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An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey
Abstract
Drawing upon economic and environmental sustainability, this study explores how developing the operational resilience of the medical supply chain (MSC) contributes to maintaining healthcare in the face of disruption risks, such as the COVID-19 pandemic. To this end, an optimization-based roadmap is proposed by employing lean tools to achieve and realize MSC resilience. A novel two-stage stochastic optimization model and robust counterpart are developed with the objective of overall cost minimization to cope with the unknowable demand uncertainty represented by scenarios. The reason behind proposing a scenario-based stochastic model is to implement preparedness strategies during the (re)design phase by making strategic and operational level decisions. That being the case, seven cases are generated based on the demand uncertainty intervals along with seven different reliability levels for sensitivity analysis. Computational experiments are conducted through a real case study to compare the centralized and decentralized distribution models in terms of efficiency and responsiveness. The results obtained by the stochastic model and robust counterpart are compared to demonstrate how strong the proposed model is. On top of that, lean tools are used to visualize and analyze the improvement opportunities to contribute to the methodology. By doing so, this paper presents novel theoretical and empirical insights regarding MSC resilience. The computational results emphasize the importance of employing a pre-disruption strategy via the proposed methodology to design a resilient MSC to be prepared for pandemic-related risk. The findings from the sensitivity analysis also verify that regardless of the disruption degree, the developed roadmap with the centralized distribution model leads to up to 40% improvements in terms of the overall cost, order lead time, emission amount, and inventory shortage metrics.
... Accordingly, the COVID-19 pandemic exposed the critical vulnerabilities and fundamental flaws of global SCs. It highlighted that these chains, often conceived as the central nervous system of our economy, society, and environment, were inadequately prepared to deal with such disruptions [8,9]. ...
... Nevertheless, there is a dearth of comprehensive discourse in the literature, particularly within critical sectors like healthcare, regarding the intricate connections, interdependencies, potential benefits, and drawbacks, as well as optimal methodologies associated with these concepts [14]. The pandemic has emphasized the crucial need for sustainable development in various supply chain networks, including the supply chain for essential items like vaccines, test kits, PPEs, etc [9]. A sustainable supply chain can help minimize social risks and environmental impacts associated with mask production, necessitating production, distribution, and disposal centers to be located far from densely populated urban areas. ...
... 8 The occurrence possibility of pandemic severities is distinguished by the proposed IoT architecture. 9 The range of parameters for different pandemic severity is known and certain. 10 Each product has its holding capacity due to different warehousing conditions. ...
Pandemics, such as the Influenza virus and the contemporary COVID-19, can lead to widespread disruptions in key segments, including the supply chain, beyond the capacity of communities or governments. The establishment of a robust relief supply chain network can alleviate the destructive effects of pandemics and strengthen the distribution of relief supplies across medical centers and demand zones. Furthermore, the Internet of Medical Things (IoMT), which connects medical devices and applications over the internet, provides healthcare providers with real-time data collection, transmission, and sharing. Considering this motivation, in this work, a multi-objective sustainable network is firstly modeled mathematically to not only curb the direct flow of relief supplies among specific components, but also to operate the reverse flow of waste within the network. Furthermore, in accordance with the Sustainable Development Goals (SDGs), the model emphasizes the energy consumption of critical activities like production and transportation. Additionally, an IoMT configuration is propounded to strengthen the mathematical model with real-time data. metaheuristic optimizers are effective toolkits owing to the NP-hardness of the model. To ensure that the model is compatible and applicable under varying conditions, a suite of tuned metaheuristic optimizers is utilized as well as five scales of test problems. Additionally, the performance of optimizers is examined using a number of recognized performance indicators. The normality of the results is evaluated through statistical tests, namely the Kolmogorov-Smirnov and Shapiro-Wilk tests. Following this assessment, a comprehensive analysis is carried out using the Wilcoxon test and Paired-Samples T Test to compare the results in a pairwise manner, while maintaining a significance level of 0.05. The outcomes derived from these tests reveal the presence of significant disparities among the performance indicators. To ascertain the algorithm with superior performance, an evaluation is conducted using interval plots and the Friedman test, considering each individual performance indicator. The empirical evidence derived from this analysis indicates that the Multi-objective Seagull Optimization Algorithm (MOSOA) exhibited the highest overall mean rank score of 1.93, surpassing other metaheuristic algorithms in terms of performance.
... Efficient and resilient HSCs ensure the continuous delivery of essential medical supplies, particularly during crises such as pandemics, natural disasters, or conflicts. These challenges make resilience in HSCs a cornerstone for public health (Yılmaz et al., 2023). HSC encompasses a vast network of processes and components aimed at ensuring the timely manufacturing, distribution, and delivery of medicines and healthcare supplies to patients (Beaulieu & Bentahar, 2021;Kumar et al., 2023aKumar et al., , 2023b. ...
... HSC encompasses a vast network of processes and components aimed at ensuring the timely manufacturing, distribution, and delivery of medicines and healthcare supplies to patients (Beaulieu & Bentahar, 2021;Kumar et al., 2023aKumar et al., , 2023b. That being the case, in the contemporary world, especially considering disruptions like natural disasters, labor strikes (Ivanov & Dolgui, 2021), and infectious disease outbreaks such as SARS, Ebola, and the unprecedented COVID-19 pandemic (Araz et al., 2020), enhancing the HSC is of utmost significance to ensure the efficient and continuous delivery of healthcare services, adaptability to swiftly changing circumstances, and the secure and timely provision of healthcare products (Özçelik et al., 2021;Yılmaz et al., 2023). For example, the COVID-19 pandemic vividly illustrated this necessity, as disruptions in global supply chains led to severe shortages of personal protective equipment and vaccines, emphasizing the critical need for resilient HSCs capable of adapting to unforeseen circumstances. ...
Ensuring resilience in healthcare supply chains (HSCs) is crucial for maintaining the timely, accurate, and reliable delivery of common goods in health, especially in the face of disruptions such as pandemics or natural disasters. This study investigates the role of Industry 4.0 strategies in enhancing HSC resilience through a two-stage optimization-oriented methodology integrating Fermatean fuzzy numbers (FFNs), the Best Worst Method (BWM), and goal programming (GP). In the first stage, FF-BWM is used to determine the weights of HSC resilience factors derived from the SCOR model, based on expert evaluations. In the second stage, Industry 4.0 strategies are evaluated under these criteria, and the optimal strategy is identified using GP. The results reveal that among the evaluated strategies, the Internet of Things (IoT) is the most effective in enhancing overall HSC resilience. Blockchain and Digital Twin also demonstrate significant potential in this regard. Furthermore, the analysis highlights that Digital Twin excels in the Plan and Transform processes, Blockchain is most effective in the Order and Return processes, Machine Learning performs best in the Source process, and IoT leads in the Fulfill process. These findings provide actionable insights for decision-makers to prioritize digital investments and strengthen HSCs against potential disruptions. This study underscores the transformative role of digitalization in optimizing HSC operations and fortifying their resilience.
... Preventive strategies aim to reduce the system vulnerability before a disruption incidence, while reactive strategies help to minimize and recover from the damages caused by a disruptive event after it occurs (Vali-Siar and Roghanian, 2022). Various studies have been conducted on resilience in the supply chain and organization, including those undertaken by Yılmaz et al. (2023), Ghasemi et al. (2022), Sadrabadi et al. (2021), GÜRSOY and KARA (2021), Yılmaz et al. (2021), Özçelik et al. (2021), and Goodarzian et al. (2021). Yılmaz et al. (2023) proposed an optimization-based roadmap with lean tools to design a medical supply chain that can deal with pandemic-related disruptions and uncertainty. ...
... Various studies have been conducted on resilience in the supply chain and organization, including those undertaken by Yılmaz et al. (2023), Ghasemi et al. (2022), Sadrabadi et al. (2021), GÜRSOY and KARA (2021), Yılmaz et al. (2021), Özçelik et al. (2021), and Goodarzian et al. (2021). Yılmaz et al. (2023) proposed an optimization-based roadmap with lean tools to design a medical supply chain that can deal with pandemic-related disruptions and uncertainty. They applied a novel two-stage stochastic optimization model and robust counterpart to a case study from Turkey, showing some significant cost and performance metrics improvements. ...
Abstract
Purpose-The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance, which is discussed in this study.
Design/methodology/approach-This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented ε-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model. Findings-The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning, and direct transportation of products to markets are the most efficient resilience strategies in the understudy industry.
Originality/value-Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.
... Ignoring uncertainty in the design of a reverse logistics network or integrated forward/ reverse logistics network affords a static logistics network, such as that associated with largescale, large-volume centralised production. In such a network, the uncertainty of product quality and quantity of recovered products has a low impact [3]. However, actual manufacturing supply chain logistics networks operate in complex and dynamic environments. ...
The inherent unpredictability within the low-carbon integrated supply chain logistics network complicates its management. This paper endeavours to address the challenge of designing a low-carbon logistics network within a context of uncertainty and with consideration of low-carbon policies. It also endeavours to identify locations of facilities and appropriate transportation routes between nodes. Robust optimisation and fuzzy programming techniques are employed to examine the various attributes of the network. In addition, the strategic planning model of a multi-level forward/reverse integration logistics network is examined, with the aims of cost minimisation and emission reduction. Extensive computational simulations substantiate the efficacy of the proposed robust fuzzy programming model. Moreover, analytical results indicate the rationality and applicability of the decisions suggested by the proposed optimisation model and the solution approach. Furthermore, the results indicate that a decision maker can ascertain that the decisions derived from three cases considered have a 50% probability of being the most favourable outcomes.
