A fuzzy sustainable model for COVID-19 medical waste supply chain network

The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. © 2023, Crown.

The impact on supply chain networks of shifting demand and supply dynamics

The supply chain networks that support a business have usually evolved over time, shaped by various market and supply forces and by the expectations and strategic intent of a series of leaders. Recent shocks and higher awareness of risk and shifts on both the demand and supply side are expected to require global networks to be reassessed. This chapter reviews some of the key changes impacting global chains and considers the implications for future supply chain networks and the people who will manage them. © Springer Nature Switzerland AG 2023. All rights reserved.

A PRODUCT SERVICE SUPPLY CHAIN NETWORK EQUILIBRIUM CONSIDERING RISK MANAGEMENT IN THE CONTEXT OF COVID-19 PANDEMIC

This paper studies the equilibrium decision-making problem of product service supply chain (PSSC) network under the impact of COVID-19 related risks. The PSSC is composed of service-oriented transformation of manufacturing enterprises to sell product service systems (PSSs) to customers. So, under the impact of COVID-19, the network faces dual risks of products and services. This paper constructs the PSSC network of raw material suppliers, service providers, manufacturing service integrators and demand markets. Through variational inequalities, a network equilibrium model of PSSC considering risk management was established, and their decision-making problems were discussed. Three numerical examples were used to analyse the impact of risk management on the supply chain network at various levels. The results show that the risk management of upstream and downstream enterprises will have mutual in uence, and the cost input of service risk management will benefit the entire PSSC network. Therefore, through the diversified development and improvement of services, the market demand for PSSs can be increased. © 2023,Journal of Industrial and Management Optimization. All Rights Reserved.

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.

Review of Supply Chain Management Operational Resilience, Risk, and Disruption in COVID-19 Pandemic

The largest and most significant disruption to the world's supply chain networks recently was brought on by the COVID-19 pandemic. Supply chain networks are facing unprecedented pressure to reassess their resilience although risks and disasters occur more frequently. Recent years have seen significant research on supply chain management and its importance to firm performance. Resilient supply chain management has been analyzed and explained using a variety of managerial theories. Thus, we can lay the groundwork for future supply chain resilience research by identifying trends in previous studies. Although a sizable amount of literature on resilient supply networks, only a small number of studies have gone in-depth. This article analyses supply chain management research. Based on a literature study, the amount of research on SCM theory and practice has dramatically expanded during the previous ten years. The current analysis identified the major themes, significant literature, and significant authors in supply chain resilience studies. Additional research in other sectors connected to supply chain resilience is anticipated to benefit from these findings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Breaking out of the pandemic: How can firms match internal competence with external resources to shape operational resilience?

This study explores how firms sought to effectively match their internal competence with external resources from the supply chain network to improve operational resilience (OR) during the COVID‐19 pandemic. Drawing upon matching theory, this study provides an internal–external matching perspective based on flexibility–stability features of OR to explain the operational mechanisms underlying the different matchings between internal flexibility (i.e., product diversity)/stability (i.e., operational efficiency) and external flexibility (i.e., structural holes)/stability (i.e., network centrality). We find that more heterogeneous matchings between internal (external) flexibility and external (internal) stability have a complementary effect that enhances OR, whereas more homogeneous matchings between internal flexibility (or stability) and external flexibility (or stability) have a substitutive effect that reduces OR. This study provides valuable contributions to research focusing on the supply chain, organizational resilience, and operations management.

Supply Chain Network Model using Multi-Agent Reinforcement Learning for COVID-19

The COVID-19 vaccination management in Japan has revealed many problems. The number of vaccines available was clearly less than the number of people who wanted to be vaccinated. Initially, the system was managed by making reservations with age group utilizing vaccination coupons. After the second round of vaccinations, only appointments for vaccination dates were coordinated and vaccination sites were set up in Shibuya Ward where the vaccine could be taken freely. Under a shortage of vaccine supply, the inability to make appointments arose from a failure to properly estimate demand. In addition, the vaccine expired due to inadequate inventory management, resulting in the vaccine being discarded. This is considered to be a supply chain problem in which appropriate supply could not be provided in response to demand. In response to this problem, this paper examines whether it is possible to avoid shortage and stock discards by a decentralized management system for easy on-site inventory control instead of a centralized management system in real world. Based on a multi-agent model, a model was created to redistribute inventory to clients by predicting future shortage based on demand fluctuations and past inventory levels. The model was constructed by adopting the Kanto region. The validation results of the model showed that the number of discards was reduced by about 70% and out-of-stocks by about 12% as a result of learning the dispersion management and out-of-stock forecasting © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

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.

