Resilient Supply Chain Network Research Articles

Designing a resilient supply chain network: A multi-objective data-driven distributionally robust optimization method

Uncertainty of supply chain disruption poses a significant challenge to decision-making in many real-life problems. In this paper, a new multi-objective data-driven distributionally robust optimization (MDDRO) model for the resilient supply chain network (RSCN) design problem under disruption scenario is developed, which includes minimizing the total cost, carbon emissions, and delivery time. In the context of supply chain disruption, it is important self-evidently that products can be supplied normally and the practical demand can be met. In handling the partial probability distribution information of the considered uncertain demand, this paper uses a data-driven Wasserstein-Moment ambiguity set (WMAS), which incorporate the Wasserstein metric and Moment information, and a robust counterpart to transform the developed model into a tractable approximation form. The finite-sample performance guarantee of the optimal solution of MDDRO model with Wasserstein-Moment ambiguous chance constraint is given. An accelerated Branch and cut algorithm (BCA) is constructed to solve the MDDRO model. Finally, through the investigation of a real-life case and sensitivity analysis of key parameters, some managerial insights are put forward.

A dynamic resilience management framework for deep-tier supply networks

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.

Designing resilient supply chain networks: a systematic literature review of mitigation strategies

AbstractWith increased globalisation supply chain (SC) disruption significantly affects people, organisations and society. Supply chain network design (SCND) reduces the effects of disruption, employing mitigation strategies such as extra capacity and flexibility to make SCs resilient. Currently, no systematic literature review classifies mitigation strategies for SCND. This paper systematically reviews the literature on SCND, analysing proposed mitigation strategies and the methods used for their integration into quantitative models. First to understand the key failure drivers SCND literature is categorised using geography, with local, regional or global disruptions linked to vulnerable sections of a SC. Second, the strategies used in mathematical models to increase SC resilience are categorized as proactive, reactive, or SC design quality capabilities. Third, the relative performance of mitigation strategies is analysed to provide a comparison, identifying the most effective strategies in given contexts. Forth, mathematical modelling techniques used in resilient SCND are reviewed, identifying how strategies are integrated into quantitative models. Finally, gaps in knowledge, key research questions and future directions for researchers are described.

The Shock of US-China trade war and the job Market: Downstream shrinkage and upstream employment

Using the 2018 US-China trade war as a quasi-natural experiment, we investigate how downstream shrinkage affects upstream employment. Utilizing a difference-in-differences (DID) approach, we reveal a negative effect of downstream shrinkage on upstream firm employment. Our mechanism analysis indicates that trade wars increase the tax burden, inventories, and accounts payable for downstream firms while reducing their employment. This shrinkage of downstream firms limits the scale of investment and operations for upstream firms through supply chain transmission, thereby decreasing employment of upstream firms. Moreover, our finding is more pronounced for specific types of firms, such as non-state-owned firms, firms operating across multiple industries. Our study provides an essential reference for firms in building resilient supply chain networks to better cope with potential negative shocks.

Enhancing multi-facility manufacturing resilience through integrated product and supply chain network design

This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework’s capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience.

A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

Resilient supply chain network design with proactive and reactive strategies under major disruptions

Resilient supply chain network design with proactive and reactive strategies under major disruptions

Resilient supply chain network design under disruption and operational risks

Resilient supply chain network design under disruption and operational risks

Resilient supply chain network design under uncertain environment

Modern supply chain operates in a highly uncertain environment caused by natural disasters and market changes. Such a fact has motivated academics and practitioners to pay more attention to the supply chain resilience. This paper reports on designing a resilient supply chain under uncertain environment by an uncertain programming method. The desired resilience level against disruption is achieved with less redundancy by controlling them in the presented models. And parameter uncertainty caused by the limited historical data is also addressed using uncertainty theory. To cope with the problem's complexity, we convert the proposed models into their deterministic formulations, which can easily be solved by cplex solver. The results from sensitivity analysis demonstrate the necessity of including uncertainty during the planning phase of supply chain network design. The performance of resilience constraint and the positive effect of sticker redundancy constraint in improving service level are also investigated fully.