... Ensuring the uninterrupted availability of high-demand medical products from suppliers is crucial to prevent delays or gaps in meeting healthcare needs, as any disruption can have severe consequences for patient outcomes [3]. These challenges have underscored the imperative to enhance the flexibility of MSCs to address sudden demand fluctuations and supply disruptions, highlighting the critical role of resilience in maintaining continuity of healthcare services [4]. ...
Recent global crises, particularly the COVID-19 pandemic, have exposed significant vulnerabilities in medical supply chains (MSCs), underscoring the necessity for resilient strategies to manage demand fluctuations and disruptions. This study addresses the resilient MSC network design problem under demand uncertainty. To tackle this problem, a robust optimization model is proposed, aiming to maximize the total quantity of medical products delivered to hospitals. The model incorporates supplier and warehouse opening and allocation decisions using a risk-averse approach that ensures reliable delivery while managing operational costs. Small-sized instances of the problem are solved using GAMS 24.7/CPLEX. Computational results reveal that increasing the the level of robustness enhances the MSC resilience but leads to higher costs due to additional resource allocation. These findings provide strategic insights into designing MSC networks that balance cost-effectiveness and resilience, ensuring uninterrupted delivery of critical medical supplies during crises.
... Their model employed a robust stochastic optimization approach to address uncertainty, providing a practical heuristic method to manage complexity. Yılmaz et al. (2023) examined the need for enhancing the operational resilience of medical supply chains in response to disruptions like the COVID-19 pandemic, proposing an optimization-based framework that integrates lean tools to improve both resilience and responsiveness. Table 2. offers a summary of the main attributes found in papers closely related to our research. ...
Reevaluating the food supply chain network is crucial in today’s fast-changing world. This paper dives into the process of redesigning the food supply chain network. It considers the impact of pandemic disruptions and multiple aspects of uncertainty, including random and deep ones, while highlighting the importance of resilience and responsiveness. The discussion covers various aspects of this redesign effort, including facility location decisions, inventory management, and distribution strategies. A hybrid robust stochastic optimization approach is used to handle the problem uncertainty. Furthermore, the paper investigates the integration of resilience strategies, including fortification, capacity expansion, and dual-channel distribution, to enhance the network's ability to withstand disruptions. Also, the Benders Decomposition algorithm is employed to address large-scale instances. A set of test instances are generated to assess the model's performance. Across 15 test problems, the proposed method consistently exhibited a smaller relative error, substantially improving 22 percent compared to the nominal approach. These findings highlight the model’s potential as a valuable tool for designing resilient and responsive supply chains to aid decision-making.
... Traditional manual counting methods are prone to errors and inefficiencies, necessitating a comprehensive and integrated approach to address the plate counting problem effectively. These limitations can lead to inaccuracies in inventory counts and cause wasted resources, delayed production, and inefficiencies that directly impact operational costs and productivity [3]. ...
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking.
... At its core, lean thinking revolves around the reduction of waste and the pursuit of operational excellence. Initially conceived in the context of manufacturing, lean principles seek to eliminate non-value-added activities, overproduction, inventory excess, waiting times, and defects (Yılmaz et al., 2023). When applied to the broader SC, these concepts emphasize the optimization of inventory management, precise demand forecasting, and a relentless commitment to continuous improvement. ...
... Then, the decisions on location of distribution facilities, inventory rules, and routing were made in the second phase. Yılmaz et al. (2023) introduced a novel two-stage stochastic optimization model integrated with lean tools to improve the resilience of the medical supply chain during a pandemic. The findings of the study highlighted the significance of utilizing a pre-disruption strategy. ...
Due to the unforeseen outcomes of the recent COVID-19 pandemic, the adoption of essential policies and tactics to implement public vaccination programs has gained attention worldwide. Statistics indicate that medical waste production is growing dramatically, posing a major risk to both individuals and the environment if not properly handled. In this context, we present a sustainable fuzzy multi-objective, multi-period, multi-product, and location-allocation model that considers both the vaccine distribution phase and the medical waste management process. The model addresses the capacitated and multi-depot vehicle routing problem (VRP) with heterogeneous vehicles. Moreover, as a new social aspect from vaccination centers’ perspective, a fuzzy version of the time-window is considered in the model to integrate both satisfaction level and priority of the nodes that must be visited. Subsequently, the augmented ε-constraint, TH method, and LP-Metric approach are utilized to solve the proposed multi-objective model. To provide empirical support for the model’s input parameters and evaluate the effectiveness of the solution strategies, data for a medical supply chain network (MSCN) in Iran is used. The results demonstrate that shifting the vaccination centers’ desired time to be serviced might be a suitable solution to stop the emission rate from increasing or the satisfaction levels from falling.
... In contrast to supply chain resilience, e.g. Ivanov (2021a), which is considered at the moderate severity disruptions level, the supply chain viability is related to severe crises such as pandemic, when the resilience of various supply chains, in particular medical supply chains becomes crucial, see the recent paper by Yilmaz et al. (2023). In a recent editorial paper (Ivanov et al. 2023), the authors noticed that: 'Supply chain viability extends resilience and risk management knowledge towards survivability under conditions of long-term and unpredictably scaling disruptions. ...
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.
... Therefore, in order to analyze and manage the total flow of the company under analysis, its suppliers and customers must be represented. Thus, to implement improvements, three situations are expected: the analysis of the initial situation (value stream mapping) and identification of improvement opportunities; mapping of the future situation (value stream design) and, finally, implementing the changes [23,43]. ...
Background: Nowadays, as a result of globalization, markets are more competitive, and customers are more demanding. To respond to these challenges, organizations must develop mechanisms for continuous improvement in order to eliminate waste and increase the efficiency and effectiveness of processes. Thus, the present study carried out at an industrial unit responsible for the customization of cork stoppers for wines had as its main objectives to identify and eliminate or at least reduce waste; improve production and internal logistics flows; balance workloads; improve productivity; reduce lead time; motivate employees and promote the spirit of continuous improvement. Methods: The action-research methodology was used, whereby several cycles of data recovery and analysis, identification and implementation of opportunities for improvement, assessment and standardization were carried out. Therefore, the Total Flow Management (TFM) model was implemented, and several methods and tools were used, such as Value Stream Mapping (VSM), work measurement and 5S’s. Results: Several wastes and overloads were identified, and some actions were implemented, such as workload balancing, layout changes, implementation of visual management and supermarkets. That said, it was possible to reduce lead time by 4 days, improve productivity from 26.63 ML (a thousand cork stoppers)/h to 35.75 ML/h, and promote flexibility. In addition, employees were motivated, and a culture of continuous improvement was fostered. Conclusions: This project demonstrated that it is possible to implement improvement actions, with good results, without high investments, as well as motivating employees and taking advantage of their best capabilities. Additionally, it was demonstrated that the use of TFM can be very useful in continuous improvement, with evident improvements in production and internal logistics flows. So, this project demonstrated the practical implementation of TFM regarding basic reliability, production and internal logistics flow, and the simultaneous use of several methods and tools to implement continuous improvement. Thus, significant improvements were possible on the factory floor, as well as improving employee motivation their personal development and encouraging the focus on continuous improvement. Therefore, it responds to the gap identified in the literature.
Purpose
Supply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities.
Design/methodology/approach
In the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability.
Findings
For the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards.
Originality/value
The current SLR found research gaps in AI-based SCRM and proposed directions for future studies.
Purpose
This research aims to analyze the deployment of lean practices and resilience capabilities within the healthcare supply chain across different disruptive scenarios. The study addresses the gap in how different tier levels of the healthcare supply chain integrate lean and resilience.
Design/methodology/approach
Employing a case study approach, the research evaluated four Italian organizations (two healthcare providers, one pharmaceutical distributor and one pharmaceutical producer) representing the three main tier levels of the healthcare supply chain. The methodology involved a questionnaire assessing the adoption of specific lean practices and resilience capabilities, followed by a scenario analysis by experts used to identify critical practices and capabilities across different disruptive scenarios.
Findings
This research systematically identified critical lean practices and resilience capabilities that are underutilized at various tier levels within the healthcare supply chain, highlighting significant opportunities for theoretical advancement in operational efficiency and system robustness during disruptions. Additionally, the study introduced a novel methodological approach to evaluate the effectiveness of lean and resilience practices across different disruptive scenarios, thereby enriching the theoretical framework for crisis management within healthcare operations. Finally, we emphasized the crucial roles of just-in-time and anticipation capability in bolstering the performance of all the healthcare supply chain.
Originality/value
The study contributes to the fields of supply chain management and healthcare by systematically identifying and classifying the importance of lean practices and resilience capabilities in managing disruptions. Additionally, the potential for cross-tier collaboration and knowledge sharing to enhance overall supply chain resilience is highlighted.
