Abstract
This chapter introduces the topic of demand prediction for retail applications. We first present several managerial motivations behind demand prediction and discuss the potential practical impact of having good demand prediction capabilities. We also outline the different modules and concepts covered in this book. We then present the accompanying dataset that will be used to illustrate all the concepts and test the various demand prediction methods. This dataset reports the weekly sales of 44 stock-keeping units (SKUs) from a tech-gadget e-commerce retailer over a period of 100 weeks. We next discuss the objective and scope considered in this book by elaborating on the concepts of training and test data, presenting several demand prediction accuracy metrics, and pinpointing the specific application under consideration.
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Notes
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See e.g., Chase (2013).
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If this link does not work, an alternative link is https://demandprediction.github.io/.
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Provost and Fawcett (2013).
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If the price (or any other variable) varies during the week, one can compute the resulting weighted average value (where the weights can be based on the sale volumes or on the revenues).
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References
Chase, C.W., 2013. Demand-driven forecasting: a structured approach to forecasting. John Wiley & Sons.
Downey, A., 2012. Think Python. O’Reilly Media, Inc.
Matthes, E., 2019. Python crash course: A hands-on, project-based introduction to programming. no starch press.
McKinney, W., 2012. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc.
Provost, F. and Fawcett, T., 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.
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Cohen, M.C., Gras, PE., Pentecoste, A., Zhang, R. (2022). Introduction. In: Demand Prediction in Retail . Springer Series in Supply Chain Management, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-85855-1_1
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DOI: https://doi.org/10.1007/978-3-030-85855-1_1
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