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An Exploration of Big Data Practices in Retail Sector

Author

Listed:
  • Emel Aktas

    (Cranfield School of Management, Cranfield University, College Road, Cranfield MK43 0AL, UK)

  • Yuwei Meng

    (Apple Computer Trading (Shanghai), No. 391 Yuanshen Road, Pudong, Shanghai 200135, China)

Abstract

Connected devices, sensors, and mobile apps make the retail sector a relevant testbed for big data tools and applications. We investigate how big data is, and can be used in retail operations. Based on our state-of-the-art literature review, we identify four themes for big data applications in retail logistics: availability, assortment, pricing, and layout planning. Our semi-structured interviews with retailers and academics suggest that historical sales data and loyalty schemes can be used to obtain customer insights for operational planning, but granular sales data can also benefit availability and assortment decisions. External data such as competitors’ prices and weather conditions can be used for demand forecasting and pricing. However, the path to exploiting big data is not a bed of roses. Challenges include shortages of people with the right set of skills, the lack of support from suppliers, issues in IT integration, managerial concerns including information sharing and process integration, and physical capability of the supply chain to respond to real-time changes captured by big data. We propose a data maturity profile for retail businesses and highlight future research directions.

Suggested Citation

  • Emel Aktas & Yuwei Meng, 2017. "An Exploration of Big Data Practices in Retail Sector," Logistics, MDPI, vol. 1(2), pages 1-28, December.
    • Handle: RePEc:gam:jlogis:v:1:y:2017:i:2:p:12-:d:122679
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    References listed on IDEAS

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    1. GfiRARD CACHON & MARSHALL FISHER, 1997. "Campbell Soup'S Continuous Replenishment Program: Evaluation And Enhanced Inventory Decision Rules," Production and Operations Management, Production and Operations Management Society, vol. 6(3), pages 266-276, September.
    2. Narendra Agrawal & Stephen A. Smith, 2008. "Multi-Location Inventory Models for Retail Supply Chain Management," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, chapter 0, pages 207-235, Springer.
    3. Narendra Agrawal & Stephen A. Smith, 2008. "Supply Chain Planning Processes for Two Major Retailers," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, chapter 0, pages 11-23, Springer.
    4. Narangajavana, Yeamduan & Garrigos-Simon, Fernando J. & García, Javier Sanchez & Forgas-Coll, Santiago, 2014. "Prices, prices and prices: A study in the airline sector," Tourism Management, Elsevier, vol. 41(C), pages 28-42.
    5. 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.
    6. Clark, Robert & Vincent, Nicolas, 2012. "Capacity-contingent pricing and competition in the airline industry," Journal of Air Transport Management, Elsevier, vol. 24(C), pages 7-11.
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    Cited by:

    1. Muhammad Azmat & Sebastian Kummer & Lara Trigueiro Moura & Federico Di Gennaro & Rene Moser, 2019. "Future Outlook of Highway Operations with Implementation of Innovative Technologies Like AV, CV, IoT and Big Data," Logistics, MDPI, vol. 3(2), pages 1-20, June.
    2. Myung Kyo Kim & Ram Narasimhan & Tobias Schoenherr, 2020. "Leveraging Logistics Competence in New Product Sourcing: The Role of Strategic Intent and Impact on Performance," Logistics, MDPI, vol. 4(4), pages 1-17, October.

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