This study introduces a model that combines dynamic disease modeling and an optimization approach for drone-based vaccine delivery to achieve fair distribution and enhance equity in vaccine access across different regions, including rural areas and small cities. Our approach aims to achieve optimal allocation of vaccines by considering regional infection rates and equilibrium vaccination rates, which allows us to forecast vaccine demand effectively. To achieve this, we employ a region-specific dynamic disease model that considers population size, infection rates, and vaccination rates. Utilizing this dynamic disease model with a well-structured delivery network minimizes travel and healthcare costs resulting from insufficient vaccination delivery while ensuring equitable distribution. Our model also considers logistical factors specific to drone vaccine delivery, including routing and recharging plans, payload capacity, flight range, and regional vaccine demand. These considerations are crucial to addressing the unique challenges rural areas and small cities face in accessing healthcare services. This study also investigates the essential trade-offs between minimizing delivery costs and mitigating healthcare burdens during a pandemic response. We study drone vaccine delivery during the COVID-19 pandemic to validate our model, explicitly focusing on Orange County (OC) and small cities. The results of this study have important practical implications for designing drone-based vaccine delivery systems that prioritize fairness and equitable access, especially in smaller cities and rural areas. It highlights that cities with lower populations but higher transmission rates may require more vaccines, while larger cities with lower rates need fewer.
The concept of viability has become increasingly prominent in recent years, driven by the inadequacies of the existing frameworks, particularly following the disruptions caused by the pandemic. This growing focus on viability, especially within the context of medical supply chains (MSC), has garnered significant attention in the literature as researchers seek to address the challenges of ensuring continuous medical products delivery. This study conducts a comprehensive review of 516 research papers retrieved from academic databases, including Google Scholar, Scopus and ScienceDirect, employing selection criteria to assess studies for developing viable MSC networks. Furthermore, the review examines the interconnections between viability, resilience, robustness, and sustainability to identify the key drivers of this emerging concept. The findings reveal an increasing emphasis on viability within MSC research while also exposing critical gaps in the existing literature. By identifying these gaps, this study contributes to a deeper understanding of viable MSC network problems and provides valuable insights for future research.
The COVID-19 pandemic has exposed critical vulnerabilities in medical supply chains (MSCs), leading to severe and long-lasting disruptions characterized by the ripple effect. Traditional risk mitigation strategies have proven inadequate for ensuring the resilience and long-term viability of MSCs in such volatile environments. This paper aims to design a resilient MSC by developing a hybrid risk management framework that enhances supply chain adaptability and survivability in the post-pandemic era. To address this issue, a risk-averse two-stage stochastic programming model that integrates Conditional Value at Risk (CVaR) and chance constraints (ChanceCon) is proposed to effectively manage the risk of unsatisfied demand. The hybrid CVaR-ChanceCon approach allows for a more comprehensive risk assessment by combining the benefits of both methods. To efficiently solve the complex optimization problem, a novel math-heuristic algorithm is developed that generates high-quality solutions within reasonable computational times. Compared to traditional risk measures and solution methods, the proposed hybrid framework demonstrates significant advantages in balancing cost efficiency and service level requirements. Key findings from extensive computational experiments reveal that the proposed method effectively reduces expected shortages, stabilizes supplier and warehouse utilization decisions, and enhances overall MSC resilience under various disruption scenarios. Policy implications suggest that adopting this hybrid risk management approach can substantially improve the preparedness and responsiveness of MSCs to future disruptions. It is recommended that policymakers and supply chain managers incorporate advanced risk aversion strategies like the CVaR-ChanceCon method to ensure the continuous supply of medical products, thereby safeguarding public health during crises.
This paper addresses the medical kit allocation problem by employing a unified robust stochastic programming (URSP) approach to enhance medical supply chain (MSC) viability during pandemics. A two-stage methodology is developed to account for the inherent uncertainty of demand. It begins with a machine learning (ML) algorithm for contagion level prediction, which adjusts demand forecasts accordingly. Subsequently, the URSP approach incorporates risk aversion and various types of uncertainty by combining stochastic programming and robust optimization through an adjustable weight in the objective function. As a risk-aversion technique, conditional value-at-risk (CVaR) is employed to restrict shortage levels, providing a more realistic assessment of MSC resilience. To balance cost-effectiveness and robustness against a spectrum of uncertainties, the URSP method leverages the strengths of both stochastic programming and robust optimization. Taguchi's orthogonal array design is utilized to generate cases representing combinations of government policies aimed at mitigating potential risks during future epidemics or pandemics. The effectiveness of the proposed methodology is demonstrated through a comprehensive case study conducted in Türkiye, comparing several modeling approaches. Extensive experiments under different types of uncertainties are performed to assess MSC viability. Computational analysis reveals that the URSP approach provides more robust and computationally tractable solutions than the purely stochastic approach and offers more cost-effective kit allocation decisions than the purely robust approach by allowing decision-makers to fine-tune the robustness level based on their priorities. The insights indicate that integrating ML predictions with URSP significantly enhances MSC viability to withstand deep uncertainties during pandemics.
This study proposes a method to support product architecture design considering the supply chain. Previous studies on product architecture design include methods to realize modularization and product family design based on the relationships among components. From the supply chain perspective, mathematical models and simulation models have been proposed to improve the supply chain of modularized products. However, there has been little research on design support that simultaneously considers product architecture and supply chain. In particular, there is little consideration given to the impact on the supply chain caused by the connection between components. To address this issue, this study proposes a design methodology for the simultaneous design of both product architecture and supply chain from the perspectives of economy, environment, quality, and transportation. This paper newly proposes an evaluation model that incorporates the production capacity of each supplier, the delivery capacity of each transportation mode, and the on-time delivery rate with consideration of above component connections. The proposed method supports the selection of an architecture and its supply chain by visualizing the characteristics, such as the trade-off between evaluation indicators depending on the degree of modularization. As a result of applying the proposed method to the architectural design of a laptop computer, we confirmed that the proposed method can suggest to the designer an appropriate architecture and its supply chain according to the number of products to be manufactured and their means of transportation.
Purpose
The purpose of this study is to comprehensively explore the impact of digitalization on healthcare supply chains (HcSCs). It seeks to understand how digital technologies enhance efficiency, transparency and responsiveness within these complex logistical systems. The study aims to provide a holistic view of the transformative potential of digitalization in the healthcare sector, with a particular focus on improving patient care and streamlining operational processes.
Design/methodology/approach
This research employs a systematic review methodology, carefully curating a selection of 45 relevant articles from 66 articles rigorously screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to provide a holistic view. It follows established systematic review protocols, incorporating a meticulous search strategy and precise keyword selection. The chosen research design enables a comprehensive examination of the existing body of knowledge concerning digital platforms, real-time tracking technologies, transparency and responsiveness in the context of HcSCs.
Findings
The findings of this study emphasize the pivotal role of digital technologies in reshaping HcSCs. Digital platforms, real-time tracking systems and technological integrations substantially enhance efficiency, transparency and responsiveness. Data-driven decision-making, improved communication and agile responses to dynamic demands are key aspects. These findings underscore the transformative impact of digitalization on healthcare logistics, emphasizing the potential for streamlined operations, enhanced patient care and more efficient resource allocation.
Research limitations/implications
Despite the systematic methodology, this study is subject to certain limitations. It relies on existing literature, which may not cover the most recent developments in the rapidly evolving field of digital HcSCs. Furthermore, the study may be influenced by publication bias. The implications suggest the need for continued research to explore emerging digital technologies and their effects on healthcare logistics, ensuring that supply chains remain agile and responsive.
Practical implications
The practical implications of this research are significant for HcSC managers with insights into digital technologies to enhance transparency and collaboration and improve resource visibility. The integration of data analytics can lead to more effective inventory management and demand forecasting. Blockchain (BC) technology can ensure transparent and secure transactions, fostering trust among stakeholders. For practitioners, this research offers actionable guidance for navigating the digital age, promoting operational efficiency and ensuring a consistent supply of essential medical products. Researchers can use these insights as a foundation for further exploration into the potential of digitalization in HcSCs.
Social implications
The social implications of digitalization in HcSCs are far-reaching. They encompass improved patient care, as digital technologies enhance the efficiency, transparency and responsiveness of supply chains. This translates to better access to critical medical supplies, potentially reducing healthcare disparities and benefiting underserved populations. Enhanced patient safety is a significant social outcome, as transparent and secure transactions enabled by technologies like BC mitigate the risks associated with counterfeit medications. Furthermore, digitalization builds trust among stakeholders, promotes accountability and fosters resilient healthcare systems, which are capable of responding effectively to crises. It also has the potential to make healthcare more affordable, contributing to increased healthcare access and transparency in decision-making.
Originality/value
The originality and value of this study lie in its comprehensive synthesis of diverse findings related to digitalization in HcSCs. While prior studies have examined isolated facets of digital technology adoption, this research provides a comprehensive overview. It contributes to a deeper understanding of the transformative potential of digitalization within the healthcare sector, offering practical approaches to enhance patient care and streamline operations.
The tragic consequences of the COVID-19 epidemic highlight to governments the importance of keeping sufficient medical supplies. Given that the physical stocks are subject to considerable obsolescence risk, existing studies have considered combining holding safety stocks with keeping production capacity or capital reserve. Pioneering the exploration of keeping all three resources of safety stocks, production capacity, and capital in reserve, we derive closed-form optimal reserve policies under different scenarios. One crucial insight is that the optimal reserve policies should be determined by considering both the cost and utilization efficiency, e.g., solely adopting safety stocks is optimal when the utilization efficiency of safety stocks exceeds that of production capacity and capital reserves, and when the expected benefit of utilizing safety stocks outweighs the cost. Additionally, we find that the optimal safety stocks level is impacted by the combination of demand uncertainty and epidemic outbreak probability, i.e., the optimal safety stocks level decreases (resp., increases) with demand uncertainty in case with a sufficiently low (resp., high) epidemic outbreak probability, whereas the optimal levels of production capacity and capital always increase with demand uncertainty. We show that coordinating the three resources brings extra benefits compared with keeping fewer resources. By exploring the practical scenario where the government faces multiple potential epidemics, we find that below a threshold, a lower outbreak probability of a large-scale epidemic leads to more capital and production capacity reserves.
