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We consider a decentralised assembly supply chain that consists of an assembler and multiple suppliers. The cooperation between the assembler and each supplier is organised under a pull contract, which indicates that the suppliers bear the overstock risk. One of the suppliers may suffer disruption and endogenously builds the reliability, and the assembler could provide the subsidy for this unreliable supplier to improve the reliability. We reveal that in addition to the assembler, a reliable supplier with a sufficiently high wholesale price also has incentives to jointly offer subsidy for the ex ante reliability improvement. The reliable supplier's participation in reliability improvement could benefit all the supply chain members. Furthermore, we examine a subsequent reliable supplier's incentives to offer subsidy based on the previous one's. It suggests that it is not necessary for a subsequent reliable supplier with a lower wholesale price than the previous one's to provide the subsidy. Higher reliability for the whole supply chain could be achieved if the previous reliable supplier with a higher wholesale price provides the subsidy. We finally examine the impact of multiple unreliable suppliers and show that a larger number of unreliable suppliers would discourage a reliable supplier's subsidy. [ABSTRACT FROM AUTHOR]

Capacity management and supply chain disruptions are the most challenging problems faced in high tech industries. This paper focuses on establishing a capacity reservation contract between a single supplier and two heterogeneous retailers in a two-stage supply chain, wherein the retailers purchase a product from the supplier and sell it to the customers. In this study, a mathematical model is built for all parties involved, incorporating numerous essential elements of a real contract. The proposed model aims to assist in determining the best solutions for the retailers’ reserved capacities to the supplier and the supplier’s constructed capacity. Analytical results and sensitivity analyses are presented to demonstrate how various cost elements, price, and uncertainty affect the optimal capacity decisions. [ABSTRACT FROM AUTHOR]

As a new sustainable building production mode, prefabricated building supply chains can realize energy saving, environmental protection and full cycle value maximization of building products. Prefabricated building supply chains often experience disruptions due to supply instability, transportation delay and force majeure, resulting in project delays and cost escalations and posing challenges to the sustainable development objectives of enterprises. Therefore, it is important and essential to study the strategy of enhancing the resiliency of prefabricated building supply chains, which has not been comprehensively explored in previous papers. This paper constructs decision-making models for supply chain cost resilience strategies under varying scenarios of supply disruptions, incorporating both redundant inventory and back-up supplier strategy. It considers the total cost and resilience of the supply chain as dual objective functions. Parameter-tuned non-dominated sorting genetic algorithm-Π (NSGA-Π) algorithms were used innovatively to solve the project case, and the impacts of the redundant inventory coefficient and back-up supplier supply price coefficient on the model result were analyzed. The results indicate that the supply chain with resilience construction has a superior capability to cope with disruption. The results show that when there is a mild supply disruption, the general contractor uses the capacity within the supply chain and chooses a redundant inventory strategy to restore resilience. In the event of moderate disruption, both the easy inventory strategy and back-up supplier strategy are selected to maintain supply chain stability. In the event of a severe disruption, only the back-up supplier strategy is selected to cover the losses and maintain the project schedule. In addition, the choice of resilience strategy is impacted by the inventory levels and component prices of back-up suppliers. It further verifies the effectiveness of the model and the impacts of uncertain parameters in the model on the results. This study contributes to enhancing the resilience management of the prefabricated building supply chain by the general contractor, thereby elevating the overall efficiency and competitiveness of the supply chain and furthering the sustainable development of prefabricated buildings. [ABSTRACT FROM AUTHOR]

