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A data-driven framework for predicting weather impact on high-volume low-margin retail products

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  • Verstraete, Gylian
  • Aghezzaf, El-Houssaine
  • Desmet, Bram

Abstract

Accurate demand forecasting is of critical importance to retail companies operating in high-volume low-margin industries. Inaccuracies in the forecasts lead either to stock-outs or to excess inventories, resulting in either lost sales or higher working capital, and for both cases in extra unnecessary costs. Prediction accuracy is essential to retail companies having a part of their product portfolio manufactured in low-cost countries and requiring long delivery times. It is rather vital when the demand for these goods is strongly weather dependent. The combination of long delivery times and weather dependence creates a business challenge, as the availability period of accurate weather information is much shorter than the lead time. In this paper we propose a methodology that handles the impact of both the short-term (with available weather data) and the long-term weather uncertainty on the forecast. For the former, the proposed framework is capable of automatically selecting the best prediction model. For latter, the framework fits a distribution on simulated and aggregated sales using the short-term regression model with historical weather data. This framework has been tested on a company's sales data and is proven to satisfactorily address the challenges that the company is facing.

Suggested Citation

  • Verstraete, Gylian & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "A data-driven framework for predicting weather impact on high-volume low-margin retail products," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 169-177.
    • Handle: RePEc:eee:joreco:v:48:y:2019:i:c:p:169-177
    DOI: 10.1016/j.jretconser.2019.02.019
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    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    2. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    3. Fallah Tehrani, Ali & Ahrens, Diane, 2016. "Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 131-138.
    4. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
    5. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    6. Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
    7. Fan, Shu & Hyndman, Rob J., 2011. "The price elasticity of electricity demand in South Australia," Energy Policy, Elsevier, vol. 39(6), pages 3709-3719, June.
    8. Murray, Kyle B. & Di Muro, Fabrizio & Finn, Adam & Popkowski Leszczyc, Peter, 2010. "The effect of weather on consumer spending," Journal of Retailing and Consumer Services, Elsevier, vol. 17(6), pages 512-520.
    9. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    10. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    11. Xiao, Tiaojun & Qi, Xiangtong, 2008. "Price competition, cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers," Omega, Elsevier, vol. 36(5), pages 741-753, October.
    12. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    13. Thomassey, Sébastien, 2010. "Sales forecasts in clothing industry: The key success factor of the supply chain management," International Journal of Production Economics, Elsevier, vol. 128(2), pages 470-483, December.
    14. Di Fatta, Davide & Patton, Dean & Viglia, Giampaolo, 2018. "The determinants of conversion rates in SME e-commerce websites," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 161-168.
    15. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    16. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    17. Martha Starr-McCluer, 2000. "The effects of weather on retail sales," Finance and Economics Discussion Series 2000-08, Board of Governors of the Federal Reserve System (U.S.).
    18. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    19. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    20. Silva, Ana Teresa & Moro, Sérgio & Rita, Paulo & Cortez, Paulo, 2018. "Unveiling the features of successful eBay smartphone sellers," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 311-324.
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    Cited by:

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    2. Badorf, Florian & Hoberg, Kai, 2020. "The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
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    4. Ketron, Seth & Spears, Nancy, 2020. "Schema-ing with color and temperature: The effects of color-temperature congruity and the role of non-temperature associations," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    5. Tian, Xin & Cao, Shasha & Song, Yan, 2021. "The impact of weather on consumer behavior and retail performance: Evidence from a convenience store chain in China," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).

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