Adapting supply chain operations in anticipation of and during the COVID-19 pandemic

Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic

The COVID-19 virus's high transmissibility has resulted in the virus's rapid spread throughout the world, which has brought several repercussions, ranging from a lack of sanitary and medical products to the collapse of medical systems. Hence, governments attempt to re-plan the production of medical products and reallocate limited health resources to combat the pandemic. This paper addresses a multi-period production-inventory-sharing problem (PISP) to overcome such a circumstance, considering two consumable and reusable products. We introduce a new formulation to decide on production, inventory, delivery, and sharing quantities. The sharing will depend on net supply balance, allowable demand overload, unmet demand, and the reuse cycle of reusable products. Undeniably, the dynamic demand for products during pandemic situations must be reflected effectively in addressing the multi-period PISP. A bespoke compartmental susceptible-exposed-infectious-hospitalized-recovered-susceptible (SEIHRS) epidemiological model with a control policy is proposed, which also accounts for the influence of people's behavioral response as a result of the knowledge of adequate precautions. An accelerated Benders decomposition-based algorithm with tailored valid inequalities is offered to solve the model. Finally, we consider a realistic case study - the COVID-19 pandemic in France - to examine the computational proficiency of the decomposition method. The computational results reveal that the proposed decomposition method coupled with effective valid inequalities can solve large-sized test problems in a reasonable computational time and 9.88 times faster than the commercial Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand on the average up to 32.98% and 20.96%, respectively.

The impact of green innovation initiatives on competitiveness and financial performance of the land transport industry

The transportation sector is one of the primary contributors to greenhouse gas emissions that have deteriorating effects on the state of the environment. The implementation of sustainable practices has become one of the most challenging tasks of organizations at present. This study examined the effect of implementing green innovation initiatives on a firm's competitiveness and financial performance of motor vehicle companies in the Philippines. Data were gathered through an online survey questionnaire with a total of 206 respondents composed of employees of various ranks working in companies engaged in the manufacture, distribution, retail, and service of motor vehicles. The theoretical framework presented a hierarchical latent variable model which was validated using the partial least square structural equation modelling (PLS-SEM). The model fit, measurement, general construct fit, discriminant validity, and structural model parameters were examined and found to have acceptable values. The findings indicated that environmental regulations, market demand, government pressure, competitor pressure, corporate social responsibility, and employee conduct were the significant drivers of green innovation initiatives. The study also revealed that the implementation of green innovation initiatives positively affects the firm's competitiveness and financial performance. Motor vehicle companies and other types of organizations are encouraged to demonstrate not only their concern for society or community but also their concern for the environment to acquire better market leverage and financial position.

Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis

The global-local supply chains are affected by the forward and downward propagation of COVID-19. The pandemic disruption is a low-frequency and high-impact (black swan) event. Adapting to the "New Normal" situation requires adequate risk mitigation strategies. This study proposes a methodology to implement a risk mitigation strategy during supply chain disruptions. Random demand accumulation strategies are considered to identify the disruption-driven challenges under different pre and post-disruption scenarios. The best mitigation strategy and the optimal location of distribution centers to maximize the overall profit were determined using simulation-based optimization, greenfield analysis, and network optimization techniques. The proposed model is then evaluated and validated using appropriate sensitivity analysis. The main contribution of the study is to (i) perform cluster-based supply chain disruption analysis, (ii) propose a resilient and flexible model to illustrate the proactive and reactive measures for the ripple effect, (iii) prepare the supply chain for future pandemic-like crises, and (v) reveal the relationship between the pandemic impact and supply chain resilience. A case study of an ice cream manufacturer is used to demonstrate the proposed model.

Research on Real-Time Robust Optimization of Perishable Supply-Chain Systems Based on Digital Twins

Aiming at the real-time robust optimization problem of perishable supply-chain systems in complex environments, a real-time robust optimization scheme based on supply-chain digital twins is proposed. Firstly, based on the quantitative logical relationship between production and sales of single-chain series supply-chain system products, the state space equation of the supply-chain system with logical characteristics, structural characteristics, and quantitative characteristics was constructed, and twin data were introduced to construct the digital twins of supply chains based on the state-space equation. Secondly, the perishable supply-chain system in complex environments was regarded as an uncertain closed-loop system from the perspective of the state space equation, and then a robust H∞ controller design strategy was proposed, and the supply-chain digital twins was used to update and correct the relevant parameters of the supply-chain system in real-time, to implement the real-time robust optimization based on the supply-chain digital twins. Finally, the simulation experiment was carried out with a cake supply-chain production as an example. The experimental results show that the real-time updating of relevant parameters through the digital twins can help enterprise managers to formulate reasonable management plans, effectively avoid the shortage problem of enterprises in the cake supply-chain system, and reduce the maximum inventory movement standard deviation of each link by 12.65%, 6.50%, and 14.87%, and the maximum production movement standard deviation by 70.21%, 56.84%, and 45.19%.

