TY - JOUR
T1 - Product life cycle data set
T2 - Raw and cleaned data of weekly orders for personal computers
AU - Acimovic, Jason
AU - Erize, Francisco
AU - Hu, Kejia
AU - Thomas, Douglas J.
AU - Van Mieghem, Jan A.
N1 - Publisher Copyright:
© 2018 INFORMS
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068890761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068890761&partnerID=8YFLogxK
U2 - 10.1287/msom.2017.0692
DO - 10.1287/msom.2017.0692
M3 - Article
AN - SCOPUS:85068890761
SN - 1523-4614
VL - 21
SP - 171
EP - 176
JO - Manufacturing and Service Operations Management
JF - Manufacturing and Service Operations Management
IS - 1
ER -