Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit

Gen Sakoda, Hideki Takayasu and Misako Takayasu
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Gen Sakoda: Department of Mathematical and Computing Sciences, School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
Hideki Takayasu: Sony Computer Science Laboratories, 3-14-13 Higashi-Gotanda, Shinagawa-ku, Tokyo 141-0022, Japan
Misako Takayasu: Department of Mathematical and Computing Sciences, School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan

Sustainability, 2019, vol. 11, issue 13, 1-30

Abstract: Waste reduction in retail is a fundamental problem for sustainability. Among waste reduction approaches such as recycling and donation, stock management based on demand estimation which leads to mitigate waste generation and maintain a high profit is expected to play an important role. However, demand estimation is generally difficult because fluctuations in sales are quite volatile, and stock-out leads to incomplete demand observation. Here, we propose data science solutions to estimate non-stationary demand with censored sales data including stock-outs and realize scientific stock management. Concretely, we extend a non-stationary time series analysis method based on Particle Filter to handle censored data, and combine it with the newsvendor problem formula to determine the optimal stock. Moreover, we provide a way of pricing waste reduction costs. A method to verify consistency between the statistical model and sales data is also proposed. Numerical analysis using actual Point-Of-Sales data in convenience stores shows food waste could be reduced several tenths percent keeping high profits in most cases. Specifically, in cases of foods disposed of frequently about 75% of working days, food waste decreases to about a quarter with the profit increases by about 140%. The way of pricing waste reduction costs tells new insights such as 27% waste reduction is achieved by 1% profit loss. Our method provides a practical solution for food waste reduction in the retail sector.

Keywords: food waste reduction; retail; Corporate Social Responsibility; time series analysis; Point-Of-Sales (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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