To deal with disruption at the supply side, the use of backup suppliers is among the commonly employed strategies. In this research, we derived a dual sourcing model using capacity reservation contracts for a supply chain consisting of a retailer, a primary supplier who may experience disruption, and a reliable backup supplier. This dual sourcing model is a reactive strategy that balances flexibility and redundancy to improve supply chain resilience. The aim of the proposed model is to help find optimal solutions for the reserved capacities of the retailer to the suppliers and the constructed capacities of the suppliers. It has been derived in this research that the above-mentioned reserved/constructed capacities exist uniquely. Sensitivity analyses were also conducted to examine the impact of contract parameters on decision variables. This research contributes to existing knowledge by providing deeper insights into the application of the capacity reservation contract to handle supply disruption. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry faces increased sustainability demands. The Environmental, Social, and Governance (ESG) framework is most widely accepted to discuss corporate sustainability, but little is known about the effects of ESG measure implementation on supply chain security. Understanding these effects is especially important in the face of drug shortages. Leveraging a quantitative and a qualitative method based on a grounded theory approach, we aim to investigate the state of ESG maturity of pharmaceutical corporations in Germany and develop a perspective on the effects of ESG measure implementation on supply chain security. We find publicly traded and larger companies having higher ESG target‐setting maturity than privately‐owned or smaller companies and that company priorities, costs, and regulatory processes limt ESG progress overall. Our findings suggest that ESG measure implementation can induce short‐term supply disruption risks. A risk assessment considers current ESG maturity, implementation effects outside of own operations, short‐term supply buffers, implementation timeline, and scope, and change management capabilities. [ABSTRACT FROM AUTHOR]

We study the inventory review policy for a healthcare facility to minimize the impact of inevitable drug shortages. Usually, healthcare facilities do not rely on a single source of supply, and alternative mechanisms are present. When the primary supplier is not available, items are produced in-house or supplied through another supplier, albeit with additional cost. Our aim in this study is to determine how optimal inventory parameters are adjusted depending on the availability of the primary supplier. We show that an approximation provides trivial results, yet fails to capture the nuances therein. Our proposed Markov chain model overcomes these issues, and numerical results illustrate the significant economic impact of inventory parameter optimization. Furthermore, we simulate uncertainty scenarios and provide sensitivity analyses concerning fixed ordering cost for the secondary supplier, shortage frequency, shortage duration, and demand rates. [ABSTRACT FROM AUTHOR]

In this study, a multi-period contingent sourcing model for sustainable sourcing is developed to mitigate the ripple effect caused by supply disruption. In this model, the manufacturer's main supplier is a recycled materials supplier who is subject to random disruptions and whose materials have setup time uncertainty. His backup supplier is a virgin materials supplier who is reliable. We investigate how the manufacturer's attitude towards risk affects his absorptive-capacity decision making and his sourcing strategy, both of which have not been addressed in the related literature. Our analyses reveal that the manufacturer's decisions are strategic substitutes as cost-related parameters (except the product holding cost) vary when he is risk-neutral and can become strategic complements when he is averse to risk. Furthermore, the manufacturer can improve contingent sourcing performance by leveraging adaptive capacity through choosing the proper timing at which to switch to the backup supplier. Finally, the manufacturer's sourcing strategy is sensitive to attitude towards risk when the cost difference between virgin and recycled materials is within a specific range over which higher restorative capacity from the main supplier and lower setup time uncertainty with recycled materials are required to justify a more risk-averse manufacturer's adoption of contingent sourcing. [ABSTRACT FROM AUTHOR]

