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When is the supply chain resilient? Customer and operational perspectives

Taylor & Francis
International Journal of Production Research
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In this study, the ripple effect in the supply chain is analysed. Ripple effect describes the impact of a disruption propagation on supply chain performance and disruption-based scope of changes in supply chain structural design and planning parameters. We delineate major features of the ripple effect as compared to the bullwhip effect. Subsequently, we review recent quantitative literature that tackled the ripple effect explicitly or implicitly and give our vision of the state of the art and perspectives. The literature is classified into mathematical optimisation, simulation, control theoretic and complexity and reliability research. We observe the reasons and mitigation strategies for the ripple effect in the supply chain and present the ripple effect control framework that includes redundancy, flexibility and resilience analysis. Even though a variety of valuable insights has been developed in the said area in recent years, some crucial research avenues have been identified for the near future.
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In this paper, stochastic optimisation of CVaR is applied to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. In order to establish the risk-averse boundaries on supply chain viability space, two stochastic optimisation models are developed with the two conflicting objectives: minimisation of Conditional Cost-at-Risk and maximisation of Conditional Service-at-Risk. Then, the risk-averse viable production trajectory between the two boundaries is selected using a stochastic mixed integer quadratic programming model. The proposed approach is applied to maintain the supply chain viability in the smartphone manufacturing and the results of computational experiments are provided. The findings indicate that when the decision-making is more risk-aversive, the size of the viability space between the two boundaries is greater. As a result, more room is available for selecting viable production trajectories under severe disruptions. Moreover, the larger is viability space, the higher is both worst-case and average resilience of the supply chain. Risk-neutral, single-objective decision-making may reduce the supply chain viability. A single objective supply chain optimisation which moves production to the corresponding boundary of the viability space, should not be applied under severe disruption risks to avoid greater losses.
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The COVID-19 pandemic has illustrated the unprecedented challenges of ensuring the continuity of operations in a supply chain as suppliers' and their suppliers stop producing due the spread of infection, leading to a degradation of downstream customer service levels in a ripple effect. In this paper, we contextualize a dynamic approach and propose an optimal control model for supply chain reconfiguration and ripple effect analysis integrated with an epidemic dynamics model. We provide supply chain managers with the optimal choice over a planning horizon among subsets of interchangeable suppliers and corresponding orders; this will maximize demand satisfaction given their prices, lead times, exposure to infection, and upstream suppliers' risk exposure. Numerical illustrations show that our prescriptive forward-looking model can help reconfigure a supply chain and mitigate the ripple effect due to reduced production because of suppliers' infected workers. A risk aversion factor incorporates a measure of supplier risk exposure at the upstream echelons. We examine three scenarios: (a) infection limits the capacity of suppliers, (b) the pandemic recedes but not at the same pace for all suppliers, and (c) infection waves affect the capacity of some suppliers, while others are in a recovery phase. We illustrate through a case study how our model can be immediately deployed in manufacturing or retail supply chains since the data are readily accessible from suppliers and health authorities. This work opens new avenues for prescriptive models in operations management and the study of viable supply chains by combining optimal control and epidemiological models.
Article
Ever more interconnected and complex supply chains increase the risk of disruptions and their propagation. Companies need to simultaneously develop capabilities such as responsiveness, resilience, and robustness (Triple-R) to hedge against these risks, while staying competitive. This requires careful investment decisions on which portfolio of Triple-R strategies to create. Being a dynamic, transient phenomena, analytical tools are often not feasible to quantify effects. Computer simulation provides an important alternative, which led to a surge of literature in recent years. This study conducted a systematic literature review of studies that use computer simulation for assessing Triple-R capabilities. Based on a final sample of 174 full articles, we found that classical means to create Triple-R capabilities have been widely assessed for a broad set of different disruption characteristics, including type of disruption and type of propagation. But, while there exists a broad literature that focusses on engineering resilience and adaptation, more research is needed that focusses on resilience as transformation. Recent disruptions are social-ecological disruptions that require new mitigation strategies. To appropriately design and assess these strategies, which need to consider societal impacts towards Industry 5.0, hybrid simulations, online simulations, and physical simulations are required that can model social-ecological aspects and contexts.
Article
This paper presents a novel quantitative approach and stochastic quadratic optimization model to maintain supply chain viability under the ripple effect. Instead of viability kernel commonly used in the viability theory, this paper establishes the boundaries on acceptable production states for which the production can be continued under the ripple effect, with no severe losses. For a given implementable portfolio of controls, the boundaries on acceptable production trajectories associated with the two conflicting objectives, cost and customer service level, are determined. The decision maker selects a viable production trajectory in-between the two boundary trajectories: the cost-optimal and the service-optimal. The selection depends on the decision maker preference, represented by a chosen weight factor in the optimized quadratic objective function that minimizes weighted deviations from the cost-optimal and from the service-optimal production schedules under the ripple effect. The findings indicate that for the extreme values of the weight factor, the viable production trajectory is inclined toward the corresponding boundary trajectory and remains in-between the two boundaries, when both objectives are equally important. Keeping production trajectory in-between the two boundaries makes the supply chain more resilient to disruption risks, while the supply chain resilience diminishes as the production trajectory approaches a boundary trajectory. Then, a more severe disruption may push the production outside the viability region and cause greater losses.
