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Exploratory data science for discovery and ex‐ante assessment of operational policies: Insights from vehicle sharing
Author(s) -
Brandt Tobias,
Dlugosch Oliver
Publication year - 2021
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1002/joom.1125
Subject(s) - computer science , relocation , context (archaeology) , identification (biology) , ex ante , big data , data science , data sharing , process management , service (business) , service provider , mobile device , knowledge management , business , marketing , world wide web , data mining , macroeconomics , pathology , economics , biology , programming language , medicine , paleontology , botany , alternative medicine
The proliferation of mobile devices and the emergence of the Internet of Things are leading to an unprecedented availability of operational data. In this article, we study how leveraging this data in conjunction with data science methods can help researchers and practitioners in the development and evaluation of new operational policies. Specifically, we introduce a two‐stage framework for exploratory data science consisting of a policy identification stage and an ex‐ante policy assessment stage. We apply the framework to the context of free‐floating carsharing—a novel mobility service that is an example of data‐rich smart city services. Through data exploration, we identify a novel preventive user‐based relocation policy and provide an ex‐ante assessment regarding the feasibility of its implementation. We discuss practical implications of our approach and results for shared‐mobility providers as well as the relationship between data science and operations management research.