Problem definition: For many supply chains, deep-tier suppliers, due to their small size and lack of access to capital, are most vulnerable to disruptions. We study the use of advance payment (AP) as a financing instrument in a multitier supply chain to mitigate the supply disruption risk in a traditional system (with limited visibility) and a blockchain-enabled system (with perfect visibility). The main goal of this paper is to shed light on how blockchain adoption impacts agents' operational and financial decisions as well as profit levels in a multitier supply chain. Academic/practical relevance: Traditionally, because of the limited visibility in the deep tiers, powerful downstream manufacturers' financing schemes offered to their immediate upstream suppliers are not effective in instilling capital into the deep tiers. Advancements in blockchain technology improve the supply chain visibility and enable the manufacturer to better devise deep-tier financing to improve supply chain resilience. Methodology: We develop a three-tier supply chain model and take a game-theoretic approach to compare how blockchain-enabled deep-tier financing schemes affect a financially constrained supply chain's optimal risk-mitigation and financial strategies. Results: We find that although improved visibility via blockchain adoption can help the manufacturer make informed supply chain financing decisions, whether it can benefit all supply chain members depends on the financing schemes in use. Blockchain-enabled delegate financing increases risk-mitigation investments and benefits all three tiers of the supply chain only when the tier 2 supplier is severely capital-constrained with the working capital below a threshold. Because delegate financing endows the intermediary tier 1 supplier with leverage over the manufacturer, the inefficiency inhibits an all-win outcome when the tier 2 supplier is not severely capital-constrained. Blockchain-enabled cross-tier direct financing exhibits a compelling performance as it always leads to win-win-win outcomes (and is thus ubiquitously implementable) regardless of the suppliers' working capital profile. Managerial implications: Our insights help firms assess opportunities and challenges associated with enhancing supply chain visibility via blockchain adoption. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on the Interface of Operations, Finance and Technology. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1123. [ABSTRACT FROM AUTHOR]

Alternative measures to deal with supply disruptions exist. We consider a make-to-order (MTO) supply chain with one manufacturer who sources from a single supplier. When a supply disruption occurs, the manufacturer can choose to satisfy some demand by either maintaining production through safety stocks or through a secondary contingent source, and turn some unmet demand into backorders on the basis of compensation. An optimal control model under consideration of the customers' dynamic reactions to the joint implementation of these strategies is formulated with the objective of minimising the cost of disruption. Through the application of Pontryagin's Maximum Principle, optimal mitigation strategies are established in closed form. They provide analytical guidance on how to dynamically and jointly adapt the quantity of contingent sourcing, the price of compensation, and the speed of safety inventory consumption. The results indicate how cost and time-related factors impact these strategies. We also demonstrate that pure strategies are only effective in tackling short supply shortages. For long disruptions, it is superior to adopt combined strategies that simultaneously incorporate two countermeasures in certain periods. [ABSTRACT FROM AUTHOR]

Enterprise captures market share from competitors through brand advertising and increases consumer demand for this category of products through general advertising. In this paper, considering a supply chain with a manufacturer who is responsible for generic advertising and a retailer who undertakes brand advertising, a Stackelberg differential game-theoretic model is established to examine the impact of supply disruption faced by the manufacturer on the optimal decision-making and profits of supply chain members. When the supply of raw materials is disrupted, the optimal advertising strategies and advertising subsidy scheme designed for the initially estimated product sales need to be revised. The key to solving this problem is to explicitly consider possible deviation profit margins of supply chain members and total market demand for products caused by changes in the original product supply. Considering that the deviations of the profit margins and the total market demand are arbitrary, we analytically derive several propositions and obtain the optimal advertising strategies and the advertising subsidy scheme before and after the supply disruption. Then, we explore the impact of supply disruption through numerical analysis and provide management insights that are helpful for managers to re-design the advertising strategies and advertising subsidy schemes under supply disruption. [ABSTRACT FROM AUTHOR]

