Retail Strategy to Reduce Waste Keeping High Profit Utilizing Taylor's Law in Point-of-Sales Data
Waste reduction is a fundamental problem for sustainability. Methods for waste reduction with point-of-sales (POS) data are proposed, utilizing the knowledge of a recent econophysics study on a statistical property of POS data. Concretely, the non-stationary time series analysis method based on the Particle Filter is developed, which considers abnormal fluctuation scaling known as Taylor's law. This method is extended for handling incomplete sales data because of stock-outs by introducing maximum likelihood estimation for censored data. The way for optimal stock determination with pricing the cost of waste reduction is also proposed. This study focuses on the examination of the methods for large sales numbers where Taylor's law is obvious. Numerical analysis using aggregated POS data shows the effectiveness of the methods to reduce food waste maintaining a high profit for large sales numbers. Moreover, the way of pricing the cost of waste reduction reveals that a small profit loss realizes substantial waste reduction, especially in the case that the proportionality constant of Taylor’s law is small. Specifically, around 1% profit loss realizes half disposal at =0.12, which is the actual value of processed food items used in this research. The methods provide practical and effective solutions for waste reduction keeping a high profit, especially with large sales numbers.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3593212Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 172
 United Nations, “World Population Prospects the 2017 Revision,” USA: United Nations, 2017.
 Food and Agriculture Organization of the United Nations, “Food Wastage Footprint—Impacts on Natural Resources,” Rome: Food and Agriculture Organization of the United Nations, 2013.
 United Nations, “Transforming Our World: The 2030 Agenda for Sustainable Development,” New York: United Nations, 2015.
 G. Sakoda, H. Takayasu, and M. Takayasu, “Tracking Poisson Parameter for Non-Stationary Discontinuous Time Series with Taylors Abnormal Fluctuation Scaling,” Stats, vol. 2, no. 1, pp. 5569, Jan. 2019.
 G. Sakoda, H. Takayasu, and M. Takayasu, “Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit,” Sustainability, vol. 11, no. 13, p. 3589, Jun. 2019.
 G. Fukunaga, H. Takayasu, and M. Takayasu, “Property of fluctuations of sales quantities by product category in convenience stores,” PLoS One, vol. 11, no. 6, pp. 119, 2016. PLoS ONE 2016, 11, e0157653.
 L. R. Taylor, “Aggregation, variance and the mean,” Nature, vol. 189, pp.732–735, 1961.
 E. T. Lee, J.W.Wang, “Statistical Methods for Survival Data Analysis,” USA: WILEY, 2013, pp. 133-205.
 G. Kitagawa, “Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models,” J. Comput. Graph. Stat, vol.5, pp.1–25, 1996.
 N. J. Gordon, D.J. Salmond, A.F.M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” IEE Proc.F, vol. 140, pp.107–113, 1993.
 S. R. Cherry, J.A. Sorenson, M.E. Phelps, “Physics in Nuclear Medicine,” 4th ed. Amsterdam: Elsevier, pp. 126–128, 2012.
 E. L. Porteus, “Stochastic Inventory Theory. Handbooks in Operations Research and Management Science,” Amsterdam: Elsevier, vol. 2, pp. 605–652, 1990.
 M. Khouja, “The single-period (news-vendor) problem: Literature review and suggestions for future research,” Omega, vol. 27, pp. 537–553, 1999.
 Y. Qin, R. Wang, A.J. Vakharia, Y. Chen, M.M.H. Seref, “The newsvendor problem: Review and directions for future research,” Eur. J. Oper. Res., vol. 213, pp. 361–374, 2011.
 Seven & i Holdings Co., Ltd., “Corporate Outline 2011,” Available online:https://www.7andi.com/library/dbps_data/_template_/_res/en/ir/library/co/pdf/2011_07.pdf (accessed on 8 September 2019).