Designing a two-stage model for the resilient agri-food supply chain network under dynamic competition

PurposeSupply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs a resilient retail supply chain network for perishable foods under the dynamic competition to maximize retailer's profits.Design/methodology/approachA two-stage mixed-integer non-linear model is presented for designing the supply chain network. In the first stage, an equilibrium model that considers the characteristics of perishable foods is developed. In the second stage, a mixed integer non-linear programming model is presented to deal with the strategic decisions. Finally, an efficient memetic algorithm is designed to deal with large-scale problems.FindingsThe optimal the selection of suppliers, distribution centers and the order allocation are found among the supply chain entities. Considering the perishability of agri-food products, the equilibrium retail price and selling quantity are determined. Through a numerical example, the optimal inventory period under different maximum shelf life and the impact of three resilient strategies on retailer's profit, selling price and selling quantity are analyzed.Research limitations/implicationsAs for future research, the research can be extended in a number of directions. First, this paper studies the retail supply chain network design problem under competition among retailers. It can be an interesting direction to consider retailers competing with suppliers. Second, the authors can try to linearize the non-linear model and solve the large-scale integer programming problem by exact algorithm. Finally, the freshness of perishable foods gradually declines linearly to zero as the maximum shelf life approaches, and it would be a meaningful attempt to consider the freshness of perishable foods declines exponentially.Originality/valueThis paper innovatively designs the resilient supply chain network for perishable foods under dynamic competition. The retailer's dynamic competition and resilient strategies are considered simultaneously when designing supply chain network for perishable foods. In addition, this paper gives insights into how to obtain the optimal inventory period and compare the retailer's resilient strategies.

Designing a resilient supply chain network under ambiguous information and disruption risk

In case of supply chain disruption following severe disasters, many supply chains tend to collapse and take a long time to recover. Resilient supply chain network design (RSCND) is an important research problem in supply chain management, which means that the supply chain can maintain continuous supply and quickly restore the supply capability in part destruction. Based on the limited distribution information of uncertain demand, a two-stage distributionally robust optimization (DRO) model with ambiguous chance constraint (ACC) is proposed to solve the RSCND problem under demand uncertainty and disruption scenario to provide decision support for planning the supply chain network. Finally, to verify the effectiveness and practicability of the proposed DRO model, we apply the method to a real case study in Wuhan, China, about designing a resilient RSC network to withstand disruption. By comparison and sensitivity analysis in numerical experiments, some management insights of industry decision-makers are obtained.

A Bi-Objective Optimization Model for a Low-Carbon Supply Chain Network with Risk of Uncertain Disruptions

Disruption risks exacerbate the complexity of low-carbon supply chain network design in an uncertain supply chain environment. Considering the low frequency and non-repeatability of these disruption events makes it impossible to collect data to obtain their probabilities. In this study, supply disruptions were regarded as uncertain events; supply chain uncertain disruption risk is defined and quantified based on the uncertainty theory, in which uncertain disruptions are characterized by the belief degree on account of expert estimation with duality, i.e., symmetry. Optimization models were constructed with the objective of minimizing expected carbon emissions and costs, which optimizes the selection of suppliers with uncertain disruptions, and the assignment of manufacturers and customers. The properties of the model were analyzed, and the models were solved separately using different methods according to different decision criteria. Finally, the validity of the proposed models and algorithm were verified using a real case study of a glass manufacturing company. The findings exhibit promising insights for designing a sustainable and resilient supply chain network in an uncertain environment.

An integrated decision support framework for resilient vaccine supply chain network design

Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the ε-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.

RETRACTED ARTICLE: Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original Whale and Grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet

The resilience and sustainability of supply chains are facing new challenges due to the increasing complexity of supply chains. Compared to traditional supply chain networks, the Physical Internet (PI) has the potential to address sustainable development challenges and build resilient supply chain networks by providing interconnected and open logistics services. However, the interplay between resilience and sustainability has yet to be thoroughly explored in the PI-enabled supply chain literature. This paper aims to investigate the relationship between resilience and sustainability in PI-enabled supply chains. An innovative hybrid approach that combines the resilient-sustainable performance scoring method with mixed-possibilistic programming is proposed to trade-off and analyze the relationship between the two. Applying the concepts of resilience and sustainability to PI-enabled supply chains involves optimizing costs, sustainable performance, and resilience levels simultaneously. This enables coping with unforeseen disruptions from suppliers, plants, and PI hubs. The results of computational experiments have demonstrated that the PI-enabled supply chain exhibits stronger sustainable and resilience performance in dealing with random disruptions compared to traditional supply chains. The fruitful research yielded important management insights and practical implications, thereby contributing to the reinforcement of the literature on PI.

