Data Set: 187 Weeks of Customer Forecasts and Orders for Microprocessors from Intel Corporation

Matthew P. Manary () and Sean P. Willems ()
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Matthew P. Manary: Data Center Group, Intel Corporation, Hillsboro, Oregon 97124
Sean P. Willems: Haslam College of Business, University of Tennessee, Knoxville, Tennessee 37996

Manufacturing & Service Operations Management, 2022, vol. 24, issue 1, 682-689

Abstract: Problem definition : This data set contains 187 consecutive weeks of Intel microprocessor demand information for all five distribution centers in one of its five sales geographies. For every stock keeping unit (SKU) at every location, the weekly forecasted demand and actual customer orders are provided as well as the SKU’s average selling price category. These data are provided by week and by distribution center, producing 26,114 records in total. Academic/practical relevance : The 86 SKUs in the data set span five product generations. It provides years of product evolution across generations and price points. Methodology : As a data set paper, its purpose is to provide interesting and rich real-world data for researchers developing forecasting, inventory, pricing, and product assortment models. Results : The data set demonstrates the presence of significant forecast bias, heterogeneity of forecast errors between distribution centers, generational differences, product life cycles, and pricing dynamics. Managerial implications : This data set provides access to a rich pricing and sales setting from a major corporation that has not been made available before.

Keywords: industries: high technology and semiconductors; forecasting; bias removal; empirical research; data set; pricing; product generations (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:24:y:2022:i:1:p:682-689

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