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A note on big data analytics capability development in supply chain

Author

Listed:
  • Ashish Kumar Jha

    (Trinity Business School - Trinity College Dublin)

  • Maher Agi

    (ESC [Rennes] - ESC Rennes School of Business)

  • Eric W.T. Ngai

    (POLYU - The Hong Kong Polytechnic University [Hong Kong])

Abstract

Big data analytics (BDA) are gaining importance in all aspects of business management. This is driven by both the presence of large-scale data and management's desire to root decisions in data. Extant research demonstrates that supply chain and operations management functions are among the biggest sources and users of data in the company. Therefore, their decision-making processes would benefit from increased use of BDA technologies. However, there is still a lack of understanding of what determines a company's ability to build BDA capability to gain a competitive advantage. In this study, we attempt to answer this fundamental question by identifying the factors that assist a company in or inhibit it from building its BDA capability and maximizing its gains through BDA technologies. We base our findings on a qualitative analysis of data collected from field visits, interviews with senior management, and secondary resources. We find that, in addition to technical capacity, competitive landscape and intra-firm power dynamics play an important role in building BDA capability and using BDA technologies.g

Suggested Citation

  • Ashish Kumar Jha & Maher Agi & Eric W.T. Ngai, 2020. "A note on big data analytics capability development in supply chain," Post-Print hal-03164004, HAL.
    • Handle: RePEc:hal:journl:hal-03164004
    DOI: 10.1016/j.dss.2020.113382
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    Citations

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    Cited by:

    1. Rajesh Chidananda Reddy & Biplab Bhattacharjee & Debasisha Mishra & Anandadeep Mandal, 2022. "A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy," Information Systems and e-Business Management, Springer, vol. 20(1), pages 223-255, March.
    2. Joash Mageto, 2021. "Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    3. Tiwari, Sunil & Sharma, Pankaj & Jha, Ashish Kumar, 2024. "Digitalization & Covid-19: An institutional-contingency theoretic analysis of supply chain digitalization," International Journal of Production Economics, Elsevier, vol. 267(C).
    4. 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).
    5. Behera, Rajat Kumar & Bala, Pradip Kumar & Rana, Nripendra P. & Irani, Zahir, 2023. "Responsible natural language processing: A principlist framework for social benefits," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Md Ahsan Uddin Murad & Dilek Cetindamar & Subrata Chakraborty, 2022. "Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    7. 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).
    8. Ka Chung Ng & Ping Fan Ke & Mike K. P. So & Kar Yan Tam, 2023. "Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2101-2122, July.
    9. Manupati, V.K. & Schoenherr, Tobias & Ramkumar, M. & Panigrahi, Suraj & Sharma, Yash & Mishra, Prakriti, 2022. "Recovery strategies for a disrupted supply chain network: Leveraging blockchain technology in pre- and post-disruption scenarios," International Journal of Production Economics, Elsevier, vol. 245(C).
    10. Luminița Hurbean & Florin Militaru & Mihaela Muntean & Doina Danaiata, 2023. "The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 70(SI), pages 43-54, February.
    11. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).
    12. 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).

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