EAGER: Turning Uncertainty into Advantage: Novel Policies that Exploit Randomness of Lead Times in Supply Chains

EAGER：将不确定性转化为优势：利用供应链中交货时间随机性的新政策

• 批准号: 2113314
• 负责人: Aleksandr Stolyar
• 金额: $ 14.57万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113314&HistoricalAwards=false
• 关键词: EAGER; Turning; Uncertainty; into; Advantage

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.

由于环境、政治和商业风险促使企业使其供应来源多样化，技术进步使分散的供应商网络具有竞争力，提前期的随机性仍然是库存管理中的一个重要和具有挑战性的问题。与变化性降低库存管理战略绩效的普遍看法相反，非常早的证据表明，在某些情况下，提前期变异性可被用来制定降低库存总成本的战略。这一探索性研究早期概念补助金(AGER)项目将提供进一步的实验证据，以了解是否以及如何利用提前期的不确定性来降低库存成本。该项目将调查交货期和需求模式中的分布信息和可变性如何产生对库存管理的适应性方法的新见解，这些方法可以动态地调整交付期的变化。该项目将彻底调查这样一个前提，即变异性可以有效地以自适应方式加以利用，以开发数据驱动的算法，以确定降低库存总成本的战略。这与绝大多数库存文献形成鲜明对比，后者侧重于明确的分析方法，这些方法需要对提前期和需求的(平稳)分布做出假设。这些假设在现实的库存管理方案中往往得不到支持。本项目审查现实的和一般的模型设置，目的是确定影响新政策绩效的关键因素并量化这种绩效。虽然这个项目仍然是非常初步的和实验的，但这个项目的结果预计会导致可测试的假设，这些假设可能对清单理论和应用产生巨大的影响，从而产生广泛的实际影响。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。