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

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.