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

Author

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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
    These authors contributed equally to this work.)

  • Hideki Takayasu

    (Sony Computer Science Laboratories, 3-14-13 Higashi-Gotanda, Shinagawa-ku, Tokyo 141-0022, Japan
    Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
    These authors contributed equally to this work.)

  • Misako Takayasu

    (Department of Mathematical and Computing Sciences, School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
    Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Gen Sakoda & Hideki Takayasu & Misako Takayasu, 2019. "Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit," Sustainability, MDPI, vol. 11(13), pages 1-30, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3589-:d:244161
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    References listed on IDEAS

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    2. Goran Avlijas & Vesna Vukanovic Dumanovic & Miljan Radunovic, 2021. "Measuring the Effects of Automatic Replenishment on Product Availability in Retail Stores," Sustainability, MDPI, vol. 13(3), pages 1-14, January.
    3. Kazuki Koyama & Mariko I. Ito & Takaaki Ohnishi, 2022. "Fluctuation in Grocery Sales by Brand: An Analysis Using Taylor’s Law," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 417-430, October.
    4. Carlos Martin-Rios & Anastasia Hofmann & Naomi Mackenzie, 2020. "Sustainability-Oriented Innovations in Food Waste Management Technology," Sustainability, MDPI, vol. 13(1), pages 1-12, December.

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