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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Illinois At Urbana-Champaign |
| Country | United States |
| Start Date | Jul 01, 2021 |
| End Date | Jun 30, 2023 |
| Duration | 729 days |
| Number of Grantees | 2 |
| Roles | Principal Investigator; Co-Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2113314 |
As environmental, political and business risks motivate companies to diversify their supply sources, and technology progress makes distributed networks of suppliers competitive, randomness in lead times remains an important and challenging issue in inventory management. Contrary to prevailing wisdom that variability degrades the performance of inventory management strategies, very early evidence suggests that lead-time variability can, in certain cases, be used to advantage to produce strategies that reduce overall cost of inventory.
This EArly-concept Grant for Exploratory Research (EAGER) project will provide further experimental evidence for understanding whether and how lead time uncertainty can be exploited to reduce inventory costs. The project will investigate how distributional information and variability in lead-time and demand patterns may yield new insights into adaptive approaches to inventory management that can dynamically adjust to changes in lead times.
This project will thoroughly investigate the premise that variability can be effectively exploited in an adaptive fashion to develop data-driven algorithms that identify strategies to reduce overall cost of inventory. This stands in contrast with the vast majority of inventory literature, which is focused on explicit analytical methods that require assumptions on the (stationary) distribution of lead time and demand.
These assumptions are often not supported in realistic inventory management scenarios. This project examines realistic and general model settings, with the goal of identifying the key factors affecting the performance of new policies and quantifying this performance. While still very preliminary and experimental, the results of this project are expected to lead to testable hypotheses that could potentially have great implications for inventory theory and application and therefore broad practical effect.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
University of Illinois At Urbana-Champaign
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