Best in Class Use of Point of Sale Data to Drive Demand Planning and Forecasting

Open Access
- Author:
- Sweeney, Jamie Alison
- Area of Honors:
- Industrial Engineering
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Dr. Robert Alexander Novack, Thesis Supervisor
Harriet Black Nembhard, Thesis Honors Advisor - Keywords:
- Point of Sale Data
Forecasting
Demand Planning
Customer Data
Supply Chain
Third Party Data Provider - Abstract:
- In recent years there has been a significant shift in industry strategy and business due to the emergence of data-driven business models. New industry standards and technological advances have allowed point-of-sale (POS) data to be utilized in increasingly inventive and strategic ways. One of the most effective uses of POS data is in inventory management. POS data gives users supply chain transparency. This transparency results in a more robust understanding of demand, which improves both forecasting and replenishment. However, many of the capabilities of this POS data are still unknown as companies struggle to aggregate, cleanse, and make effective future decisions from the data. There is tremendous potential to use this customer data to influence long term forecasts and prevent stock outs. Reducing out-of stocks generates incremental sales, therefore effectively improving business performance. To help the industry better understand the potential uses of POS data in this space, a background review of publicly available information was conducted to identify current and planned future strategies of customer data use. Furthermore, interviews were conducted with leading companies of varied sizes in various industries to understand specific POS strategies and uses within each respective company (See interview guide Appendix B). The findings of these conversations were compiled to identify the best in class use of customer data. Furthermore, one company in particular, Company ‘A’, was targeted to develop an in-depth understanding of its use of POS data. After conversations with many different employees involved in various segments of Company ‘A’, the dissemination of this customer information from the register in store to high level demand planning was mapped. This current flow of POS data was then analyzed to target inefficiencies, and offer recommendations for improvement. Ultimately, throughout the investigation into the methods employed by industry leaders as well as Company ‘A’ it was concluded that most organizations struggle to use POS data effectively. Due to the lack of control over POS data availability, detail, and format industry professionals have struggled to aggregate the data and use it to impact long term demand planning. However, POS data is being used effectively to better understand buying behavior of consumers, and track promotions and new products. The recommendation to Company ‘A’ is the development of a central data repository. In this data bank Company ‘A’ can streamline and aggregate incoming data from various third party providers and retailers. By ensuring all incoming data meets required minimum parameters and timeliness the data in the repository can be assured useful. Moreover, syndicated market data can also be incorporated in the repository to allow more robust analysis and review when needed. Individuals in all functions of Company ‘A’ can be given access to the data bank, therefore simplifying the streamlining the data analysis process. This tool can also be valuable at the headquarters level when conducting monthly demand and supply review and creating the national level forecast. The remainder of this paper is organized first by the literature review, followed by the methodology, concluding with the discussion and recommendation.