Background: A serious shortage of cefazolin (CEZ) occurred in Japan in 2019. We compared the impact of the CEZ shortage on the selection of parenteral antibiotics at affected and non-affected hospitals. Methods: The data were extracted from a nationwide Japanese administrative database and included all hospitalized cases between April 2016 and December 2020. We defined ‘hospitals with shortage’ as those hospitals with a statistically significant decrease in the use of CEZ during the supply disruption period compared to the same months of the previous year; other hospitals as ‘hospitals without shortage’. We determined the proportion of each selected parenteral antibiotic use to the sum of all selected antibiotic use in the two groups of hospitals during the supply disruption period and during the same months of the previous year. A controlled interrupted time series (CITS) analysis was conducted to estimate the impact of the CEZ shortage on each antibiotic use and the cost of all parenteral antibiotics per patient day in hospitals with shortage as compared to those without shortage. Results: In the hospitals with shortage, the proportion of CEZ use to the sum of all selected antibiotics decreased (23.5–11.1%). The decrease in CEZ use was mainly offset by the use of ceftriaxone, ceftriaxone, and ampicillin/sulbactam. The CITS analysis showed a statistically significant increase in the use of broader-spectrum beta-lactams and clindamycin during the supply disruption period (flomoxef up 58.1%, cefotiam up 63.1%, cefmetazole up 14.5%, ceftriaxone up 13.9%, and clindamycin up 20.1%). The analysis showed no statistically significant change in the cost of all parenteral antibiotics per patient day. Conclusions: During the CEZ supply disruption, there was a statistically significant increase in the use of broader-spectrum beta-lactams and clindamycin in hospitals with shortage compared with those without shortage.

Due to possible supply disruptions because of a low-cost unreliable supplier, a firm may use a high-cost reliable supplier as an additional regular supplier (dual sourcing) or an emergency backup supplier with an extra emergency cost (contingent sourcing). We consider the firm's sourcing problem when the pricing decision is made before any supply uncertainty is resolved (committed pricing) or after the supply state is realised (responsive pricing). By comparing the relative value of responsive pricing in contingent sourcing to that in dual sourcing, we study the relationship between contingent sourcing and responsive pricing in mitigating supply disruption risks. We show that the emergency cost and potential lost sales caused by disruption probability jointly impact the interplay of these two strategies. More specifically, when the emergency cost is low and the potential lost sales are lower under contingent sourcing than that under dual sourcing, contingent sourcing and responsive pricing are substitutes; otherwise, they are complements. Furthermore, we examine how disrupted capacity, i.e. the quantity that the unreliable supplier can deliver when disrupted, impacts the interplay, and find that the probability of the substitution relationship becomes higher when the disrupted capacity increases. We also find that under committed pricing, contingent sourcing is not optimal for any value of disruption probability when the emergency cost is high, a phenomenon that does not exist under responsive pricing. [ABSTRACT FROM AUTHOR]

In case of supply disruption following major disasters, many supply chains tend to break down due to stock-outs and take a long time to recover. However, by keeping emergency sources of supply, some supply chains continue to function smoothly even after a major disaster. In this work, using a game-theoretic-framework, we consider a two-suppliers-one-retailer supply chain with price-dependent stochastic demand in which suppliers are prone to disruption. To investigate the impact of supply disruption we consider two models: SC model, in which the retailer does not maintain any emergency sources of supply against any supply disruption, and SCB model, in which the retailer maintains a backup supplier to mitigate the impact of supply disruption. We mainly focus on the pricing strategies of the suppliers and the mitigating strategies of the retailer under supply and demand uncertainty. We address two coordinating mechanisms to enhance supply chain performance. Our results indicate that in the presence of supply disruption, even with lower probabilities, the retailer would always prefer to take the advantage of a backup supplier and the optimal reserve quantity increases with disruption probabilities. We further investigate the scenario in which the suppliers would always prefer to cooperate with each other. [ABSTRACT FROM AUTHOR]

Giant retailers sell their store brands and (re)sell substitute national brands for manufacturers (suppliers) simultaneously, which becomes an important sourcing and selling supply chain strategy in the sharing economy era. In this paper, we develop a game-theoretic model to examine the impact of risk-averse suppliers on dual sourcing decisions under supply disruption when two substitute products are sold in a common market. We examine the impact of risk-aversion, demand volatility, and supply disruption on the supply chain profitability and pricing policies. We hypothesise and then analytically derive several propositions on the dependencies of the profitability and risk-aversion, demand volatility and pricing, and risk-aversion and pricing. We also conduct numerical analysis to explore the impact of disruption. We offer insights for managers and researchers alike by uncovering the role of supplier risk-aversion for decision-making settings with substitute products and dual sourcing when product supply may be disrupted. [ABSTRACT FROM AUTHOR]

