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. © 2023 Elsevier B.V.

Designing an IoT-enabled supply chain network considering the perspective of the Fifth Industrial Revolution: Application in the medical devices industry

The rapid growth of technology, environmental concerns, and disruptions caused by the COVID-19 pandemic have led researchers to pay more attention to an emerging concept called the fifth industrial revolution (I5.0). Despite the high importance of the I5.0, the literature shows that no study investigated the supply chain network design problem based on the I5.0 pillars. Hence, this research develops a multi-stage decision-making framework to configure a closed-loop supply chain based on I5.0 dimensions to cover this gap. In the first stage, the score of technologies that utilized in the supply chain is calculated using the analytic hierarchy process method. Afterwards, in the second stage, a mathematical model is proposed to configure the supply chain. Then, Furthermore, an efficient solution method, named the fuzzy lexicographic multi-choice Chebyshev goal programming method, is developed to obtain the optimal solution. In general, the main contributions of the current study can be divided into two major parts as follows: (i) the current study is the first research that incorporates the dimensions of the I5.0 into the supply chain network design problem, and (ii) this work develops a novel and efficient solution method. In this regard, the major problems and challenges that existed include the limitation of available resources in relation to Industry 5, especially in the field of the supply chain, as well as quantifying the elements of Industry 5.0 in the form of a mathematical programming model. © 2023

Modeling the supply chain network in the fast-moving consumer goods industry during COVID-19 pandemic

The outbreak of the COVID-19 pandemic in recent years has raised serious concerns about the distribution of fast-moving consumer goods products, given the freshness of their use. On the one hand, the distribution of fast-moving consumer goods with multiple vehicles has led to maintaining the freshness of items at the supply chain level, and on the other hand, it involves the high costs of using vehicles. Congestion of vehicles and drivers in the distribution of items has also increased the possibility of COVID-19 transmission. The importance of the above issue has led to the modeling of a multi-level supply chain problem in the FMCG industry by considering the freshness of items to reduce COVID-19 transmission. The most important issue considered in this article is to send fresh food in the shortest possible time to customers who cannot go to stores and wait in line to buy items in the conditions of Covid-19. Therefore, the designed model provides the possibility for customers to receive fresh food in addition to reducing costs and also reduce the possibility of contracting Covid-19. Designed supply chain network levels include suppliers of raw materials, manufacturers of consumer goods, distributors and end customers. In order to optimize the objectives of the problem, including minimizing the total costs of supply chain network design and maximizing the freshness of items, various strategic and tactical decisions such as locating potential facilities, routing vehicles, and optimally allocating the flow of goods should be made. Since the supply chain network model is considered to be NP-hard, meta-heuristic algorithms have been used to solve the problem by providing a modified priority-based encoding. The results show the high efficiency of the proposed solution method in a short time.

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study.

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective mixed-integer linear programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.

Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments

Supply chain (SC) resilience is imperative to cope with disruptions using some preparedness and recovery capabilities such as network redundancy (e.g., backup suppliers) and process flexibility (e.g., capacity agility). These capabilities frame an SC resilience portfolio. Both designing a resilient portfolio and recovering in case of a real disruption require investments. This paper presents a new mathematical model for designing an efficient resilience portfolio in a multi-echelon SC. Through computational and comparative analyses using a real-life case-study, we demonstrate that our model allows increasing resilience at minimal costs by determining an optimal combination of preparedness and recovery investments. Interestingly, the optimal solutions (i.e., efficient resilient SC designs) increase SC efficiency even in business-as-usual scenarios. This result contributes to the literature on transforming resilience from an expensive spend to a value-creation asset. We illustrate our approach using a real-life industrial example that allows for the identification of important relations between disruption duration/magnitude and the efficiency of preparedness and recovery strategies. Based on computational, comparative, and case-study analyses, we deduce and generalize managerial implications at the network, supplier, and manufacturer levels. We take an extra step by extrapolating our major findings and generalized managerial implications toward the COVID-19 pandemic setting. The outcome of our research can be instructive for SC managers when deciding on investments in resilient redundancy allocation as a part of preparedness strategy and efficient recovery deployment.

E-commerce supply chain network design using on-demand warehousing system under uncertainty

During the COVID-19 pandemic, e-commerce retailers have had trouble satisfying the growing demand because of limited warehouse capacity constraints. Fortunately, an on-demand warehousing system has emerged as a new alternative to mitigate warehouse capacity issues. In recent years, several studies have focused on the supply chain problem considering on-demand warehousing. However, there is no study that deals simultaneously with inherent uncertainties and the property of commitment, which is the main advantage of on-demand warehousing. To fill these research gaps, this paper presents an e-commerce supply chain network design problem considering an on-demand warehousing and decisions for commitment periods. We propose the two-stage stochastic programming model that captures the inherent uncertainties to formulate the presented problem. We solve the proposed model utilizing sample average approximation combined with the Benders decomposition algorithm. Of particular note, we develop a method to generate effective initial cuts for improving the convergence speed of the Benders decomposition algorithm. Computational results show that the developed method could find an effective feasible solution within a reasonable computational time for problems of practical size. Furthermore, we show the significant cost-saving effects, based on experiment results, that occur when an on-demand warehousing system is used for designing supply chain networks.

Viable healthcare supply chain network design for a pandemic.

The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model's applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

Resilient supply chain network design: a systematic literature review

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. [ FROM AUTHOR] Copyright of Transport Reviews is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption.

The occurrence of the COVID-19 pandemic is a disruption that has adversely affected many supply chains (SCs) around the world and further proved the necessity of combination and interaction of resilience and sustainability. In This paper, a multi-objective mixed-integer linear programming model is developed for responsive, resilient and sustainable mixed open and closed-loop supply chain network design (SCND) problem. The uncertainty of the problem is handled with a hybrid robust-stochastic optimization approach. A Lagrangian relaxation (LR) method and a constructive heuristic (CH) algorithm are developed for overcoming problem complexity and solving large-scale instances. In order to assess the performance of the mathematical model and solution methods, some test instances are generated. The computations showed that the model and the solution methods are efficient and can obtain high-quality solutions in suitable CPU times. Other analyses and computations are done based on a real case study in the tire industry. The results demonstrate that resilient strategies are so effective and can improve economic, environmental and social dimensions substantially. Research findings suggest that the proposed model can be used as an efficient tool for designing sustainable and resilient SCs and the related decision-makings. Also, our findings prove that resilience is necessary for continued SC sustainability. It is concluded that using proposed resilience strategies simultaneously brings the best outcome for SC objectives. Based on the sensitivity analyses, the responsiveness level significantly affects SC objectives, and managers should consider the trade-off between responsiveness and their objectives.