Purpose
This study aims to address challenges related to long lead time within a hazelnut company, primarily attributed to product quality issues. The purpose is to propose an integrated lean-based methodology incorporating a continuous improvement cycle, drawing on Lean Six Sigma (LSS) and Industry 4.0 applications.
Design/methodology/approach
The research adopts a systematic approach, commencing with a current state analysis using VSM and fishbone analysis to identify underlying problems causing long lead time. A Pareto analysis categorizes these problems, distinguishing between supplier-related issues and deficiencies in lean applications. Lean tools are initially implemented, followed by a future state VSM. Supplier-related issues are then addressed, employing root cause analyses and Industry 4.0-based countermeasures, including a proposed supplier selection model.
Findings
The study reveals that, despite initial lean implementations, lead times remain high. Addressing supplier-related issues, particularly through the proposed supplier selection model, significantly reduces the number of suppliers and contributes to lead time reduction. Industry 4.0-based countermeasures ensure traceability and strengthen supplier relationships.
Originality/value
This research introduces a comprehensive LSS methodology, practically demonstrating the application of various tools and providing managerial insights for practitioners and policymakers. The study contributes theoretically by addressing challenges comprehensively, practically by showcasing tool applications and managerially by offering guidance for system performance enhancement.
Purpose – The ripple effect (i.e., disruption propagation in networks) belongs to one of the central pillars in supply chain resilience and viability research, constituting a type of systemic disruption. A considerable body of knowledge has been developed for the last two decades to examine the ripple effect triggered by instantaneous disruptions, e.g., earthquakes or factory fires. In contrast, far less research has been devoted to study the ripple effect under long-term disruptions, such as in the wake of the COVID19 pandemic.
Design/methodology/approach – This study qualitatively analyses secondary data on the ripple effects incurred in automotive and electronics supply chains. Through the analysis of five distinct case studies illustrating operational practices used by companies to cope with the ripple effect, we uncover a disruption propagation mechanism through the supply chains during the semiconductor shortage in 2020-2022.
Findings – Applying a theory elaboration approach, we sequence the triggers for the ripple effects induced by the semiconductor shortage. Second, the measures to mitigate the ripple effect employed by automotive and electronics companies are delineated with a cost-effectiveness analysis. Finally, the results are summarised and generalised into a causal loop diagram providing a more complete conceptualisation of long-term disruption propagation.
Originality/value – The results add to the academic discourse on appropriate mitigation strategies. They can help build scenarios for simulation and analytical models to inform decision-making as well as incorporate systemic risks from ripple effects into a normal operations mode. In addition, the findings provide practical recommendations for implementing short- and long-term measures during long-term disruptions.
In this chapter, stochastic optimization of CVaR (Conditional Value-at-Risk) 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 optimization models are developed with the two conflicting objectives: minimization of Conditional Cost-at-Risk and maximization of Conditional Service-at-Risk. Then, the risk-averse viable production trajectory at between the two boundaries is selected using stochastic quadratic MIP 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 optimization which moves production to the corresponding boundary of the viability space should not be applied under severe disruption risks to avoid greater losses. The major decision-making insights are summarized at the end of this chapter.
In this paper, we propose a new mathematical optimization approach to make decisions on the optimal design of the complex logistic system required to produce biogas from waste. We provide a novel and flexible decision-aid tool that allows decision makers to optimally determine the locations of different types of plants (pretreatment, anaerobic digestion, and biomethane liquefaction plants) and pipelines involved in the logistic process, according to a given budget, as well as the most efficient distribution of the products (from waste to biomethane) along the supply chain. The method is based on a mathematical optimization model that we further analyze and that, after reducing the number of variables and constraints without affecting the solutions, is able to solve real-size instances in reasonable CPU times. The proposed methodology is designed to be versatile and adaptable to different situations that arise in the transformation of waste to biogas. The results of our computational experiments, both in synthetic and in a case study instance, prove the validity of our proposal in practical applications. Synthetic instances with up to 200 farms and potential locations for pretreatment plants and 100 potential locations for anaerobic digestion and biomethane liquefaction plants were solved, exactly, within <20 min, whereas the larger instances with 500 farms were solved within <2 h. The CPU times required to solve the real-world instance range from 2 min to 6 h, being highly affected by the given budget to install the plants and the percent of biomethane that is required to be injected in the existing gas network.
The detrimental effects of excessive fossil fuel consumption have recently become a major global concern. Moreover, fuel supply chains are vulnerable to disruptive and operational risks. This study proposes a robust-stochastic approach to design a resilient sustainable biodiesel supply chain from Jatropha and waste cooking oil, while facilities and transportation links are vulnerable to multiple site-dependent disruptions. The main contributions are implementing preventive resiliency strategies to manage risks, integrating sustainability and resiliency, and selecting biodiesel conversion technology based on byproducts’ demands. Herein, candidate locations are identified to cultivate Jatropha using the best-worst method and geographical information system. Moreover, the supply chain network is designed to optimize economic performance, network non-resiliency, and social impacts. The Monte Carlo simulation approach, k-means clustering, augmented epsilon constraint, and Mulvey robust optimization are adopted to generate and reduce probable scenarios, manage multiple objectives, and handle uncertainty, respectively. The results showed that the non-resilient supply chain experiences a 20.11% additional cost in disruption scenarios; however, even with a higher initial cost, adopting resiliency strategies reduces disruption cost effects by 9.46 %. Managerial insights have demonstrated that resilience and sustainability are positively correlated in biofuel supply chain management, and rising biodiesel demand enhances social welfare and economic performance.
In today’s digital age, businesses are tasked with adapting to rapidly advancing technology. This transformation is far from simple, with many companies facing difficulties navigating new technological trends. This paper highlights a key segment of a comprehensive strategic model developed to address this challenge. The model integrates various planning and decision-making tools, such as the Balanced Scorecard (BSC), Objectives and Key Results (OKR), SWOT analysis, TOWS, and the Spherical Fuzzy Analytic Hierarchy Process (SFAHP). Integrating these tools in the proposed model provides businesses with a well-rounded pathway to manage digital transformation. The model considers human elements, uncertainty management, needs prioritization, and flexibility, aiming to find the optimal balance between theory and practical applications in real-world business scenarios. This particular study delves into the use of SFAHP, specifically addressing the challenge of effectively selecting the most suitable strategy among various options. This approach not only brings a new perspective to digital transformation but also highlights the importance of choosing the right strategy. This choice is crucial for the overall adaptation of businesses. It shows how carefully applying the SFAHP method is key. Combining this with a successful digital transformation strategy is essential. Together, they provide practical and efficient solutions for businesses in a fast-changing technological environment.
The purpose of this study was to identify and exhibit the interrelationships among COVID-19's impacts on supply chain activities. Based on a literature review and the manager's input , twenty COVID-19 impacts were collected. An integrated approach of exploratory factor analysis (EFA) and grey-decision-making trial and evaluation laboratory (G-DEMATEL) was used to reveal the causal interrelationships among the COVID-19 impacts. Initially, a questionnaire survey was administered among 220 respondents for EFA. Based on the outcome of EFA, the twenty COVID-19 impacts were categorized into seven critical areas. Then, based on the experts' inputs, G-DEMATEL was utilized to reveal the causal interrelationships among various COVID-19 impacts. The results indicate that disruption management, relationship management, and production management are the top three critical areas that need to be addressed in the COVID-19 crisis. Disruption in supply, ripple effect on supply chain operations, and obsolescence of machines were found to be the most influential impacts while disproportionateness between supply and demand, difficulty in demand forecasting, and reduced cash inflow were found to be the most influenced impacts. This study's outcomes will help policymakers and supply chain managers develop strategies to restructure supply chain networks. This study is an original contribution to the analysis of COVID-19 impacts in the supply chain activities in India due to the use of EFA and G-DEMATEL. This study considers India only, and hence, the outcomes lack generalizability. A study considering multiple developing countries could generalize these findings.
The COVID-19 pandemic has become a global health and humanitarian crisis that catastrophically affects many industries. To control the disease spread and restore normal lives, mass vaccination is considered the most effective way. However, the sustainable last-mile cold chain logistics operations of COVID-19 vaccines is a complex short-term planning problem that faces many practical challenges, e.g., low-temperature storage and transportation, supply uncertainty at the early stage, etc. To tackle these challenges, a two-stage decision-support approach is proposed in this paper, which integrates both route optimization and advanced simulation to improve the sustainable performance of last-mile vaccine cold chain logistics operations. Through a real-world case study in Norway during December 2020 and March 2021, the analytical results revealed that the logistics network structure, fleet size, and the composition of heterogeneous vehicles might yield significant impacts on the service level, transportation cost, and CO 2 emissions of last-mile vaccine cold chain logistics operations.