Recently, owing to uncertain business environments, the supply chain has become more prone to disruption. In this paper, we model a closed-loop supply chain comprising suppliers, backup suppliers, factories, markets, collection centers, and disposal centers. We propose a robust optimization model for the design of a closed-loop supply chain network that is flexible and robust to disruptions. The proposed mathematical model considers, in the before-disruption stage, the location of the manufacturing factory, and contraction with the backup supplier in addition to under the contracting supplier, possibility of disruption of transshipment, and, in the reactive stage, depression of productivity at the facility. We aim to determine the location of the manufacturing facility, select the supplier, finalize the backup supplier contract, allocate stock at the before-disruption stage, and determine the quantities for production, transportation, and collection at the reactive stage of each scenario. The purpose is to minimize the total cost of the supply chain and regret value, which is the value that could reduce shortage or the delaying effect across different disruption scenarios. The proposed bi-objective mathematical model is solved using an optimization solver by weighting each objective function into a single objective problem. Computational results demonstrate the significant impact of considering disruptive events on the selected supply base and relieve of disruption effects of each scenario based on the decision of contracting backup suppliers.

The unprecedented outbreak of COVID-19 has left many multinational enterprises facing extremely severe supply disruptions. Besides considering triple-bottom-line requirements, managers now also have to consider supply disruption due to the pandemic more seriously. However, existing research does not take these two key objectives into account simultaneously. To bridge this research gap, based on the characteristics of COVID-19 and similar global emergency events, this article proposes a model that aims to solve the problem of sustainable supplier selection and order allocation considering supply disruption in the COVID-19 era. It does so by using a multi-stage multi-objective optimization model applied to the different stages of development and spread of the pandemic. Then, a novel nRa-NSGA-II algorithm is proposed to solve the high-dimensional multi-objective optimization model. The applicability and effectiveness of the proposed model is illustrated in a well-known multinational producer of shortwave therapeutic instruments. JEL Classification: M11 [ABSTRACT FROM AUTHOR]

A serious shortage of cefazolin (CEZ) occurred in Japan in 2019. We compared the impact of the CEZ shortage on the selection of parenteral antibiotics at affected and non-affected hospitals. The data were extracted from a nationwide Japanese administrative database and included all hospitalized cases between April 2016 and December 2020. We defined 'hospitals with shortage' as those hospitals with a statistically significant decrease in the use of CEZ during the supply disruption period compared to the same months of the previous year; other hospitals as 'hospitals without shortage'. We determined the proportion of each selected parenteral antibiotic use to the sum of all selected antibiotic use in the two groups of hospitals during the supply disruption period and during the same months of the previous year. A controlled interrupted time series (CITS) analysis was conducted to estimate the impact of the CEZ shortage on each antibiotic use and the cost of all parenteral antibiotics per patient day in hospitals with shortage as compared to those without shortage. In the hospitals with shortage, the proportion of CEZ use to the sum of all selected antibiotics decreased (23.5–11.1%). The decrease in CEZ use was mainly offset by the use of ceftriaxone, ceftriaxone, and ampicillin/sulbactam. The CITS analysis showed a statistically significant increase in the use of broader-spectrum beta-lactams and clindamycin during the supply disruption period (flomoxef up 58.1%, cefotiam up 63.1%, cefmetazole up 14.5%, ceftriaxone up 13.9%, and clindamycin up 20.1%). The analysis showed no statistically significant change in the cost of all parenteral antibiotics per patient day. During the CEZ supply disruption, there was a statistically significant increase in the use of broader-spectrum beta-lactams and clindamycin in hospitals with shortage compared with those without shortage. [ABSTRACT FROM AUTHOR]

