 65-86, January.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:1:p:65-86
    DOI: 10.1080/00207543.2019.1630770
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    Cited by:

    1. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    2. Huynh, Minh-Tay & Nippa, Michael & Aichner, Thomas, 2023. "Big data analytics capabilities: Patchwork or progress? A systematic review of the status quo and implications for future research," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    3. Sheshadri Chatterjee & Ranjan Chaudhuri & Sachin Kamble & Shivam Gupta & Uthayasankar Sivarajah, 2023. "Adoption of Artificial Intelligence and Cutting-Edge Technologies for Production System Sustainability: A Moderator-Mediation Analysis," Information Systems Frontiers, Springer, vol. 25(5), pages 1779-1794, October.
    4. Amit Kumar Gupta & Harshit Goyal, 2021. "Framework for implementing big data analytics in Indian manufacturing: ISM-MICMAC and Fuzzy-AHP approach," Information Technology and Management, Springer, vol. 22(3), pages 207-229, September.
    5. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Giannakis, Mihalis & Foropon, Cyril, 2023. "Data-driven digital transformation and the implications for antifragility in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 266(C).
    6. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    7. Kamble, Sachin & Rana, Nripendra P. & Gupta, Shivam & Belhadi, Amine & Sharma, Rohit & Kulkarni, Praveen, 2023. "An effectuation and causation perspective on the role of design thinking practices and digital capabilities in platform-based ventures," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    8. Pan, Qiaohong & Luo, Wenping & Fu, Yi, 2022. "A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China," Technology in Society, Elsevier, vol. 71(C).
    9. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    10. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Chan, Hau-Ling & Bryde, David J., 2022. "The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events," International Journal of Production Economics, Elsevier, vol. 251(C).
    11. Sivarajah, Uthayasankar & Kumar, Sachin & Kumar, Vinod & Chatterjee, Sheshadri & Li, Jing, 2024. "A study on big data analytics and innovation: From technological and business cycle perspectives," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    12. Syed Abdul Rehman Khan & Manoj Mathew & P. D. D. Dominic & Muhammad Umar, 2022. "Evaluation and selection strategy for green supply chain using interval-valued q-rung orthopair fuzzy combinative distance-based assessment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 10633-10665, September.
    13. Cosa, Marcello & Urban, Boris, 2023. "A systematic review of performance measurement systems and their relevance to social enterprises," OSF Preprints 6ft2p, Center for Open Science.
    14. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    15. Belhadi, Amine & Kamble, Sachin & Jabbour, Charbel Jose Chiappetta & Gunasekaran, Angappa & Ndubisi, Nelson Oly & Venkatesh, Mani, 2021. "Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    16. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    17. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Kamble, Sachin S. & Belhadi, Amine & Gunasekaran, Angappa & Ganapathy, L. & Verma, Surabhi, 2021. "A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    19. Zhao, Guoqing & Xie, Xiaotian & Wang, Yi & Liu, Shaofeng & Jones, Paul & Lopez, Carmen, 2024. "Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    20. Sharma, Rohit & Jain, Geetika & Paul, Justin, 2023. "Does the world need to change its vaccine distribution strategy for COVID-19?," Technovation, Elsevier, vol. 126(C).
    21. Zbysław Dobrowolski, 2021. "Internet of Things and Other E-Solutions in Supply Chain Management May Generate Threats in the Energy Sector—The Quest for Preventive Measures," Energies, MDPI, vol. 14(17), pages 1-11, August.

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