Product Life Cycle Data Set: Raw and Cleaned Data of Weekly Orders for Personal Computers

We provide and describe a data set of N = 8,935 weekly, normalized customer orders over the entire product life cycle for 170 Dell computer products sold in North America over a three and a half year period, from 2013–2016. Total orders for these products exceeded four million units and well over on...

Full description

Saved in:
Bibliographic Details
Published inManufacturing & service operations management Vol. 21; no. 1; pp. 171 - 176
Main Authors Acimovic, Jason, Erize, Francisco, Hu, Kejia, Thomas, Douglas J, Van Mieghem, Jan A
Format Journal Article
LanguageEnglish
Published Linthicum INFORMS 2019
Institute for Operations Research and the Management Sciences
Subjects
Analysis
Computer industry
Datasets
empirical research
Evaluation
Microcomputers
Order processing
Personal computers
Product life cycle
Production management
Services
Supply and demand
United States
Online AccessGet full text

Cover

More Information
Summary:We provide and describe a data set of N = 8,935 weekly, normalized customer orders over the entire product life cycle for 170 Dell computer products sold in North America over a three and a half year period, from 2013–2016. Total orders for these products exceeded four million units and well over one billion dollars in revenue. While Dell is historically known for fulfilling customer demand with a build-to-order approach, the products in this data set were designated as build-to-stock products. There are three elements in the data that, depending on the research application, researchers may want to identify or mitigate. First, some products have seemingly anomalous orders representing one-time purchases from large customers. Second, there are negative values for some products representing order cancellations. Third, end-of-life sales may be significantly influenced by management action. We present approaches for cleaning the data to address these issues. The supplemental material is available at https://doi.org/10.1287/msom.2017.0692 .
ISSN:1523-4614
1526-5498
DOI:10.1287/msom.2017.0692