Epidemic outbreaks are a special case of supply chain (SC) risks which is distinctively characterized by a long-term disruption existence, disruption propagations (i.e., the ripple effect), and high uncertainty. We present the results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs. First, we articulate the specific features that frame epidemic outbreaks as a unique type of SC disruption risks. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of epidemic outbreaks on the SC performance using the example of coronavirus COVID-19 and anyLogistix simulation and optimization software. We offer an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers. The major observation from the simulation experiments is that the timing of the closing and opening of the facilities at different echelons might become a major factor that determines the epidemic outbreak impact on the SC performance rather than an upstream disruption duration or the speed of epidemic propagation. Other important factors are lead-time, speed of epidemic propagation, and the upstream and downstream disruption durations in the SC. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the SCs and develop pandemic SC plans. Our approach can also help to identify the successful and wrong elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks. The paper is concluded by summarizing the most important insights and outlining future research agenda.

/pdf/predicting-the-impacts-of-epidemic-outbreaks-on-global-1nyswc5jve.pdf

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

http://www3.eng.cam.ac.uk/~hj231/Papers/rostering.pdf

Staff scheduling and rostering: A review of applications, methods and models

State-of-the-art exact and heuristic solution procedures for simple assembly line balancing

Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion-the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply-demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.

/pdf/viable-supply-chain-model-integrating-agility-resilience-and-3a64nqwkq5.pdf

Viable supply chain model: integrating agility, resilience and sustainability perspectives-lessons from and thinking beyond the COVID-19 pandemic.

We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design an...

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

Supply chains often experience significant economic losses from disruptions such as facility breakdowns, transportation mishaps, natural calamities, and intentional attacks. To help respond and recover from a disruption, we investigate adjustments in order activity across four echelons including assembly. Simulation experiments reveal that the impact of a disruption depends on its location, with costlier and longer lasting impacts occurring from disruptions at echelons close to ultimate consumption. Cost functions based on system inventory and service can be quite ill-behaved in these complex problem settings. Expediting, an adaptive ordering approach often used to mitigate disruptions, can trigger unintended bullwhip effects, and hurt rather than help overall performance. As an alternative to expediting interventions, dynamic order-up-to policies show promise as an adaptive mitigation tool. We also find benefits in the dynamic policies from incorporating a metaheuristic parameter search over multiple echelons, yielding significantly better solution quality than embedded unimodal search.

Mitigating disruptions in a multi-echelon supply chain using adaptive ordering

Supply chains often experience significant economic losses from disruptions such as facility breakdowns, transportation mishaps, natural calamities, and intentional attacks To help respond and recover from a disruption, we investigate adjustments in order activity across four echelons including assembly Simulation experiments reveal that the impact of a disruption depends on its location, with costlier and longer lasting impacts occurring from disruptions at echelons close to ultimate consumption Cost functions based on system inventory and service can be quite ill-behaved in these complex problem settings Expediting, an adaptive ordering approach often used to mitigate disruptions, can trigger unintended bullwhip effects, and hurt rather than help overall performance As an alternative to expediting interventions, dynamic order-up-to policies show promise as an adaptive mitigation tool We also find benefits in the dynamic policies from incorporating a metaheuristic parameter search over multiple echelons, yielding significantly better solution quality than embedded unimodal search

Mitigating Disruptions in a Multi-echelon Supply Chain using Adaptive Ordering

This invention provides a method and system for cost-based resource scheduling. This invention develops an initial resource schedule. This schedule is then represented as a Schedule Precedence Graph, which is an acyclic directed graph consisting of nodes and arcs. Each node corresponds to a task to be performed, and each arc corresponds to a technological or assigned task precedence. Each node is assigned a cost, which corresponds to cost or savings due to delaying the task one time unit. In this invention, the Maximum Flow Procedure is iteratively invoked to determine which tasks can be profitably delayed.

Scheduling method and system

Resolving uncertainty in manufacturing systems

Production processes are most frequently classified in the literature into one of three categories: projects, intermittent processes (i.e., job or flow shops), or continuous processes (i.e., assembly lines or flow processes). A major shortcoming of this classification system is that it often fails to capture important aspects of real world production processes, thus providing manufacturing managers with little conceptual work to guide their decision-making. In this paper, a more comprehensive framework (which we denote as the production classification system, or PCS) is developed which defines and relates many types of production processes, In addition, a direct linkage between this framework and corporate competitive strategy is described.

Thomas G. Schmitt

Papers

Mitigating disruptions in a multi-echelon supply chain using adaptive ordering

Mitigating Disruptions in a Multi-echelon Supply Chain using Adaptive Ordering

Scheduling method and system

Resolving uncertainty in manufacturing systems

Production classification system: concepts, models and strategies