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Article

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

by 1,†, 2,3,† and 1,3,*,†
1
Department of Mathematical and Computing Sciences, School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
2
Sony Computer Science Laboratories, 3-14-13 Higashi-Gotanda, Shinagawa-ku, Tokyo 141-0022, Japan
3
Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2019, 11(13), 3589; https://doi.org/10.3390/su11133589
Received: 29 May 2019 / Revised: 24 June 2019 / Accepted: 25 June 2019 / Published: 29 June 2019
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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. View Full-Text
Keywords: food waste reduction; retail; Corporate Social Responsibility; time series analysis; Point-Of-Sales food waste reduction; retail; Corporate Social Responsibility; time series analysis; Point-Of-Sales
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MDPI and ACS Style

Sakoda, G.; Takayasu, H.; Takayasu, M. Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit. Sustainability 2019, 11, 3589. https://doi.org/10.3390/su11133589

AMA Style

Sakoda G, Takayasu H, Takayasu M. Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit. Sustainability. 2019; 11(13):3589. https://doi.org/10.3390/su11133589

Chicago/Turabian Style

Sakoda, Gen, Hideki Takayasu, and Misako Takayasu. 2019. "Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit" Sustainability 11, no. 13: 3589. https://doi.org/10.3390/su11133589

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