Supply Chain Management is in constant evolution, and Supply Chain Resilience (SCR) appears as a recent offspring result of changes in how companies do business. Research efforts on the topic have led to a focus on the basic concepts of SCR, leaving a relevant research gap on the modelling and quantification of the SCR behaviour. In fact, there is not yet a consensus on SCR metrics or on how to quantify SCR. Most SCR models fail to incorporate relevant characteristics of the supply chain’s performance, as are the impacts perceived by downstream customers. This work addresses such gaps, and a new resilient SC metric is proposed, which is incorporated into a developed optimisation model, where economic and responsiveness objectives are maximised when designing and planning resilient SC considering all SC entities. The model is applied to a case study that shows that decision-makers should avoid adopting universal strategies when managing their SC and instead should define the best plan for their SC operation. The impacts perceived by downstream customers are analysed. Moreover, it can be concluded that there is a correlation between the SC performance and the new SCR metric, easing the process of designing and planning the SC when resilience concerns are at stake.
In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between - 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health.
Supplementary information:
The online version contains supplementary material available at 10.1007/s10479-021-04238-2.
The poultry industry is one of the most important agricultural sectors, which constitutes a significant part of the per capita consumption of protein and meat. Integrating operations of poultry industry sections including production, distribution and consumption becomes vital. Although the proper poultry supply chain has been established and made plenty of benefits for a long time, the global outbreak of COVID-19 shows that operations under pandemic are still challenge for the poultry industry. In this paper, the impacts of pandemic on poultry industry is investigated by developing a multi-period multi-modal stochastic poultry supply chain. Two models are developed aiming to mitigate the negative effects of pandemic occurrence through product stocking policy. In the first model, distribution system is in accordance with a multi-component structure, while the second model allows direct connections between suppliers (farmers) and demanders (customers). In both models, poultry productions are negatively affected by COVID 19. Due to the complexity of the model, a hybrid solution approach based on Branch and Cut and Dynamic Programming is developed. To validate the performance of the proposed model and solution procedure, a case study on the broiler industry in the state of Mississippi is performed. The results show that storing poultry products in the pre-pandemic along with direct logistics during pandemic period can save the broiler supply chain cost up to 30%.
The global spread of the novel coronavirus, also known as the COVID-19 pandemic, has had a devastating impact on supply chains. Since the pandemic started, scholars have been researching and publishing their studies on the various supply-chain-related issues raised by COVID-19. However, while the number of articles on this subject has been steadily increasing, due to the absence of any systematic literature reviews, it remains unclear what aspects of this disruption have already been studied and what aspects still need to be investigated. The present study systematically reviews existing research on the COVID-19 pandemic in supply chain disciplines. Through a rigorous and systematic search, we identify 74 relevant articles published on or before 28 September 2020. The synthesis of the findings reveals that four broad themes recur in the published work: namely, impacts of the COVID-19 pandemic, resilience strategies for managing impacts and recovery, the role of technology in implementing resilience strategies, and supply chain sustainability in the light of the pandemic. Alongside the synthesis of the findings, this study describes the methodologies, context, and theories 2 used in each piece of research. Our analysis reveals that there is a lack of empirically designed and theoretically grounded studies in this area; hence, the generalizability of the findings, thus far, is limited. Moreover, the analysis reveals that most studies have focused on supply chains for high-demand essential goods and healthcare products, while low-demand items and SMEs have been largely ignored. We also review the literature on prior epidemic outbreaks and other disruptions in supply chain disciplines. By considering the findings of these articles alongside research on the COVID-19 pandemic, this study offers research questions and directions for further investigation. These directions can guide scholars in designing and conducting impactful research in the field.
Awareness of the importance of U-shaped assembly line balancing problems is all on the rise. In the U-shaped assembly line, balancing is affected by the uncertainty associated with the assembly task times. Therefore, it is crucial to develop an approach to respond to the uncertainty caused by the task times. When the great majority of existing literature related to uncertainty in the assembly line is considered, it is observed that the U-shaped assembly line balancing problem under uncertainty is scarcely investigated. That being the case, we aim to fill this research gap by proposing a robust counterpart formulation for the addressed problem. In this study, a robust optimization model is developed for the U-shaped assembly line worker assignment and balancing problem (UALWABP) to cope with the task time uncertainty characterized by a combined interval and polyhedral uncertainty set. A real case study is conducted through data from a company producing water meters.
COVID-19 is a highly infectious respiratory virus that has posed a great threat to the general public. In order to prevent its spread, many governments have enacted stringent measures. Supply chains around the world are facing major disruptions and difficulties adjusting to the new demands and needs of a locked down world. In this paper, we will address the relationship between supply chain operations and the ongoing COVID-19 pandemic. Given current global shortages in essential goods such as medication, we explore the connection between said shortage and supply chain issues, such as the lack of supply chain transparency and resilience, as well as unsustainable just-in-time manufacturing. To mitigate the effects of these issues and protect supply chain operations, we propose some recommendations, such as nationalizing the medical supply chains, adopting a plus one diversification approach, and increasing safety stock. These recommendations are given to not only mitigate current consequences in relation to the ongoing crisis, but also to suggest measures that will provide firms the resiliency needed to weather similar potential shortages in the future.
Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion—the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply–demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.
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Epidemic outbreaks are a special case of supply chain (SC) risks which is distinctively characterized by a long-term disruption existence, disruption propagations (i.e., the ripple effect), and high uncertainty. We present the results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs. First, we articulate the specific features that frame epidemic outbreaks as a unique type of SC disruption risks. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of epidemic outbreaks on the SC performance using the example of coronavirus COVID-19 and anyLogistix simulation and optimization software. We offer an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers. The major observation from the simulation experiments is that the timing of the closing and opening of the facilities at different echelons might become a major factor that determines the epidemic outbreak impact on the SC performance rather than an upstream disruption duration or the speed of epidemic propagation. Other important factors are lead-time, speed of epidemic propagation, and the upstream and downstream disruption durations in the SC. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the SCs and develop pandemic SC plans. Our approach can also help to identify the successful and wrong elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks. The paper is concluded by summarizing the most important insights and outlining future research agenda.
Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis.
The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in a single-echelon-single-event setting is desirable and indeed critical for some firms, modelling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by the need to consider both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on integration of Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) to quantify the ripple effect. We use the DTMC to model the recovery and vulnerability of suppliers. The proposed DTMC model is then equalised with a DBN model in order to simulate the propagation behaviour of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level. This ripple effect metric is applied to two case studies and analysed. The findings suggest that our model can be of value in uncovering latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritising contingency and recovery policies.
This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case-study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.
The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods.
Based on the uncertain conditions such as uncertainty in blood demand and facility disruptions, and also, due to the uncertain nature of blood products such as perishable lifetime, distinct blood groups, and ABO-Rh(D) compatibility and priority rules among these groups, this paper aims to contribute blood supply chains under uncertainty. In this respect, this paper develops a bi-objective two-stage stochastic programming model for managing a red blood cells supply chain that observes above-mentioned issues. This model determines the optimum location-allocation and inventory management decisions and aims to minimize the total cost of the supply chain includes fixed costs, operating costs, inventory holding costs, wastage costs, and transportation costs along with minimizing the substitution levels to provide safer blood transfusion services. To handle the uncertainty of the blood supply chain environment, a robust optimization approach is devised to tackle the uncertainty of parameters, and the TH method is utilized to make the bi-objective model solvable. Then, a real case study of Mashhad city, in Iran, is implemented to demonstrate the model practicality as well as its solution approaches, and finally, the computational results are presented and discussed. Further, the impacts of the different parameters on the results are analyzed which help the decision makers to select the value of the parameters more accurately.
Resilient supplier selection is a key strategic decision in the context of the supply chain (SC) disruption management.
We offer an efficient solution to the resilient supplier selection and optimal order allocation problem.
We first show how to compute the likelihood of disruption scenarios for the supplier selection problem using a
probabilistic graphical model. That model can capture (i) a large number of disruptive events with no computational
burden, and (ii) the dependencies among disruptive events and their impacts on supplier performance,
i.e., the ripple effect. We then propose a stochastic bi-objective mixed integer programming model to support the
decision-making in how and when to use both proactive and reactive strategies in supplier selection and order
allocation. The outcomes of this research, if utilized properly, can benefit suppliers to find the optimal set of
operational decisions (e.g., the optimal level of surplus capacity and restorative capacity) that enhance their
resilience capabilities. Finally, the proposed model can be utilized as a decision support tool to assist manufacturers
in performance assessment of supplier alternatives when costs and resilience are considered simultaneously,
which helps to build up both efficient and resilient SC (i.e., to achieve the SC resilience) to ensure the
operations continuity. These outcomes can help SC managers organize their disruption risk mitigation efforts
with balancing the efficiency and resilience while focusing on critical suppliers and order (re)-allocation that will
have a more significant impact on the performance of the SC when disrupted.
One of the key issues in supply chain sustainability is the efficient usage of the available resources. At the same time, proactive supply chain design with disruption risk considerations frequently leads to a network redundancy which implies some resource reservations in anticipation of possible disruptions. Even if resilient supply chain design has received much attention in literature, there is a research gap in designing both resilient and sustainable supply chains. This study contributes to closing the given gap by proposing a novel methodological approach to modelling network redundancy optimization. This allows for simultaneous computation of both optimal network redundancy and proactive contingency plans, considering both supply dynamics and structural disruption risks. The novelties of this study are the integration of sustainable resource utilization and SC resilience based on coordination of structure- and flow-oriented optimization. The model uncovers a practical approach to analyze and optimize supply chain redundancy by varying processing intensities of resource consumption in the network according to supply and structural dynamics. This makes it possible to explicitly include the dynamics of resource consumption for contingency plan realization in disruption scenarios.
