Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 307-328 |
| Number of pages | 22 |
| Journal | Journal of Operations Management |
| Volume | 67 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Apr 2021 |
Bibliographical note
Funding Information:We thank Sebastian Wagner for early contributions to this research. We are also grateful for feedback by Dirk Neumann and Eric van Heck on previous versions of this work. The work in this article has been supported by a Microsoft Azure for Research award. Microsoft did not influence or take any part in the design and execution of the study. 1
Publisher Copyright:
© 2020 The Authors. Journal of Operations Management published by Wiley Periodicals LLC. on behalf of the Association for Supply Chain Management, Inc.
Research programs
- RSM LIS
