PFI (MCA): Price and Lead Time Quotation for Make-to-Order Firms with Contingent Demand and Demand Learning

PFI (MCA)：具有或有需求和需求学习的按订单生产公司的价格和交货时间报价

• 批准号: 2223752
• 负责人: Ana Muriel
• 金额: $ 50万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223752&HistoricalAwards=false
• 关键词: PFI; MCA; Price; Lead; Time

The broader impact/commercial potential of this Partnerships for Innovation – Mid-Career Advancement (PFI(MCA)) project is to enhance the operations and customer service of make-to-order (MTO), customized service, and healthcare delivery organizations. The research will focus on developing and enabling the commercialization of decision support tools to generate real-time, operationally attainable pricing and lead-time quotes. The success of healthcare delivery organizations, many of which are small-to-medium size enterprises, is complex due to uncertainty. If a firm bids too aggressively, they often experience a demand surplus, resulting in poor service and penalties from unfulfilled proposal terms. If a firm proposes too cautiously, they become noncompetitive, resulting in business loss and under-utilization of capacity. This research will build a general decision support framework, which will be validated in the context of the industrial partner’s sales and operations environment. The project contributes directly to undergraduate and graduate recruitment, education and training in STEM, leadership, innovation, and entrepreneurship through participation and outreach activities. Underrepresented students will be recruited by leveraging the pull of working with dynamic industry partners and various supporting programs. This project seeks to develop decision support tools that integrate demand learning and revenue management techniques while explicitly considering the presence of ‘contingent demand.’ Dealing with the underlying uncertainty associated with contingent jobs, which may vary wildly in their requirements, necessitates robust approaches built around learning customer behavior and the stochastic system performance resulting from the exponential number of possible realizations of the demand backlog. The real-time quoting of arriving jobs in the presence of contingent demand has not received much attention in the literature. The development of optimization approaches that balances exploration and exploitation in operational decision making is recent and presents additional challenges in this context. The problem will be approached in a comprehensive fashion building on data science and operations research techniques to optimize system performance over a time horizon. Various customer behavior functions and special cases will be studied to gain a deeper understanding of the theoretical properties of systems subject to contingent demand and leverage them to address general cases. An online simulation platform will be built for practical evaluation and performance comparison of various approaches.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.

创新伙伴关系-职业中期发展（PFI(MCA)）项目的更广泛影响/商业潜力是增强按订单生产（MTO）、定制服务和医疗保健交付组织的运营和客户服务。该研究将侧重于开发和实现决策支持工具的商业化，以生成实时的、可操作的定价和交货时间报价。由于不确定性，医疗保健服务组织（其中许多是中小型企业）的成功是复杂的。如果一个公司出价过于激进，他们通常会遇到需求过剩，导致服务质量差，并因未履行提案条款而受到惩罚。如果企业过于谨慎，就会失去竞争力，导致业务损失和产能利用率不足。本研究将建立一个通用的决策支持框架，该框架将在工业合作伙伴的销售和运营环境中进行验证。该项目通过参与和外展活动，直接为本科生和研究生的招聘、STEM、领导力、创新和创业方面的教育和培训做出贡献。通过与充满活力的行业合作伙伴和各种支持计划的合作，将招募代表性不足的学生。该项目旨在开发决策支持工具，整合需求学习和收益管理技术，同时明确考虑“偶然需求”的存在。“处理与偶然工作相关的潜在不确定性，这些工作的需求可能会有很大的不同，需要围绕学习客户行为和随机系统性能建立强大的方法，这些方法是由需求积压的可能实现的指数数量产生的。”在偶然需求存在的情况下，实时报价到达的工作在文献中没有得到太多的关注。在作业决策中平衡勘探和开采的优化方法的发展是最近才出现的，在这种情况下提出了额外的挑战。该问题将在数据科学和运筹学技术的基础上以综合的方式进行处理，以在一段时间内优化系统性能。将研究各种客户行为函数和特殊情况，以更深入地了解受偶然需求影响的系统的理论特性，并利用它们来解决一般情况。将建立一个在线仿真平台，对各种方法进行实际评估和性能比较。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。