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Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

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  • Tino T. Herden

    (Chair of Logistics, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany)

  • Benjamin Nitsche

    (Chair of Logistics, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany)

  • Benno Gerlach

    (Chair of Logistics, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany)

Abstract

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations.

Suggested Citation

  • Tino T. Herden & Benjamin Nitsche & Benno Gerlach, 2020. "Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations," Logistics, MDPI, vol. 4(1), pages 1-27, February.
    • Handle: RePEc:gam:jlogis:v:4:y:2020:i:1:p:5-:d:325175
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Christoph Bode & Denis Hübner & Stephan M. Wagner, 2014. "Managing Financially Distressed Suppliers: An Exploratory Study," Journal of Supply Chain Management, Institute for Supply Management, vol. 50(4), pages 24-43, October.
    3. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    4. Ravi Srinivasan & Morgan Swink, 2018. "An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1849-1867, October.
    5. Johan Rauer & Lutz Kaufmann, 2015. "Mitigating External Barriers to Implementing Green Supply Chain Management: A Grounded Theory Investigation of Green-Tech Companies' Rare Earth Metals Supply Chains," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(2), pages 65-88, April.
    6. Tino T. Herden & Steffen Bunzel, 2018. "Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study," Logistics, MDPI, vol. 2(2), pages 1-20, May.
    7. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    8. John P. Saldanha & John E. Mello & A. Michael Knemeyer & T.A.S. Vijayaraghavan, 2015. "Implementing Supply Chain Technologies in Emerging Markets: An Institutional Theory Perspective," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(1), pages 5-26, January.
    9. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    10. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
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    Cited by:

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    2. 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.

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