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Business Analytics For Supply Chain: A Dynamic-Capabilities Framework

Author

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  • BONGSUG KEVIN CHAE

    (Department of Management, Kansas State University, Manhattan, KS 66506, USA)

  • DAVID L. OLSON

    (Department of Management, University of Nebraska-Lincoln, Lincoln, NE 68588-0491, USA)

Abstract

Supply chain management has become more important as an academic topic due to trends in globalization leading to massive reallocation of production related advantages. Because of the massive amount of data that is generated in the global economy, new tools need to be developed in order to manage and analyze the data, as well as to monitor organizational performance worldwide. This paper proposes a framework of business analytics for supply chain analytics (SCA) asIT-enabled, analytical dynamic capabilitiescomposed of data management capability, analytical supply chain process capability, and supply chain performance management capability. This paper also presents a dynamic-capabilities view of SCA and extensively describes a set of its three capabilities: data management capability, analytical supply chain process capability, and supply chain performance management capability. Next, using the SCM best practice, sales & operations planning (S&OP), the paper demonstrates opportunities to apply SCA in an integrated way. In discussing the implications of the proposed framework, finally, the paper examines several propositions predicting the positive impact of SCA and its individual capability on SCM performance.

Suggested Citation

  • Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    • Handle: RePEc:wsi:ijitdm:v:12:y:2013:i:01:n:s0219622013500016
    DOI: 10.1142/S0219622013500016
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    Citations

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    Cited by:

    1. Svetlana Nikolicic & Milorad Kilibarda & Marinko Maslaric & Dejan Mircetic & Sanja Bojic, 2021. "Reducing Food Waste in the Retail Supply Chains by Improving Efficiency of Logistics Operations," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    2. Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    3. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    4. Appelbaum, Deniz & Kogan, Alexander & Vasarhelyi, Miklos & Yan, Zhaokai, 2017. "Impact of business analytics and enterprise systems on managerial accounting," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 29-44.
    5. Rikhardsson, Pall & Dull, Richard, 2016. "An exploratory study of the adoption, application and impacts of continuous auditing technologies in small businesses," International Journal of Accounting Information Systems, Elsevier, vol. 20(C), pages 26-37.
    6. Peretzke, Julia & Sandhaus, Gregor, 2017. "Einsatzpotentiale von Cognitive Computing zur Unterstützung der Entscheidungsfindung im Supply Chain Management," ild Schriftenreihe 53, FOM Hochschule für Oekonomie & Management, Institut für Logistik- & Dienstleistungsmanagement (ild).
    7. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    8. Muhammad Noman Shafique & Ammar Rashid & Sook Fern Yeo & Umar Adeel, 2023. "Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    9. Galetsi, P. & Katsaliaki, K. & Kumar, S., 2019. "Values, challenges and future directions of big data analytics in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 241(C).
    10. Nam, Dalwoo & Lee, Junyeong & Lee, Heeseok, 2019. "Business analytics use in CRM: A nomological net from IT competence to CRM performance," International Journal of Information Management, Elsevier, vol. 45(C), pages 233-245.
    11. Nikolai Stein & Jan Meller & Christoph M. Flath, 2018. "Big data on the shop-floor: sensor-based decision-support for manual processes," Journal of Business Economics, Springer, vol. 88(5), pages 593-616, July.
    12. Rikhardsson, Pall & Yigitbasioglu, Ogan, 2018. "Business intelligence & analytics in management accounting research: Status and future focus," International Journal of Accounting Information Systems, Elsevier, vol. 29(C), pages 37-58.
    13. Andrea Ko & Saira Gillani, 2020. "A Research Review and Taxonomy Development for Decision Support and Business Analytics Using Semantic Text Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 97-126, January.
    14. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    15. 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.
    16. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    17. 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).
    18. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    19. Okoshi, Cleina Yayoe & Pinheiro de Lima, Edson & Gouvea Da Costa, Sergio Eduardo, 2019. "Performance cause and effect studies: Analyzing high performance manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 27-41.
    20. Johanna Bragge & Henrik Kallio & Tomi Seppälä & Timo Lainema & Pekka Malo, 2017. "Decision-Making in a Real-Time Business Simulation Game: Cultural and Demographic Aspects in Small Group Dynamics," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 779-815, May.
    21. Philipp Korherr & Dominik Kanbach, 2023. "Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance," Review of Managerial Science, Springer, vol. 17(6), pages 1943-1970, August.
    22. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    23. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.

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