Assessing strategies to mitigate the impacts of a pandemic in apparel supply chains

The COVID-19 pandemic has taught global businesses that a pandemic can put business dynamics in unforeseeable turbulence. The disruptions created by the pandemic in the apparel industry exposed the vulnerabilities of apparel supply chains (SCs). To recover the supply chain impacts (SCIs) during an unprecedented event such as the COVID-19 pandemic, apparel SCs need a robust framework that can identify, measure, and mitigate the severity of SCIs by assessing effective mitigation strategies. This study identifies 12 critical SCIs in apparel SCs during a pandemic and 17 mitigation strategies. To assess SCIs and mitigation strategies, a modified grey-based bi-level analytical network process (ANP) is proposed to deal with the complex relationship between the SCIs and mitigation strategies. A real-life case study is conducted from an apparel supply chain for validation purposes. The findings suggest that policymakers in apparel SCs should prioritize implementing government policies and financial aid to deal with increased material and operational costs, the sudden surge in the unemployment rate, cancellation of orders and delayed payment, and increased transportation costs during a pandemic. This study also contributes to the literature by providing a robust decision-making framework for practitioners to deal with the complexity of SCs during future pandemics.

Adaptive R&D contract for urgently needed drugs: Lessons from COVID-19 vaccine development

This paper analyzes an incentive contract for new vaccine research and development (R&D) under pandemic situations such as COVID-19, considering the R&D contract's adaptability to the pandemic. We study how the public sector (government) designs the adaptive R&D contract and offers it to pharmaceutical enterprises. An agency-theoretic model is employed to explore the contract whose terms are an upfront grant as a fixed fee and a sales tax credit as an incentive tool, examining how the values of related parameters affect contract term determinations. We found that the adaptability factor derived from urgent policies such as emergency use authorization (EUA) as well as tax credits, can be utilized as practical incentive tools that lead vaccine developers to increase their effort levels for R&D success. We also found that public-private state-emergency contracts may not follow the conventional wisdom. Counterintuitively, dependency on tax credits (incentive part) decrease as the client's degree of risk averseness increases in the emergency contract.

A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients. A robust approach is provided to face the data uncertainty, and a new mathematical model is extended to linearize a nonlinear constraint. The ICU beds, ward beds, ventilators, and nurses are considered the four main capacity limitations of hospitals for admitting different types of COVID-19 patients. The sensitivity analysis is performed on a real-world case study to investigate the applicability of the proposed model. The results demonstrate the contribution of student nurses and backup and field hospitals in treating COVID-19 patients and provide more flexible decisions with lower risks in the system by managing the fluctuations in both the number of patients and available nurses. The results showed that a reduction in the number of available nurses incurs higher costs for the system and lower satisfaction among patients and nurses. Moreover, the backup and field hospitals and the medical staff elevated the system's resiliency. By allocating backup hospitals to COVID-19 patients, only 37% of severe patients were lost, and this rate fell to less than 5% after establishing field hospitals. Moreover, medical students and field hospitals curbed the costs and increased the satisfaction rate of nurses by 75%. Finally, the system was protected from failure by increasing the conservatism level. With a 2% growth in the price of robustness, the system saved 13%.

