An Exploration of Big Data Practices in Retail Sector
AbstractConnected 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. View Full-Text
- Supplementary File 1:
Supplementary (PDF, 1201 KB)
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Aktas, E.; Meng, Y. An Exploration of Big Data Practices in Retail Sector. Logistics 2017, 1, 12.
Aktas E, Meng Y. An Exploration of Big Data Practices in Retail Sector. Logistics. 2017; 1(2):12.Chicago/Turabian Style
Aktas, Emel; Meng, Yuwei. 2017. "An Exploration of Big Data Practices in Retail Sector." Logistics 1, no. 2: 12.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.