Supply Chain Analytics under Disruptive Uncertainty

新型冠状病毒全球大流行的影响已被证明对多个行业的供应链具有高度破坏性。例如，在零售方面，零售商经历了多类产品的恐慌性购买的影响。在航空航天业，业务中断和需求突然下降对生产和业务造成冲击和中断。关键零部件面临重大和不可预测的延误。供应链中旨在以低成本实现效率的流程和操作变得越来越复杂，因此往往容易出现高度破坏性的情况。在这种情况下，主要依赖于供应链决策过程中常用的大量历史数据的工具通常无法适应其运营环境的突然变化。该研究计划的独特之处在于其与极具挑战性的大流行和中断情况的实际相关性，以及整合运营研究和机器学习技术，以在数据质量和数量不足的情况下加强分析。我们的研究计划利用预测性和规范性分析方法，通过两个重要的对策来增强供应链的稳健性和弹性，即，(i)中断前的风险缓解计划，以主动准备供应链，使其在中断前具有足够的稳健性和弹性，以及（ii）中断后的恢复行动，以迅速发现、评估和应对手头的情况。我们将制定规范性方法，以确定供应链战略、战术和运营层面的风险缓解计划，具体而言，包括稳健的供应链网络设计、弹性生产和分销规划以及稳健的运输规划。为了使决策者能够在中断发生后处理中断，我们将集中精力在三个重要的应用程序上，即零售商的需求异常检测和在线补货，制造和生产计划中的动态调度，以及实时车辆调度和交付。分析工具的开发将基于多阶段随机和稳健的优化方法，高级分解方法，用于规定性功能的逻辑学和强化学习算法，通过用于生成对抗性（破坏性）场景的统计和机器学习方法来增强，降低维度，并构建智能不确定性表示（模糊集）。这项研究计划的大部分将与我们的零售和工业合作伙伴合作完成。该研究计划战略性地补充了我们加拿大供应链分析研究主席的现有研究以及SCALE.AI计划下的大规模工业合作。

The impact of COVID global pandemic has proven to be highly disruptive to the supply chain across multiple industries. For example, in the retail context, retailers experienced the impact of panic buying in multiple categories of products. In the aerospace industry, operations halts and sudden declines in the demand have generated shocks and disruptions in production and operations. Critical parts and components faced significant and unpredictable delays. The processes and operations in supply chains which are made to achieve efficiency at a low cost have become increasingly complex, and thus are often prone to highly disruptive scenarios. In such situations, the tools which rely mainly on a large amount of historical data commonly used in decision making processes in supply chains cannot generally be adapted to deal with sudden shifts in the environments they operate in. The unique aspects of this research program are its practical relevance to the highly challenging pandemic and disruption situations, and integration of operations research and machine learning techniques to enhance analytics in the context where quality and amount of data are insufficient. Our research program leverages the predictive and prescriptive analytics approaches to enhance the robustness and resilience of supply chains through two important countermeasures, i.e., (i) pre-disruption risk mitigation plans to proactively prepare the supply chain to be sufficiently robust and resilient prior to the disruptions, and (ii) post-disruption recovery actions to promptly detect, assess and react to the situations at hand. We will develop prescriptive approaches to determine risk mitigation plans at the strategic, tactical and operational levels of the supply chains, more specifically, in the areas of robust supply chain network design, resilience production and distribution planning, as well as robust transportation planning. To allow the decision maker to deal with a disruption after it occurs, we will focus our effort on three important applications, namely demand anomaly detection and online replenishment in retails, dynamic scheduling in manufacturing and production planning, and real-time vehicle dispatching and delivery. The developments of the analytics tools will be based on multi-stage stochastic and robust optimization methods, advanced decomposition methods, heuristics and reinforcement learning algorithms for the prescriptive capabilities, which are enhanced through statistical and machine learning methods used to generate adversarial (disruptive) scenarios, reduce dimensionality, and construct scenario-wise uncertainty representations (ambiguity set). The majority of this research program will be done in collaboration with our retail and industrial partners. This research program strategically complements existing research under our Canada Research Chair in Supply Chain Analytics as well as the large-scale industrial collaboration under the SCALE.AI initiative.