For random distributors under supply disruptions caused by emergency incidents, a fuzzy emergency model and a robust emergency strategy of the supply chain system are studied. First, for a kind of supply chain system composed of a strategic manufacturer, a backup manufacturer, and multiple distributors, the basic emergency models, including the inventory models and a total cost model, are constructed under random supply disruptions. Then, based on the Takagi-Sugeno fuzzy system, the basic emergency models of the supply chain system are converted into a discrete switching model, which can realize soft switching among the basic emergency models. Furthermore, according to the different inventory levels, the strategic manufacturer’s production strategies and the distributors’ ordering strategies are designed to reduce the inventory costs of the node enterprises in supply chain system. Second, by defining a discrete piecewise Lyapunov function in each maximal overlapped-rules group, a new fuzzy robust emergency strategy for the supply chain system is proposed through the principle of parallel distributed compensation. This emergency strategy can not only restore the impaired supply chain to the normal operation state but also keep the total cost of the supply chain at a low level and guarantee the robust stability of the emergency supply chain system. Finally, the simulation results illustrate the effectiveness of the proposed fuzzy robust emergency strategy of the supply chain system.
This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network's economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics—namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods—are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.
One of the most destructive effects of pandemics and epidemics outbreak is the severe deficiency of basic goods needed by society, especially in underdeveloped and developing countries. Therefore, designing an efficient managerial model for making operative modifications in logistics networks beside the governmental financial support policies is considered as one of the most important managerial challenges in the critical situation of the prevalence of infectious diseases that is not investigated in the literature. In this paper, an optimization-based approach is developed to design the supply, production, and distribution channels of basic goods using the capacity of small and medium-sized enterprises (SMEs) under direct supervision of the government in order to increase both the agility and resilience of logistics networks. Since the NP-hardess of the discussed problem, an iterative two-step heuristic based on local search (ITbLS) is developed to solve the proposed problem. According to the numerical results, it can be observed that the proposed algorithm has a good performance to obtain near-optimal solutions for the case study. Moreover, the numerical analyses indicate that the percentage of the active capacity of suppliers, transportation fleet, SMEs, and distribution centers, is specifically dependent on the amount of demand and the number of the established SMEs. In general, managerial results of the proposed model make supply chains more efficient by exploiting the capacity of SMEs and improve the government finaical resources allocation to the private sector to supply basic goods in critical situations.
In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.
Supply chain resilience (SCRES) is an emerging research area, which plays a crucial role in protecting supply chains (SCs) against small- to large-scale disruptions. Over the past few years, many researchers have focused on developing SCRES strategies that have significantly contributed to mitigating SC disruptions. While the number of papers on this subject has been gradually increasing, the absence of a systematic literature review means that it is unclear which SCRES strategies for mitigating SC disruptions have already been studied and which issues still need to be investigated. Therefore, there is a need to conduct a systematic literature review to provide a comprehensive overview of SC resilience initiatives and strategies. For the review and synthesis conducted in this paper, 151 relevant articles were identified through a systematic search and selection of papers published between 2010 and 2021. First, the main themes of the SCRES strategies were categorized. The development of SCRES strategies for preparedness, response, and recovery, aimed at mitigating SC disruptions, was reviewed. Second, a detailed analysis of research developments in SCRES strategies was conducted, along with an investigation into the methodological, theoretical, and contextual justifications for tackling SC disruptions. Third, literature on SCRES strategies was synthesized for mapping and identifying potential research gaps. The analysis revealed that there is a scarcity of simulation model-based and theoretically grounded studies to mitigate large-scale SC disruptions. Moreover, it was also found that most studies have identified SCRES strategies for low-demand luxury products, while high-demand essential products and services have largely been ignored. Finally, based on the analysis, this article identifies research questions and future research directions for the field of SCRES research. These can guide academics and practitioners in designing and leading effective research in the field.
In nowadays world, firms are encountered with many challenges that can jeopardize business continuity. Recently, the coronavirus has brought some problems for supply chain networks. Remarkably, perishable product supply chain networks, such as pharmaceutical, dairy, blood, and food supply chains deal with more sophisticated situations. Generally, during pandemic outbreaks, the activities of these industries can play an influential role in society. On the one hand, products of these industries are considered to be daily necessities for living. However, on the other hand, there are many new restrictions to control the coronavirus prevalence, such as closing down all official gatherings and lessening the work hours, which subsequently affect the economic growth and gross domestic product. Therefore, risk assessment can be a useful tool to forestall side-effects of the coronavirus outbreaks on supply chain networks. To that aim, the decision-making trial and evaluation laboratory approach is used to evaluate the risks to perishable product supply chain networks during the coronavirus outbreak era. Feedback from academics was received to identify the most important risks. Then, experts in pharmaceutical, food, and dairy industries were inquired to specify the interrelations among risks. Then, Pythagorean fuzzy sets are employed in order to take the uncertainty of the experts’ judgments into account. Finally, analyses demonstrated that the perishability of products, unhealthy working conditions, supply-side risks, and work-hours are highly influential risks that can easily affect other risk factors. Plus, it turned out that competitive risks are the most susceptive risk in the effect category. In other words, competition among perishable product supply chain networks has become even more fierce during the coronavirus outbreak era. The practical outcomes of this study provide a wide range of insights for managers and decision-makers in order to prevent risks to perishable product supply chain networks during the coronavirus outbreak era.
While the swift development and production of a COVID-19 vaccine has been a remarkable success, it is equally crucial to ensure that the vaccine is allocated and distributed in a timely and efficient manner. Prior research on pandemic supply chain has not fully incorporated the underlying factors and constraints in designing a vaccine allocation model. This study proposes an innovative vaccine allocation model to contain the spread of infectious diseases incorporating key contributing factors to the risk of uninoculated people including susceptibility rate and exposure risk. Analyses of the data collected from the state of Victoria in Australia show that a vaccine allocation model can deliver a superior performance in minimizing the risk of unvaccinated people when a multi-period approach is employed and augmenting operational mechanisms including transshipment between medical centers, capacity sharing, and mobile units being integrated into the vaccine allocation model.
Given the requirements of international businesses, this research addresses the supply chain network design problem in the real-world situation by considering four critical features: sustainability, resiliency, responsiveness, and globalization. For this purpose, a multi-objective mathematical model is proposed that minimizes the environmental impacts and the total costs and maximizes the social impacts while considering the resilience and responsiveness of the global supply chain. Then, the modified fuzzy robust stochastic method is employed to tackle the uncertainty. This study selects one of the most important medical devices during the pandemic (COVID-19) namely the blood bank refrigerator as a case study. Afterwards, the proposed multi-objective model is solved by developing a novel method named as augmented lexicographic weighted Tchebycheff method. Based on the obtained results, an increase in the responsiveness level of the supply chain can lead to increasing the sustainability dimensions, including job opportunities, safety, carbon emission, and economic aspects. Moreover, an increase in demands harms the economic, environmental, and responsiveness targets. The demand has a pivotal role in selecting resilience strategy, as well.
This paper presents a multi-period multi-objective distributionally robust optimization framework for enhancing the resilience of personal protective equipment (PPE) supply chains against disruptions caused by pandemics. The research is motivated by and addresses the supply chain challenges encountered by a Canadian provincial healthcare provider during the COVID-19 pandemic. Supply, price, and demand of PPE are the uncertain parameters. The ∊-constraint method is implemented to generate efficient solutions along the trade-off between cost minimization and service level maximization. Decision makers can easily adjust model conservatism through the ambiguity set size parameter. Experiments investigate the effects of model conservatism on optimal procurement decisions such as the portion of the supply base dedicated to long-term fixed contracts. Other types of PPE sources considered by the model are one-time open-market purchases and federal emergency PPE stockpiles. The study recommends that during pandemics health care providers use distributionally robust optimization with the ambiguity set size falling in one of three intervals based on decision makers’ relative preferences for average cost performance, worst-case cost performance, or cost variance. The study also highlights the importance of surveillance and early warning systems to allow supply chain decision makers to trigger contingency plans such as locking contracts, reinforcing logistical capacities and drawing from emergency stockpiles. These emergency stockpiles are shown to play efficient hedging functions in allowing healthcare supply chain decision makers to compensate variations in deliveries from contract and open-market suppliers.
This paper presents a multi-portfolio approach and scenario-based stochastic MIP (mixed integer programming) models for optimization of supply chain operations under ripple effect. The ripple effect is caused by regional pandemic disruption risks propagated from a single primary source region and triggering delayed regional disruptions of different durations in other regions. The propagated regional disruption risks are assumed to impact both primary and backup suppliers of parts, OEM (Original Equipment Manufacturer) assembly plants as well as market demand. As a result, simultaneous disruptions in supply, demand and logistics across the entire supply chain is observed. The mitigation and recovery decisions made to improve the supply chain resilience include pre-positioning of RMI (Risk Mitigation Inventory) of parts at OEM plants and ordering recovery supplies from backup suppliers of parts, located outside the primary source region. The decisions are spatiotemporally integrated. The pre-positioning of RMI implemented before a disruptive event is optimized simultaneously with the RMI usage and recovery supply portfolios for the backup suppliers in the aftermath periods. The recovery supplies of parts and production at OEM plants, are coordinated under random availability of suppliers and plants and random market demand. The resilient solutions for the resilient supply portfolios are compared with the non-resilient solutions with no recovery resources available. The findings indicate that the resilient measures commonly used to mitigate the impacts of region-specific disruptions can be successfully applied for mitigation the impacts of multi-regional pandemic disruptions and the ripple effect.
