IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v2y2018i2p10-d145192.html
   My bibliography  Save this article

Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study

Author

Listed:
  • Tino T. Herden

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

  • Steffen Bunzel

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

Abstract

While Big Data and Analytics are arguably rising stars of competitive advantage, their application is often presented and investigated as an overall approach. A plethora of methods and technologies combined with a variety of objectives creates a barrier for managers to decide how to act, while researchers investigating the impact of Analytics oftentimes neglect this complexity when generalizing their results. Based on a cluster analysis applied to 46 case studies of Supply Chain Analytics (SCA) we propose 6 archetypes of initiatives in SCA to provide orientation for managers as means to overcome barriers and build competitive advantage. Further, the derived archetypes present a distinction of SCA for researchers seeking to investigate the effects of SCA on organizational performance.

Suggested Citation

  • Tino T. Herden & Steffen Bunzel, 2018. "Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study," Logistics, MDPI, vol. 2(2), pages 1-20, May.
    • Handle: RePEc:gam:jlogis:v:2:y:2018:i:2:p:10-:d:145192
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/2/2/10/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/2/2/10/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    2. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    3. Gunasekaran, A. & Patel, C. & McGaughey, Ronald E., 2004. "A framework for supply chain performance measurement," International Journal of Production Economics, Elsevier, vol. 87(3), pages 333-347, February.
    4. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
    5. 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.
    6. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    7. Buttermann, Garry & Germain, Richard & Iyer, Karthik N.S., 2008. "Contingency theory "fit" as gestalt: An application to supply chain management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(6), pages 955-969, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Elena Barzizza & Nicolò Biasetton & Riccardo Ceccato & Luigi Salmaso, 2023. "Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review," Stats, MDPI, vol. 6(2), pages 1-21, May.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    2. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development," OSF Preprints 9auec, Center for Open Science.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    4. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    5. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    6. 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.
    7. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    8. Wang, Hui & Gong, Qiguo & Wang, Shouyang, 2017. "Information processing structures and decision making delays in MRP and JIT," International Journal of Production Economics, Elsevier, vol. 188(C), pages 41-49.
    9. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    10. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    11. Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Supply chain analytics implementation: A TOE perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 411-434, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    12. 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.
    13. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    14. 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.
    15. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    16. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    17. Raguseo, Elisabetta & Vitari, Claudio & Pigni, Federico, 2020. "Profiting from big data analytics: The moderating roles of industry concentration and firm size," International Journal of Production Economics, Elsevier, vol. 229(C).
    18. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Post-Print halshs-01923259, HAL.
    19. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    20. Hausladen, Iris & Schosser, Maximilian, 2020. "Towards a maturity model for big data analytics in airline network planning," Journal of Air Transport Management, Elsevier, vol. 82(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:2:y:2018:i:2:p:10-:d:145192. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.