Resilient supply chain network design without lagging sustainability responsibilities

In the 21st century, global supply chains have experienced severe risks due to disruptions caused by crises and serious diseases, such as the great tsunami, SARS, and, more recently, COVID-19. Building a resilient supply chain is necessary for business survival and growth. Similarly, there is increasing regulatory and social pressure for managers to continuously design and implement sustainable supply chain networks, encompassing economic, social, and environmental components. Hence, a panacea approach is required to establish a compromise position between resiliency concerns and sustainability responsibilities. To address this, this work presents a hybrid integrated BWM-CoCoSo-multi-objective programming model (BC-MOPM) formulated to deliver a compromise between resilience and sustainability supply chain network design (RS-SCND). First, a thorough literature review analysis is conducted to explore the relationship and correlation between resilience and sustainability to develop a framework for the resiliency and sustainability criteria, in a supply chain context. Second, four objectives were formulated, including the minimisation of total cost and environmental impact and the maximisation of social and resilience paradigms. A real two-tier supply chain network is deployed to evaluate the applicability of the developed BC-MOPM. Furthermore, sensitivity analysis is conducted to establish the relative importance of the identified criteria to prove the model’s robustness. Results demonstrate the capability of the BC-MOPM in revealing trade-offs between the resiliency and sustainability aspects.

Proposing a bi-objective model for the problem of designing a resilient supply chain network of pharmaceutical-health relief items under disruption conditions by considering lateral transshipment

Proposing a bi-objective model for the problem of designing a resilient supply chain network of pharmaceutical-health relief items under disruption conditions by considering lateral transshipment

Towards increasing synergistic effects of resilience strategies in supply chain network design

The recent COVID-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience.

The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events

Most parts of the world have suffered the negative impacts from extreme weather events, in whatever form they may take. To mitigate such impacts, attention in the operations management literature has focused on how firms build resilience in their supply chains, in order to quickly respond to such events and also to return, as soon as possible, to a business-as-usual state. Prior studies have examined the process of building a supply chain (SC) in different countries, industries and in response to various disruptions, such as the COVID-19 pandemic, whilst, at the same time, calling for further research in different contexts. We respond to these calls by exploring SC resilience ability in the South African mining industry under extreme weather events. We situated our study in the dynamic capability view (DCV) view of the firm. We examined the direct effect of big data and predictive analytics (BDPA) capabilities on SC visibility and the final effects on community and resource resilience. We adopted a sequential mixed methods research design, collecting data from interviews with 10 industry practitioners and from 219 respondents to an online survey. We built and tested our theoretical model using partial least squares structured equation modelling (PLS-SEM). Notable theoretical contributions of our study are that big data enables a more efficient supply chain monitoring system, which, in turn, improves SC visibility. BDPA capability improves a company's ability to make the best use of its available resources. It improves the South African mining industry's dynamic capability, allowing them to adjust their strategies in response to diverse adverse weather conditions. Furthermore, BDPA capability's ability to improve SC visibility is enhanced when there is strong alignment between BDPA strategy and initiatives. Finally, having a high level of SC visibility helps develop community and resource resilience, which are necessary to ensure that firms in the industry fulfil their responsibilities in relation to social sustainability.

Resilient supply chain network design: a systematic literature review

ABSTRACT The modern global economy has developed interconnected and complex supply chains largely due to the benefits companies have found in sophisticated trends and strategies; however, these practices are not without risk. In the wake of disruptions caused by COVID-19, natural disasters, Brexit, and the US–China trade war, supply chain resilience has become more important than ever. This study aims to provide a comprehensive review of recent literature on resilient supply chain network design (RSCND). The focus was on studies that used a quantitative approach. This study utilised a systematic literature review methodology to evaluate the body of literature on RSCND. The main contributions of this paper are as follows: (1) exploring and analysing existing literature on RSCND, particularly focusing on different types of resilience measures used from an analytical modelling perspective; (2) presenting a new way to classify the quantitative resilience measures used for RSCND and clarifying the implications of incorporating it in terms of costs and benefits; and (3) identifying the gaps and limitations of existing literature and proposing a list of potential issues for future research directions. An analysis of the literature shows that existing resilience measures mainly focus on the resilience of the nodes. The benefits of incorporating resilience measures in the RSCND are illustrated quantitatively in terms of monetary value, lost sales, and demand fulfilment. This study is the first attempt to combine studies on the RSCND using quantitative resilience measures. This study can serve as a starting point for understanding the different resilience measures discussed in the literature, how to incorporate them in designing new or redesigning existing supply chain networks, and the benefits associated with their implementation. Although only 21 studies were found in the analysis, we believe that this topic has a huge scope for future research.