Social distancing and revenue management-A post-pandemic adaptation for railways

The SARS-CoV-2 pandemic has had a significant impact on rail operations worldwide. Adopting control measures such as a 50% occupancy rate can contribute to a safer travel environment, though at the expense of operational efficiency. This paper addresses the issues of social distancing and revenue maximization for a train operating company in a post-pandemic world. Although the two objectives appear to be highly contradictory, we believe that judicious planning can optimize both to a great extent. Existing research on social distancing on public transport has only considered the risk of virus transmission during travel. This is the first attempt to recognize the risk of virus spread in different cities along with transmission risk as part of developing a social distancing plan. We study the problem of assigning seats to passenger groups on long-distance trains while ensuring social distancing within coaches. A novel seating assignment policy is proposed that takes into account several factors that govern the spread of virus. In an effort to reduce the spread of the virus and improve revenue simultaneously, a mixed-integer programming (MIP) model is proposed to assign seats to passengers. Several families of valid inequalities and preprocessing steps are proposed to strengthen the MIP formulation, which represents a substantial contribution to the literature on group seat assignment problem. The validity of the model and the effectiveness of the valid inequalities have been evaluated using real-life data from Indian Railways. The computational results demonstrate a significant reduction in the risk of contagion and an increase in seat utilization compared to the current approach employed by operators.

Assessment of regional economic restorability under the stress of COVID-19 using the new interval type-2 fuzzy ORESTE method

The economic implications from the COVID-19 crisis are not like anything people have ever experienced. As predictions indicated, it is not until the year 2025 may the global economy recover to the ideal situation as it was in 2020. Regions lacked of developing category is among the mostly affected regions, because the category includes weakly and averagely potential power. For supporting the decision of economic system recovery scientifically and accurately under the stress of COVID-19, one feasible solution is to assess the regional economic restorability by taking into account a variety of indicators, such as development foundation, industrial structure, labor forces, financial support and government's ability. This is a typical multi-criteria decision-making (MCDM) problem with quantitative and qualitative criteria/indicator. To solve this problem, in this paper, an investigation is conducted to obtain 14 indicators affecting regional economic restorability, which form an indicator system. The interval type-2 fuzzy set (IT2FS) is an effective tool to express experts' subjective preference values (PVs) in the process of decision-making. First, some formulas are developed to convert quantitative PVs to IT2FSs. Second, an improved interval type-2 fuzzy ORESTE (IT2F-ORESTE) method based on distance and likelihood are developed to assess the regional economic restorability. Third, a case study is given to illustrate the method. Then, robust ranking results are acquired by performing a sensitivity analysis. Finally, some comparative analyses with other methods are conducted to demonstrate that the developed IT2F-ORESTE method can supporting the decision of economic system recovery scientifically and accurately.

Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry

Covid-19 has allowed us to study systemic disruptions that impact entire industries. This paper explores how disruptions start, propagate, and continue over time by examining the semiconductor chip shortage faced by the auto industry during the years following Covid-19 in 2020. First, we carried out a thematic analysis of 209 pertinent newspaper articles. The analysis resulted in a thematic model of such disruptions with the interplay of various factors leading to the prolonged disruption to the auto sector. Second, we present the results from a stylized supply chain planning model run at different times to show how disruptions propagate to the auto and other sectors, causing systemic shortages. Overall, we contribute to the supply chain risk literature by focusing on system disruptions impacting entire industries versus normal disruptions affecting a particular company's supply chain.

Product availability and stockpiling in times of pandemic: causes of supply chain disruptions and preventive measures in retailing

The coronavirus pandemic in 2020 brought global supply chain disruptions for retailers responding to the increased demand of consumers for popular merchandise. There is a need to adapt the existing supply chain models to describe the disruptions and offer the potential measures that businesses and governments can take to minimize adverse effects from a retail logistics perspective. This research analyses the possible reasons for supply and demand disruptions using a mathematical model of a retail supply chain with uncertain lead times and stochastic demand of strategic consumers. The established concepts of supply chain management are applied for the model analysis: multi-period inventory policies, bullwhip effect, and strategic consumers. The impact of the pandemic outbreaks in the model is two-fold: increased lead-time uncertainty affects supply, while consumer stockpiling affects demand. Consumers' rational hoarding and irrational panic buying significantly increase retailers' costs due to higher safety stock and demand variability. The bullwhip effect further exacerbates the disruption. The research contributes to the recent literature on business response to supply chain disruptions by developing a model where both retailers and consumers decide on the order quantity and reorder point during a pandemic outbreak. Buying limits, continuous inventory review, government rationing, substitutability, and omnichannel fulfillment are the measures that can limit the damage of supply chain disruptions from stockpiling during the pandemic. Effective communication and price and availability guarantees can mitigate the negative impact of panic buying.