We study the problem of whether and how a patent-holding firm (supplier S1) should license its technology patent to a potential rival (supplier S2). If the license is given, the two suppliers both produce a critical component for their downstream manufacturer simultaneously (sequentially) when the manufacturer adopts a dual (contingent) sourcing mode. However, both suppliers face risks of complete supply disruptions. In the study, we examine how the supply risk affects S1's technology licensing willingness and under which conditions royalty or fixed-fee licensing is its optimal strategy. The results show that when the manufacturer adopts contingent sourcing, S1 is always willing to license its technology to S2 and fixed-fee licensing is the optimal strategy. However, when dual sourcing is adopted, either royalty and fixed-fee licensing could be S1's optimal licensing strategy, depending on the reliability degree of the suppliers' productions. Our findings extend the conclusion from previous researchers about royalty and fixed-fee licensing in a Cournot duopoly model. [ABSTRACT FROM AUTHOR]

Panic buying is now a frequent occurrence in many countries, leading to stockouts and supply chain disruptions. This paper highlights consumers' panic buying behavior in different types of regions and the impact of different replenishment strategies after an emergency supply disruption. Panic buying behavior occurs when consumers try to mitigate the negative impact of a supply disruption. Therefore, this paper develops a consumer-based agency model to study the correlation between public opinion and panic buying and simulates the influence of consumers' panic buying behavior under different situations in a complex network. The results show that the spread of panic feelings can lead to panic buying behavior among consumers, which then shocks the retailer market. The distribution of supplies according to the type of city and the number of people can have an impact on consumer panic buying behavior, and when the government adopts a restrictive strategy, implementing a quota policy or uniform rationing is very effective in reducing the number of consumers participating in panic buying. [ABSTRACT FROM AUTHOR]

The driver shortage is a perennial problem in the trucking industry. The review of literature reveals currently there is a lack of holistic approach to address driver shortage problem. This paper presents a systems approach to understand the factors influencing the issue of truck driver shortages in an emerging economy context and their evolution over time. For this study, we define truck driver shortage as: "The shortfall in the market when the quantity of truck drivers supplied is below the number of truck drivers demanded". A system dynamic model has been developed to study a representative truck segment unit. The model is validated and simulated for ten years. The findings from the proposed model indicate that the current system and other environmental factors do not support the truck driver supply increase, drivers and shortages are likely to become acute in the coming years. The study proposes appropriate intervention for the government, and other stakeholders must design proper steps to fill the gap beyond a specific timeframe. The study contributes to the existing literature as a pioneering work on driver shortages in the emerging economy context. Also, the study is one of its kind in using a holistic, quantitative approach to modeling the driver shortage phenomenon. In the process, the study elicits complex relationships that prevail among the factors included in the study. • A system approach to understanding key issues for truck driver shortages. • The current systems and other environmental factors are not associated with the driver supply increase. • A pilot study examining driver shortages in the emerging economy context. [ABSTRACT FROM AUTHOR]

The 2019 coronavirus disease (COVID-19) epidemic has caused serious disruptions in food supply networks. Based on the case of the remerging epidemic in China, this paper aims to investigate food supply network disruption and its mitigation from technical and structural perspectives. To solve the optimal policy choice problem that how to improve mitigation capability of food supply networks by using traceability technology and adjusting network structure, the occurrence mechanism of food supply network disruptions is revealed through a case study of the remerging COVID-19 outbreak in Beijing's Xinfadi market. Five typical traceability solutions are proposed to mitigate network disruptions and their technical attributes are analyzed to establish disruption mitigation models. The structure of food supply networks is also controlled to mitigate disruptions. The structural attributes of three fundamental networks are extracted to adjust the network connections pattern in disruption mitigation models. Next, simulation experiments involving the disruption mitigation models are carried out to explore the independent and joint effects of traceability technology and network structure on mitigation capability. The findings suggest that accuracy makes a more positive effect on the mitigation capability of food supply networks than timeliness due to the various technical compositions behind them; the difference between these effects determines the choice decision of supply networks on traceability solution types. Likewise, betweenness centralization makes a positive effect but degree centralization makes a negative effect on mitigation capability because intermediary firms and focal firms in food supply networks have different behavior characteristics; these effects are both regulated by supply network types and exhibit different sensitivities. As for the joint effect of technical and structural attributes on mitigation capability, the joint effect of accuracy and betweenness centralization is bigger than the independent effects but smaller than their sum; the joint effect of timeliness and betweenness centralization depends on networks type; while the positive effect of accuracy or timeliness on mitigation capability is greater than the negative effect of degree centralization; theses joint effects are caused by the complicated interactive effects between technical composition and behaviors of intermediary firms or focal firms. These findings contribute to disruption management and decision-making theories and practices. [ABSTRACT FROM AUTHOR]