Article
This tertiary study systematically analyzes 65 literature reviews on supply chain resilience (SCR) published in academic journals or conference proceedings. Our focus is on the vulnerabilities and capabilities of a supply chain that need to be balanced to achieve resilience. We explore the interdependencies of these two categories of SCR by developing an innovative framework to realize capabilities after identifying the SCR vulnerabilities. First, we propose a framework that systematizes the vulnerabilities and capabilities identified in the literature. Then, we discuss the identified SCR characteristics based on the framework and quantitatively evaluate the literature reviews’ focus on the two SCR categories. A synthesis of the research results shows the SCR characteristics addressed in the literature and reveals deficits for specific vulnerabilities. Finally, we outline future research opportunities based on these findings by mapping SCR capabilities and vulnerabilities in light of Industry 4.0 and digital supply chain developments. Then, we derive research gaps and recommended actions for practitioners in the context of SCR and Industry 4.0. Appendices/Supplementary materials are available on request by emailing the corresponding author or can be obtained under https://doi.org/10.5281/zenodo.7022542.
Article
Widespread product shortages during the COVID-19 pandemic and other emergencies have prompted several large studies of how to make supply chains more resilient. In this article we leverage these studies, as well as the academic literature, to provide a review of our state of knowledge about supply chain resilience. To do this, we (i) classify the failure modes of a supply chain, (ii) quantitatively evaluate the level of resilience needed in a supply chain to achieve desired business or societal outcomes, (iii) describe a structured framework of actions to enhance supply chain resilience, and (iv) use the resulting conceptual paradigm to review the academic literature on supply chain risk and resilience. In each step, we summarize key insights from our current state of understanding, as well as gaps that present opportunities for research and practice.
Article
The coronavirus pandemic (COVID-19) threatens people’s health. During the COVID-19 outbreak, people are encouraged to wear masks to reduce the spread of the virus. With the strong demand for masks, it has come a boom in counterfeit production. Combating counterfeit masks is vital and urgent to reduce the risks for public health. Motivated by the actual practices during the COVID-19, we examine how quality inspection and blockchain adoption help combat counterfeit masks. We find that quality inspection may not be always effective, as the government will tolerate the presence of counterfeit masks if the presence of the counterfeits is not significant. Comparing quality inspection with blockchain adoption, when the spread of COVID-19 is mild, authentic mask sellers may be encouraged to use the blockchain technology, which can increase their profits and reduce the social health risk. Furthermore, we extend our model to investigate the impacts of endogenous quality. Both quality inspection and blockchain adoption can induce low-quality mask sellers to enhance thequality level. When the number of counterfeit masks is increasing, encouraging the high-quality mask sellers to adopt the blockchain technology is effective to reduce social health risk when the spread of the coronavirus is rapid.
Article
We offer the notion of “commons” at different levels—within company, private across company, and government‐sponsored across‐industry sectors—and discuss how the creation of such commons enabled firms to be both efficient during normal times and resilient against the disruptions resulting from COVID‐19. At the same time, there are many proven strategies providing resilience in supply chains. For instance, companies that used multiple channels to improve efficiency when facing day‐to‐day demand‐and‐supply variations found that the structure also offered resilience without additional cost when COVID struck. We discuss how the presence of commons lowers the cost for firms to adopt such resilience‐building supply chain strategies. We discuss factors that impact the creation of these commons and conclude with a number of questions to guide further research into the role of industry commons in facilitating supply chain resilience.
Article
The COVID-19 pandemic severely tested the resilience and robustness of supply chains for medically critical items and various common household goods. Severe and prolonged shortages of personal protective equipment (PPE) and ventilators in the United States have revealed vulnerabilities in the supply chains of such essential products in a time of need. Consequently, corporations have felt public pressure to rethink their supply chains. We begin this paper by examining the underlying causes of the prolonged shortages of critical products in the US as well as government’s and some companies’ initial response. Drawing from the lessons learned from the COVID pandemic, we propose a research agenda and opportunities to develop responsive supply chains to fight future pandemics. These opportunities revolve around measures that are intended to improve the supply chain responsiveness of essential products to combat future pandemics and other major public health emergencies.
Article
Exploiting the exogenous and regional nature of the Great East Japan Earthquake of 2011, this paper provides a quantification of the role of input-output linkages as a mechanism for the propagation and amplification of shocks. We document that the disruption caused by the disaster propagated upstream and downstream along supply chains, affecting the direct and indirect suppliers and customers of disaster-stricken firms. Using a general equilibrium model of production networks, we then obtain an estimate for the overall macroeconomic impact of the disaster by taking these propagation effects into account. We find that the earthquake and its aftermaths resulted in a 0.47 percentage point decline in Japan’s real GDP growth in the year following the disaster.