The study of supply chain (SC) resilience as a research perspective is in an incipient state. Nevertheless, there is a tremendous amount of literature concerning SCs under risk and uncertainty. This paper presents a review of the quantitative models for SC resilience using bibliometric and network analyses. The study identified 3672 articles and provided statistical measurements of science, scientists, and scientific activities. Additionally, the analysis highlights the inter-temporal dimensions of decision making and classifies articles based on their usability in real-world applications. Systematic mapping using co-citation and the PageRank algorithm resulted in seven key research themes, and a microlevel analysis of these themes provides prospective research directions. This involved examining the contributions of individual articles with respect to their scope, value proposition, risk-type consideration, methodology and technique used, and their industry applications. The thematic analysis and extensive future research directions leverage the insights and potential of this review article.
Healthcare is considered one basic necessity to sustaining life; thereby, assessing the character of a healthy and resilient supply chain can help a nation develop ideas to combat the healthcare crisis. COVID-19 has led to a long-term strain on the healthcare supply chain (HCSC) and has resulted in a lack of basic healthcare necessities. It has become apparent that supply chain disruptions and increased usage has led to a lack of medical supplies needed to provide the proper care to patients. Multicriteria decision-making (MCDM) will help to indicate what characteristics contribute to resilient healthcare supply chains. To assess the characteristic of a resilient supply chain, significant healthcare supply chains will help indicate significant characteristics. A case study on the medical supplies’ supply chains is presented. A rank reversal proximity index MCDM method ranks criteria to assist with decision making. The proximity index will reduce the chances of the rank reversal phenomenon that results in incorrect rankings from occurring. Results show that redundancy, collaboration, and robustness are key indicators of a resilient supply chain while, supply chain design, communication capabilities, and supply chain risk management become comparatively less important during the COVID-19 pandemic. Furthermore, a cluster analysis is conducted to group the resilience indicators of the respective supply chain. Through this study, the best way to combat disruptions in the healthcare supply chain due to large-scale pandemics is to share information quickly, reduce reliance on the design of the supply chain, and track the usage of necessary medical supplies. Alternatively, we validated our study by comparing a Preference Selection Index (PSI) to the proposed method.
While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.
Neodymium−iron−boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.
Local disruptions can be propagated from one firm to another in a supply network (SN) and eventually influence the whole SN. Therefore, numerous studies on SN resilience considering the ripple effect have been reported recently. However, previous studies paid less attention to this phenomenon from a network structure perspective: if a firm is facing the risk of failure, then its partners may help it to mitigate the risk of failure by collaboration during the process of disruption propagation. Specifically, how SN structures (e.g. characterised by different scaling exponents) and other parameters (e.g. redundancy) influence the effectiveness of collaboration on improving SN resilience considering the ripple effect is not clear. Accordingly, we propose a ripple effect with collaboration (REC) model to consider the aforementioned phenomenon. We also present three new SN resilience metrics to evaluate SN resilience. Then, using both generated (by a novel SN generating model) and real-life SNs, we simulate the SN resilience considering REC under random and targeted disruptions. Our results demonstrate that the effectiveness of collaboration can be affected by SN structures and other parameters, and collaboration can even negatively affect SN resilience in some cases. We also summarise managerial implications and give future research directions.
The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.
This paper studies the design of the green medicine supply chain network under uncertainty, which integrates allocation, location, production, distribution, routing, inventory, and purchasing problems. The main contribution of the current paper is to design a fuzzy bi-objective Mixed-Integer Linear Programming (MILP) model for a multi-period, three-echelon, multi-product, and multi-modal transportation green medicine supply chain network (GMSCN). Additionally, the main aim of this network is to consider the environmental impacts related to the establishment of pharmacies and hospitals, by focusing on the reduction of greenhouse gases and the control of environmental pollutants. Therefore, to cope with uncertain parameters, fuzzy programming is utilized to examine uncertainty parameters. To solve the GMSCN model, meta-heuristic algorithms are used, including social engineering optimization, improved kill herd, improved social spider optimization, and hybrid whale optimization and simulated annealing. In this regard, two new hybrid algorithms called hybrid Firefly Algorithm and Simulated Annealing (HFFA-SA) and Hybrid Firefly Algorithm and Social Engineering Optimization (HFFA-SEO) to solve the proposed model for the first time are developed. In order to show the applicability of our paper and the lack of benchmark functions in the literature, a set of simulated data in two sizes including small- and large-sized problems is provided. Finally, the results of the analysis and the designed problems indicate that the GMSCN model and developed solution approaches are promising.
This paper introduces a multi-objective mathematical model to design a sustainable-resilient supply chain based on strategic and tactical decision levels. The resolution is to proactively plan for an optimal configuration to satisfy customer demands when the firm is highly vulnerable to operational and disruption risks. Compared to previous studies, we take the application of capacity planning in terms of redundancy to design a supply chain network that is resilient toward the demand-side by an optimization framework. A real-world influenza vaccine supply chain is studied to validate the proposed model and examine the tradeoff between resilience and sustainability. A robust fuzzy optimization approach is employed to cope with uncertainties. Then, the multi-objective model is solved by applying multi-choice goal programming with a utility function approach. Accordingly, managerial insights are suggested by analyzing the effects of structural parameters on the quantitative results. It is revealed that having redundancies in the supply chain does not always increase the total costs.
In this paper, we construct the first stochastic Generalized Nash Equilibrium model for the study of competition among countries for limited supplies of medical items (PPEs, ventilators, etc.) in the disaster preparedness and response phases in the Covid-19 pandemic. The government of each country is faced with a two-stage stochastic optimization problem in which the first stage is prior to the pandemic declaration and the second stage is post the pandemic declaration. We provide the theoretical constructs, a qualitative analysis, and an algorithm, accompanied by convergence results. Both illustrative examples are presented as well as algorithmically solved numerical examples, inspired by the need for N95 masks and ventilators. The results reveal that, in addition to the preparedness of countries before the pandemic declaration, their ability to adapt to the conditions in different scenarios has a significant impact on their overall success in the management of the pandemic crisis. The framework can capture competition for other medical supplies, including Covid-19 vaccines and possible treatments, with modifications to handle perishability.
There is a significant gap between the descriptions of Lean used by industry practitioners and the various bodies of academic research that have studied the theory and application of Lean. There is also a gap between applied research on Lean and basic research in the mathematical, physical and social sciences. As a result, Lean practice is based largely on trial‐and‐error experience while potentially valuable research results remain locked away unused in archival journals. This paper attempts to close these gaps by describing four “Lenses of Lean,” each of which aligns with a practical perspective and rests on a distinct body of conceptual research. Our hope is that this framework will provide a useful construct for Lean training and implementation and will also spur academic research that is relevant to advancing Lean practice.
The devastating impact of the ripple effect increases the importance of the reverse supply chain (RSC) design to ensure sustainability in the long-term. That being the case, in this study, a two-stage stochastic mixed-integer optimization model is proposed to design an RSC network under uncertainty sourcing from the ripple effect (i.e. external side of RSC) by considering the environmental and economic dimensions of sustainability. The environmental and economic disruptions of the ripple effect are represented by the increase in the carbon emission levels and the distance of roads, and the decrease in the capacity of facilities, respectively. Accordingly, a set of scenarios is considered based on the disruption levels (low- and high-impact) in case of the ripple effect. Furthermore, an
α
-reliability constraint is integrated into the model to further analyze the occurrence of scenarios. The model allows us to make integrated operational and strategic decisions by placing an emphasis on the carbon emission levels (i.e. environmental dimension) and the total cost (i.e. economic dimension). To obtain some remarkable insights, the proposed model is validated through computational experiments based on data extracted from a real case. The computational results show that the ripple effect increases the emission level and total cost up to 40%. For this reason, it suggested that the regulations regarding WEEE (Waste Electrical and Electronic Equipment) should be prepared by considering sustainability in the entire RSC network. Besides, it is realized that the centralized distribution strategy leads to a more resilient RSC network design.
This study presents a robust multi-objective optimization model to configure a green global supply chain network structure under disruption. The proposed model is adapted to a global medical device manufacturing system. Economic and environmental issues are considered in designing the network, and mitigation strategies are employed to obtain a resilient supply chain network. To deal with the computational tractability of this non-linear and multi-objective optimization problem, a novel hybrid heuristic is developed that incorporates improved strength Pareto evolutionary algorithm 2 (SPEA2). Computational results indicate that the proposed global supply chain network configuration can respond to its global customers’ demand in agile as well as green manner. Based on our results, the importance of the SC agility is highlighted by increasing the budget of uncertainty, and some of well-known mitigation strategies are in contradiction to the agile production paradigm.