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Sourcing in the face of supply chain disruptions has been one of the most challenging tasks in supply chain management, particularly when such disruptions occur due to natural calamities, such as flood, fire, and earthquake, affecting both the primary and the backup suppliers. Invariably, such disruptions lead to reduced supply from the primary supplier, encouraging the supplier to place fresh orders with the backup suppliers. In order to mitigate the adverse effect of supply disruption, in this article we use the concepts underlying the well‐known Duckworth–Lewis–Stern method, used in cricket, to revise the supply target of the primary supplier and to decide a target for the backup supplier. We simulated the supply disruption scenarios in an experimental setting by conducting a two‐round questionnaire survey among 300 purchase managers. The means and variances of the participants' estimates of probabilities of meeting the revised targets within the scheduled time for various model‐generated supply scenarios were used to find the participants' risk preferences. In the second‐round survey, the participants, clustered in groups of 10, ranked their own risk preferences. These ranks were used to find the optimal portfolio choices. Finally, we validated the theoretical predictions for the risk options using two approaches—one, at the group level by estimating the within‐ and the between‐group risk preferences of buyers, and, two, at the aggregate level, by considering all the participants, fitting quantile regression model to the experimental results, and estimating the risk preference structures for different quantiles of the relative risk–return trade‐off distributions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic crisis marks a rare example of a supply disruption that had a devastating effect on the global economy. The vaccine has then been considered to be an effective long-term solution. The particularity of the supply chain of the COVID-19 vaccine is that the procurement is made by government agencies directly from the manufacturers, thus bypassing the typical vaccine supply chain of distributors and wholesalers. The first purpose of this paper is to further understand the public procurement strategies (PPSs) for the COVID-19 vaccine in a pandemic situation, using a methodology that brings together Kraljic's purchasing portfolio model, Porter's five forces analysis and a multi-attribute decision making method. The second aim is to evaluate the sustainability of a PPS, using the sustainable development analytical grid (SDAG), an analytical tool that addresses six dimensions: ecological, social, economic, ethical, cultural and governance. To assess the effectiveness of the methodology, we consider Morocco, an emergent country, as a case study. It results that Morocco's approach is in line with the theoretical strategy: diversification and bringing the production in house. The sustainability assessment shows that the PPS covers the six dimensions, but considering the urge for an economic and social recovery, some sustainable development objectives are to be prioritized. [ABSTRACT FROM AUTHOR]