Article
Due to the increasing occurrence of disruptions across our global society, it has become critically important to understand the resilience of different socio-economic systems, i.e., to what extent those systems exhibit the ability both to resist a disruption and to recover from one once it occurs. In order to characterize this ability, however, one must be able to quantitatively measure the relative level of resilience that a given system displays in response to a disruptive event. Such a measurement should be easily understandable and straightforward to implement, but it should also utilize a consistent frame of reference so that one can properly compare the relative performance of different systems and assess the relative effectiveness of different resilience investments. With this in mind, this paper presents an improved approach for measuring system resilience that supports better decision making by providing both consistency and flexibility across different contexts. The theoretical basis for the approach is developed first, and then its advantages and limitations are illustrated in the context of several different practical examples.
Article
Supply chains are prone to several operational and disruption risks. In order to design a resilient supply chain network capable of responding to such potential risks suitably, this paper proposes a novel framework for the business continuity-inspired resilient supply chain network design (BCRSCND) problem, which includes three steps. First, four resilience dimensions including Anticipation, Preparation, Robustness, and Recovery are considered to quantify the resilience score of each facility using a multi-criteria decision-making technique and considering a comprehensive set of resilience strategies. In the second step, the critical processes and their business continuity metrics (which are vital for supply chain continuity), are identified. The outputs of the first two steps provide the inputs of a novel two-stage mixed possibilistic-stochastic programing (TSMPSP) model. The model aims to design a multi-echelon, multi-product resilient supply chain network under both operational and disruption risks. The proposed TSMPSP model allows decision makers to incorporate their risk attitudes into the design process. After converting the original TSMPSP model into the crisp counterpart, several sensitivity analyses are conducted on different features of hypothetical disruptions (i.e. their severity, likelihood and location) and DM’s risk attitudes from which useful managerial insights are provided.
Article
An outbreak of deadly COVID-19 virus has not only taken the lives of people but also severely crippled the economy. Due to strict lockdown, the manufacturing and logistics activities have been suspended, and it has affected the demand and supply of various products as a result of restrictions imposed on shopkeepers and retailers. Impacts of COVID-19 are observed ubiquitously in every type of units from different sectors. In this study, a simulation model of the public distribution system (PDS) network is developed with three different scenarios to demonstrate disruptions in the food supply chain. Difficulties have been increased in matching supply and demand in a vast network of PDS because of changing scenarios with the growth of infected cases and recovery. This paper also highlights the importance of a resilient supply chain during a pandemic. Our proposed simulation model can help in developing a resilient and responsive food supply chain to match the varying demand, and then further assist in providing decision-making support for rerouting the vehicles as per travel restrictions in areas. Paper has been summarised with significant highlights and including future research scope for developing a more robust food supply chain network.
Article
This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address riskneutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on integrated disruption mitigation and recovery decision-making and innovative, computationally efficient multi-portfolio approach to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on realworld supply chain disruption management problems, illustrate the material presented and provide managerial insights. Many propositions formulated in the book lead to a deep understanding of the properties of developed stochastic mixed integer programs and optimal solutions. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into six main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply and demand portfolios and production and inventory scheduling, Part V deals with selection of supply portfolio in multitier supply chain networks; and Part VI addresses selection of cybersecurity safequards portfolio for disruption management of information flows in supply chains.
Article
Resilience, defined as the ability to recover quickly and effectively from a disruption, is critically important for supply chains. Yet, it has not been quantified as frequently as supply chain robustness. In this paper we review the existing metrics for supply chain resilience and introduce a new metric, titled the net present value of the loss of profit (NPV-LP). We test these metrics on a small supply chain problem consisting of one supply and one demand node for a perishable good over a multi-period horizon with a possible port shut-down. We use a stochastic programming formulation of the problem. We show how the different metrics cause different investment decisions for the supply chain, and hence why it is important to carefully pick the correct metric when modeling supply chain resilience.
Article
Supply chain risk management is extremely important for the success of a company. Due to the increasing complexity of supply chains, avoiding and mitigating the effects of disruptions is very challenging. This article presents the results of a systematic literature review and content analysis in order to provide a comprehensive overview of the methods that are currently used for mitigating supply chain disruptions. The results of the review indicate that research in this field is interdisciplinary and that no common modelling language has emerged thus far. Prior research mostly redraws to graph theory and/or social network analysis, although a few methods have been developed recently specifically for supply chain risk management. We observe that prior contributions addressed risk and structure mostly separately and that only a few works focused on their intersection. The results of this review are consolidated in a research agenda that calls for research on the risk-structure-interface and the development of proxy methods.
Article
Inspired by the fact that a combination of network characteristics can describe a network more fully than the network type is able to, especially since some networks do not belong to any one specific type, this research effort investigates the relationship between network characteristics and supply chain resilience. We begin by demonstrating that the investigation of network characteristics can lead to a better understanding of supply chain resilience. We then identify the key network characteristics that best represent network structure in determining resilience. We show that utilizing a reduced list of characteristics yields performance equal to that when using a complete set of characteristics. Based on the results, we summarize key points that can support interpretation of the impact of network characteristics on resilience. We also conduct a case study to illustrate how our approach can be employed to understand resilience.