An integrated System Dynamics model for Closed Loop Supply Chains under disaster effects: The case of COVID-19

For a Closed Loop Supply Chain (CLSC), disaster is a risk source of unknown-unknowns, which may result in production disruptions with significant consequences on -but not limited to-profitability. For this reason, we provide a System Dynamics (SD)-based analysis for disaster events on the operation of CLSCs in order to study the system response (production/collection/disassembly/remanufacturing/recycling rates, inventories, cost, profit). This response is examined through the dynamics at a manufacturer, parts producer, collector, and disassembly center level, by providing control mechanisms for resilient CLSCs under disaster effects. In this dynamic analysis, COVID-19 is treated as a disaster event. Five different business scenario settings are presented for the manufacturer, which are considered as alternative mitigation policies in responding to product demand. The extensive simulation results provide insights for policy-makers, which depend on the reduction in manufacturer's production, reduction in product demand and duration of recovery period which are considered as causal effects due to the COVID-19 outbreak. For all combinations, holding base stocks during the pre-disaster period is proposed as the best mitigation policy in terms of manufacturer's inventory. In terms of economic impact, holding base stocks or coordination with third party are revealed as the best choice depending on the combination, while remote inventory policy adoption as the worst choice.

Personal protective equipment market coordination using subsidy

During a pandemic, various resources, including personal protective equipment (PPE), are required to protect people and healthcare workers from getting infected. Due to the high demand and limited supply chain, countries experience a shortage in PPE products. This global crisis imposes a decline in the international trade of PPE supplies. In fact, most governments implement a localization strategy motivating domestic manufacturers to pivot their operations to respond to PPE demands. An oligopolistic market cannot reach the socially optimal coverage without government subsidies. On the other hand, the government subsidy pays the proportion of production costs to reach the socially optimal coverage, while the government's budget is limited. Therefore, the government collaborates with manufacturers via procurement contracts to increase the supply of PPE products. We propose the first supply chain model of PPE products that investigates manufacturer costs and government expenditure. We consider how different behavioral aspects of manufacturers and government can self-organize towards a system optimum. Additionally, we integrate the consumer surplus, producer surplus, and societal surplus into the game model to maximize social benefit. A cost-sharing contract under the system optimum between government and manufacturers is designed to increase the production of PPEs and hence, helps in reducing the number of infected individuals. We conducted our computational study on real data generated from the mask usage during the Covid-19 pandemic in Los Angeles (LA) County to respond to the reported PPE shortage. Under the socially optimal strategy, the PPE coverage increases by up to 33%, and the number of infected individuals reduces by up to 30% compared to other strategies.

Mitigating personal protective equipment (PPE) supply chain disruptions in pandemics – a system dynamics approach

Purpose

The coronavirus disease (COVID-19) pandemic has emerged as an unprecedented health crisis worldwide and heavily disrupted the healthcare supply chain. This study focuses on analysing the different types of disruptions occurring in personal protective equipment (PPE) supply chains during the COVID-19 pandemic and on proposing mitigation strategies that are fit to the global scale and many interdependencies that are characteristic for this pandemic. The authors construct a conceptual system dynamics model (SD) based on the literature and adjusted with the use of empirical data (interviews) to capture the complexity of a global supply chain and identify leverage points (mitigation strategies).

Design/methodology/approach

This research follows a mix-methods approach. First, the authors developed a conceptual framework based on four types of disruptions that usually occur during health emergencies (direct effect, policy, supply chain strategy, and behaviourally induced disruptions). Second, the authors collected and analysed data from interviews with experts in the PPE supply chain. Based on the interviews data, the authors developed a conceptual system dynamics (SD) model that allows to capture the complex and dynamic interplay between the elements of the global supply chain system, by highlighting key feedback loops, delays, and the way the mitigation strategies can impact on them. From this analysis, the authors developed four propositions for supply chain risk management (SCRM) in global health emergencies and four recommendations for the policy and decision makers.

Findings

The SD model highlights that without a combination of mitigation measures, it is impossible to overcome all disruptions. As such, a co-ordinated effort across the different countries and sectors that experience the disruptions is needed. The SD model also shows that there are important feedback loops, by which initial disruptions create delays and shortages that propagate through the supply chain network. If the co-ordinated mitigation measures are not implemented early at the onset of the pandemic, these disruptions will be persistent, creating potential shortages of PPE and other critical equipment at the onset of a pandemic – when they are most urgently needed.

