Supply Chain Analytics under Disruptive Uncertainty

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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.