This chapter proposes a comprehensive methodology to design a customer-oriented production system. To this end, an axiomatic design (AD) based methodology is developed by employing one of the most widely used lean techniques, value stream mapping (VSM). The methodology is designed in three stages: (i) analyzing the current state, (ii) applying the AD method, and (iii) designing the future state. To consider the inherent vagueness of the processes, a fuzzy approach is embedded into the VSM within the context of the proposed methodology, in the first and third steps. To show the applicability of the proposed methodology, a real case study from a water-meter producer is conducted. The lead time is an indicator to show how fast a company responds to customer demands. Therefore, it is employed as a metric to compare current and future states. The process and lead times are defined by using triangular fuzzy numbers (TFNs) to capture the ambiguity in work-in-process (WIP) levels in the company. Computational results show the effectiveness of the proposed methodology in terms of comparison metric, i.e. manufacturing lead-time. This study provides a guideline for academicians and researchers and aims to be a stepping stone for future studies.
This study examines the ripple effect on the system performance of the reverse supply chain (RSC) network and introduces a robust optimisation model for designing strong RSC networks to cope with the uncertainties caused by the ripple effect. In this manner, to the best knowledge of authors, a robust optimisation model for RSC design against the ripple effect in the context of green principles is formulated for the first time. That being the case, the study aims to provide remarkable managerial insights thanks to the developed robust optimisation model by adopting a proactive strategy before a long-term disruption occurs in the network. To this end, the robust optimisation model is applied to an industrial case study from an enterprise disassembling the household appliance. The scope of the case study is limited to the enterprise's recycling activities in the northern region of Turkey which is a potential landslide site due to the heavy rainfall. Computational experiments are performed through a set of scenarios regarding the different weight uncertainty values to reveal the changes in objective function value and decision variables. Based on the results, whilst the computationally tractable robust solutions are obtained; the price of robustness is higher than expected to protect the constraints against violation when the probability of constraint violation equals 0.01.
We study blood supply network optimization considering disasters where only a small number of historical observations exist. A two-stage distributionally robust optimization (DRO) model is proposed, in which uncertain distributions of blood demand are described by a moment-based ambiguous set, to optimize blood inventory prepositioning and relief activities together. To solve this intractable DRO with integer recourse, an approximate way is developed to transform it into a semidefinite program. A case study, based on the Longmenshan Fault in China, validates that our approach outperforms typical benchmarks, including deterministic, stochastic and robust programming. Sensitivity analysis provides helpful managerial insights.
Emergency supply of blood in disasters is a crucial task for humanitarian aid. In this paper, we present a bi-objective robust optimization model for the design of blood supply chains that are resilient to disaster scenarios. The proposed two-stage stochastic optimization model aims at minimizing the time and cost of delivering blood to hospitals after the occurrence of a disaster, while considering possible disruptions in blood facilities and transportation routes. A Lagrangian relaxation-based algorithm is developed that is capable of solving large-scale instances of the model. We apply this framework to a real case study of blood banks in Jordan.
Devastating effects of disasters and global crises on people increases the importance of humanitarian logistics studies for pre and post-disaster stages. Location planning of Temporary Medical Centers/field hospitals is one of the most important problems for disaster response. We aimed to determine the location and number of temporary medical centers in case of disasters by considering the locations of the existing hospitals, casualty classification (triage), capacities of medical centers and possibilities of damage to the roads and hospitals. Besides, we aimed to assign different casualty classes to these medical centers for emergency medical response by considering the distances between disaster areas and medical centers. For this purpose, a two-stage stochastic programming model was developed. The proposed model finds an optimal TMC location solution while minimizing the total setup cost of the TMCs and the expected total transportation cost by considering casualty types, demand, possibilities of damage to the roads and hospitals, and distance between the disaster areas and the medical centers. In the model, α-reliability constraints for the expected number of unassigned casualties were also used. Besides, the model was reformulated without triage, in order to understand the impact of casualty classification on the solution of the problem. We performed a real case study for the district of Kartal expected to be widely damaged in the possible Istanbul earthquake, and a sensitivity analysis was made. The analysis of the results offer some managerial insights associated with the number of temporary medical centers’ needed, their locations, and additional hospital capacity requirements.
This paper addresses a lean holistic fuzzy methodology for the new product development (NPD) projects to cope with the uncertainties encountered in the projects by placing an emphasis on the cross-functional worker teams along with utility workers’ impact on the lead time and total operational cost. To this end, the fuzzy design structure matrix (FDSM), the fuzzy value stream mapping (FVSM), and a novel fuzzy optimization model are combined and employed sequentially for visualizing all processes, decreasing lead time and operational cost, and determining the improvement points. While the whole methodology focuses on reducing the lead time by creating the worker teams, the fuzzy optimization model aims to decrease the total operational costs. The study is conducted in a startup, which has begun to reorganize its NPD projects according to lean principles by implementing the proposed methodology. The effectiveness of the methodology to reduce the lead time and the operational cost is shown in the results based on the data taken from the startup. To analyze the impact of parameters on the total operational cost in terms of the total number of workers (w.r.t. normal and utility) and the lead time as well, the computational experiments are carried out through a set of parameter values and α-cut levels. According to the results, the proposed methodology leads to a decrease in both the lead time and the total operational cost thanks to employing the utility worker concept.
Supply chain disruptions have caused hundreds of shortages of medically-necessary drugs since 2011. Once a disruption occurs, the industry is limited in its ability to adapt, and improving strategic resiliency decisions is important to preventing future shortages. Yet, many shortages have been of low-margin, generic injectable drugs, and it is an open question whether resiliency is optimal. It is also unknown what policies would be effective at inducing companies to be resilient. To study these questions, we develop new supply chain design models that consider disruptions and recovery over time. The first model is a two-stage stochastic program which selects the configuration of suppliers, plants, and lines. The second is a multi-stage stochastic program which selects the configuration and target safety stock level. We then overlay incentives and regulations to change the market conditions and evaluate their effects on two generic oncology drug supply chains. We find that profit-maximizing firms may maintain vulnerable supply chains without intervention. Shortages may be reduced with: moderate failure-to-supply penalties; mandatory supply chain redundancy; substantial amounts of inventory; and/or large price increases. We compare policies by evaluating the societal costs to reduce the expected shortages to 2% and 5% of demand.
In the information age, demand disruptions are challenging for supply chain (SC) systems. This study explores a reverse supply chain (RSC) system dealing with demand disruptions in its online channel. The disruptions hurt the company's revenue since a fraction of the online channel demand will be lost. It is also important to determine the level of investment in sustainability to meet both the cap-and-trade rule imposed by the government on the company and the customers' expectations about the sustainability level of products. In the reverse channel, the company collects the used products through a collector whose efforts increase the collection volume. Since the earnings of the reverse channel highly depend on the collection volume, finding an effective strategy to entice the collector to collect a desirable number of used products is critically important for the company. In order to find a proper strategy for resolving these challenges, we analytically develop an RSC model and derive the optimal pricing, sustainability level, and corporate social responsibility (CSR) decisions under demand disruptions for both the decentralized and centralized RSCs. We then propose a combination scheme by using the combined two-part-tariff (CTPT) contract. We find that the proposed coordination scheme is efficient because it not only improves the profits of the RSC and its members but also enhances the environment. Moreover, the CTPT contract can properly allocate the SC surplus among the RSC members based on their bargaining powers.
Managing supply chain operations in a reliable manner is a significant concern for decision-makers in competitive industries. In this article, two mathematical models considering competition and integrity in a three-echelon supply chain under uncertainty are proposed. The competition is formulated as a Stackelberg game such that the distribution centers have more power than the retailers. In the first model, decisions are made about the location and number of distribution centers (DCs), allocation of retailers, and the selling price of products. In the second model, based on the real world, the probability of risk and failure for the distribution centers are considered. Backup facilities should be established for unreliable facilities to meet the demands of retailers during disruption. To capture uncertainty, a two-stage stochastic approach is applied to model the problems. The first stage of the model belongs to the strategic planning and is not affected by randomness, while the second stage deals with tactical decisions depending on the realization of the first stage's random vector. In order to solve the problem, a hybrid genetic algorithm has been applied to large-scale problems. Numerical experiments have been conducted to assess the effectiveness of the proposed algorithm. Next, a sensitivity analysis is performed to recognize the most important parameters and evaluate the accuracy of our approach. Finally, to demonstrate the applicability of the model, the proposed model was implemented on the data of Alborz Pharmaceutical Company.
We study production-ordering behaviour in a supply chain (SC) with disruption risks in recovery and post-disruption periods and the influence of severe disruptions on production and distribution network design. A real-life case-study of a disruption in a SC is considered and investigated with the help of discrete-event simulation blended with network optimisation in anyLogistix. Two novel findings are presented. First, disruption-driven changes in SC behaviour may result in backlog and delayed orders, the accumulation of which in the post-disruption period we call “disruption tails”. The transition of these residues into the post-disruption period causes post-disruption SC instability, resulting in further delivery delays and non-recovery of SC performance. Second, a smooth transition from the contingency policy through a special “revival policy” to normal operations mode partially mitigates the negative effects of disruption tails. The results show that isolated production and distribution network design optimisation can lead to severe decreases in performance in the event of SC disruptions. Contingent recovery policies need to be applied during the disruption period along with a revival policy in the post-disruption period to avoid disruption tails. These revival policies must be developed for the transition from the recovery to the disruption-free operations mode. A revival policy is meant to mitigate the negative impact of disruption tails and stabilise the ordering control policies and performance in the SC. Thus, recovery policies should not be limited to the disruption period only. They should also consider the post-disruption period and be included in SC design decisions. The revival policy should be included in the SC resilience framework.