Originality/value

This research enriches the understanding of the disruptions of PPE supply chains on the systems level and proposes mitigation strategies based on empirical data and the existing literature.

Making waves in resilience: Drawing lessons from the COVID-19 pandemic for advancing sustainable development

The current COVID-19 pandemic has affected societies across the world while its economic impact has cut deeper than any recession since the Second World War. Climate change is potentially an even more disruptive and complex global challenge. Climate change could cause social and economic damage far larger than that caused by COVID-19. The current pandemic has highlighted the extent to which societies need to prepare for disruptive global environmental crises. Although the dynamics of combating COVID-19 and climate change are different, the priorities for action are the same: behavioral change, international cooperation to manage shared challenges, and technology's role in advancing solutions. For a sustainable recovery from the COVID-19 crisis to be durable and resilient, a return to 'business as usual' and the subsequent often environmentally destructive economic activities must be avoided as they have significantly contributed to climate change. To avoid this, we draw lessons from the experiences of the waves of the COVID-19 pandemic and beyond to advance sustainable development.

A Fusion Decision-Making Architecture for COVID-19 Crisis Analysis and Management

The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is not appropriate or sufficient. Thus, there is an urgent requirement to reexamine and revise an enterprise’s inherent crisis management architecture so as to help it recover sooner after having encountered extremely negative economic effects. To fulfill this need, the present paper introduces a fusion architecture that integrates artificial intelligence and multiple criteria decision making to exploit essential risk factors and identify the intertwined relations between dimensions/criteria for managers to prioritize improvement plans and deploy resources to key areas without any waste. The result indicated the accurate improvement priorities, which ran in the order of financial sustainability (A), customer and stakeholders (B), enablers’ learning and growth (D), and internal business process (C) based on the measurement of the impact. The method herein will help to effectively and efficiently support crisis management for an organization confronting COVID-19. Among all the criteria, maintaining fixed reserves was the most successful factor regarding crisis management.

Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis

Increased electricity consumption along with the transformations of the energy systems and interruptions in energy supply can lead to a blackout, i.e., the total loss of power in an area (or a set of areas) of a longer duration. This disruption can be fatal for production, logistics, and retail operations. Depending on the scope of the affected areas and the blackout duration, supply chains (SC) can be impacted to different extent. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. Distinctively, we triangulate the design and evaluation of experiments with consideration of SC performance, resilience, and viability. The results allow for some generalizations. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of several SC processes, disruption propagations (i.e., the ripple effect), and a danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts, mitigation and recovery strategies. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the unpredictable customer behavior might become major factors that determine the blackout impact and influence selection of an appropriate recovery strategy. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and viability and develop mitigation and recovery strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, intertwined supply networks, and cross-structural ripple effects.

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

Organizational e-Health Readiness: How to Prepare the Primary Healthcare Providers’ Services for Digital Transformation

The COVID-19 pandemic has had two main consequences for the organization of treatment in primary healthcare: restricted patients’ access to medical facilities and limited social mobility. In turn, these consequences pose a great challenge for patients and healthcare providers, i.e., the limited personal contact with medical professionals. This can be eased by new digital technology. While providing solutions to many problems, this technology poses several organizational challenges for healthcare system participants. As the current global situation and the outbreak of the humanitarian crisis in Europe show, these and other likely emergencies amplify the need to learn the lessons and prepare organizations for exceptional rapid changes. Therefore, a question arises of whether organizations are ready to use modern e-health solutions in the context of a rapidly and radically changing situation, and how this readiness can be verified. The aim of this article is to clarify the organizational e-heath readiness concept of Polish primary healthcare units. This study employs the triangulation of analytical methods, as it uses: (i) a literature review of e-health readiness assessment, (ii) primary data obtained with a survey (random sampling of 371 managers of PHC facilities across Poland) and (iii) the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, employed to estimate the structural model. The evaluation of the model revealed that its concept was adequate for more mature entities that focus on the patient- and employee-oriented purpose of digitization, and on assuring excellent experience derived from a consistent care process. In the context of patients’ restricted access to medical facilities and limited social mobility, a simpler version of the research model assesses the readiness more adequately. Finally, the study increases the knowledge base of assets (resources and capabilities), which will help healthcare systems better understand the challenges surrounding the adoption and scaling of e-health technologies.