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

Author

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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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    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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