Virtual Supply Chain Network Platform Design and Development for Crisis Response

The COVID-19 pandemic exposed the vulnerability of the Canadian economy on many fronts. When the demand for lifesaving equipment increased globally, the supply chain networks were broken by the direct involvement of other countries. The rising competition and interruptions caused Canada to face significant difficulties in global markets to secure critical medical equipment and protective materials. Not only hospitals and healthcare workers but also the public and patients had no access to the needed equipment even though companies and organizations in the country have the required capacity and resources. In such emergency times, Canada should produce the essential equipment within the country. We propose a four-step strategic product manufacturing system to ensure crisis response. The first and second steps are creating a manufacturing capability database of Canadian companies and a library of product families, respectively. These two steps should be completed before the crisis. The third step involves emergency need analysis, equipment design and forecasting. Finally, the fourth step is developing a virtual supply chain network platform through which the procurement, production, and transportation activities will be scheduled based on the capability database, product families library, and requirements analysis in the most efficient and economical way possible. The research utilizes various tools such as forecasting, optimization, simulation, multi-criteria decision making, and engineering design tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Debt default, financial risk transmission and governance from the perspective of supply chain network.

The stock risk spillovers of 31 associated enterprises of Evergrande supply chain in China were measured with DCC-GARCH and CoVaR model, and high, moderate and low risk overflow networks in four periods were constructed, finally the overall metrics and dynamic evolution of risk spillover network were explored. The results showed that: With COVID-19 under control in China, the risk spillover of Evergrande supply chain associated enterprises continues to diverge, with the quantity and scope of high risk declining and moderate and low risk rising; The infection scope of high risk spillover has narrowed, from indirect to direct infection; Evergrande subsidiaries play obvious bridge roles in moderate and low risk networks, have strong control over the risk spread; Commercial banks suffer and trigger more risk spillovers, a number of risk spillover groups with commercial banks as cores formed in high and moderate risk networks.

Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach

Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd

Additive manufacturing integration in E-commerce supply chain network to improve resilience and competitiveness

In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty. © 2022 The Authors

Covid-19 in America: Global supply chain reconsidered.

This research asks: To what extent has America's reliance on the global supply network aggravated the country's public health and economic crisis; and how did the American government respond to supply chain weaknesses during the early years of the Covid-19 pandemic? This study first assesses important conceptual considerations that explain the expansion of global value chains and the growth of trade interdependencies among nations. Next, an analytical case study observes (1) America's supply chain vulnerability through three major waves of infection, (2) the difficulty to mend weaknesses in the supply linkages once the novel coronavirus spread globally and (3) American government's failures to both anticipate and respond to supply shortages, especially in the health sector. Trump administration's policies failed to ensure a reliable supply of simple personal protective equipment (PPE) for healthcare professionals and hospitals throughout the first three waves of infection. Moreover, state and federal governments' substantial reliance on large manufacturers who have established procurement relationship with government led to continuous nationwide supply shortages throughout 2020. The federal government's inability to engage small and medium manufacturers in the production of critical supplies of PPE and diagnostic tests deepened and prolonged the devastating impacts of the pandemic. Our case study demonstrates that the American government needs to rethink the country's substantial reliance on the global supply chain, and the specific requirements to boost domestic manufacturing capacity. The revitalisation of America's manufacturing ability and the local supply networks will boost the productive power of the nation, strengthen resiliency, reduce vulnerability in disruptive times and prepare the nation for future crises.

A multi-objective optimization modelling for design and planning a robust closed-loop supply chain network under supplying disruption due to crises

Global crises such as COVID-19 pandemic and the Russian-Ukrainian war pose many challenges for closed-loop supply chain networks (CLSCN) due to the lack of supplies of raw materials and returned products. Therefore, this research focused on developing a multi-objective MILP mathematical model for the design and planning of CLSCN to help overcome these challenges considering the uncertainty in both the supplying capacity of the raw materials and the return rate of the used products.The developed models aim to maximize total profit, minimize total cost, and maximize overall cus-tomer service level (OCSL) using the e-lexicographic procedure.The effect of variation in both the supply capacity and return rate of the used products on the design and performance of the CLSCN have been studied. It is recommended to optimize the profit then the total cost with a maximum allowable deviation of 5%, and finally optimize the OCSL.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Design of humanitarian supply chain system by applying the general two-stage network DEA model

PurposeThe recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.Design/methodology/approachThis paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.FindingsAmong various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.Originality/valueThis study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

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.

Blood plasma supply chain planning to respond COVID-19 pandemic: a case study.

The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.

Design of humanitarian supply chain system by applying the general two-stage network DEA model

PurposeThe recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.Design/methodology/approachThis paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.FindingsAmong various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.Originality/valueThis study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis

Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiments (RFE) and Intelligent Attack Experiments (IAE). Specifically, this study investigates the effect of RFE and IAE across multiple tiers and scales. The global supply chain data was mined and analysed from about 45,000 firms with about 115,000 intertwined relationships spanning across 10 tiers of the backward supply chain of medical equipment. This complex supply chain network was analysed at four scales, namely: firm, country-industry, industry, and country. A notable contribution of this study is the application of a supply chain tier optimisation tool to identify the lowest tier of the supply chain that can provide adequate resolution for the study of the supply chain pattern. We also developed data-driven-tools to identify the thresholds for breakdown and fragmentation of the medical equipment supply chain when faced with random failures or different intelligent attack scenarios. The novel network analysis tools utilised in the study can be applied to the study of supply chain reachability and viability in other industries.