Supply chain disruptions can be costly and disruptive, and reducing supplier uncertainty through investments can be an effective strategy for mitigating these risks. In this study, we explore ordering and investment strategies in a downstream competitive environment, considering supply uncertainty. We consider two buyers ordering from a single supplier and competing in a Cournot market. Buyers must balance investment benefits against costs when making investment decisions. We find that without downstream competition, a relatively low investment proportion can yield a "win-win" outcome. Under downstream competition, an increased number of investors does not necessarily lead to higher supplier reliability. When both buyers invest at a high ratio, the significant decline in order quantities may results in lower supplier reliability. When there is only one investing buyer in the market, even though the competitor may benefit from spillover reliability enhancement — free-riding, the buyer may also be willing to provide an investment subsidy to improve the supplier's reliability. When both buyers invest at a moderate ratio, downstream competition may benefit all supply chain members. Moreover, increasing wholesale price differentiation and endogenous investment ratios can effectively reduce free-riding behavior of the non-investing buyer. These findings have practical implications for supply chain managers aiming to reduce supply risks and allocate investment resources efficiently. • Buyers invest in suppliers to improve reliability under downstream competition. • Without competition, lower investment proportion leads to a win-win outcome. • Buyers may invest in suppliers despite the potential for competitor free-riding. • All supply chain members may benefit from the investment with downstream competition. • Differential wholesale prices and endogenous investment ratios reduce free-riding. [ABSTRACT FROM AUTHOR]

Due to the prevalence of supply disruptions disturbing supply chain and logistics operations, a firm should tackle such disruptions using appropriate approaches. In this research work, a dual-sourcing approach is applied by a firm. The main decision regarding the firm sourcing from two unreliable suppliers is to derive an optimal ordering policy that maximizes the firm’s expected profit. Considering the stochastic ordering yield bridges a gap in the literature. The firm’s expected profit function is analytically formulated, and the viability of the developed mathematical expression is illustrated using numerical experiments and sensitivity analyses. Through the sensitivity analyses, the importance of considering the stochastic ordering yield is confirmed, as the optimal ordering policy varies on the variance of the ordering yield. Furthermore, failing to consider the stochastic ordering yield leads to incorrect conclusions regarding the optimal ordering policy; in addition, the increase in the variance of the ordering yield prevents the firm from benefiting from incremental profit.

We consider a risk-averse firm’s sourcing problem with two suppliers: a dedicated one and a backup one. The dedicated supplier charges a lower wholesale price, but faces potential disruption risk. The backup supplier is assumed to be perfectly reliable, but charges a higher wholesale price. To mitigate the disruption risk, the firm uses a joint backup supply and responsive pricing strategy. We consider three common backup strategies between the firm and the backup supplier: advance purchase, reservation and contingency purchase. We derive under what conditions each strategy could be optimal. The results show that the thresholds that determine the optimal backup supply strategy are affected by the risk aversion level. When the risk aversion level is not extremely high, the firm should choose among the three backup suppliers by considering the disruption probability and the reservation fee. Firms with a higher risk aversion level tend to rely more on ex-ante preventive efforts (i.e. reservation or advance purchase strategies). When the risk aversion level is extremely high, the firm never considers the contingency purchase strategy, even for a low-probability disruption event. Additionally, market conditions yield non-negligible influences on the firm’s strategic choices due to the existence of risk aversion. [ABSTRACT FROM AUTHOR]

We study optimal sourcing decisions for a firm with a dedicated supplier and a backup supplier. The dedicated supplier charges a lower wholesale price but faces a potential disruption risk. The backup supplier is assumed to be perfectly reliable but charges a higher wholesale price. The primary question we address is how the firm should cooperate with the backup supplier to hedge against the disruption risk. We consider three common cooperation options: advance purchase, reservation and contingency purchase. Our basic results show that the firm should choose advance purchase strategy if the disruption probability is high, while contingency purchase strategy benefits the firm more if the disruption probability is low. Under an intermediate disruption probability, the firm should choose reservation strategy only if the reservation fee is sufficiently low. Then, we explore the optimal backup strategy under partial disruption risk. The results show that the advance purchase and the reservation strategy should be adopted more widely when the dedicated supplier guarantees a relatively high yield rate after disruption. [ABSTRACT FROM AUTHOR]

Purpose – The impact of supply disruptions from unplanned events can cause goods shortage, limited responsiveness and high opportunity cost thus compromising development aid programmes' achievement targets. These situations force humanitarian aid agencies to develop new strategies for effectively managing their supplies. The purpose of this paper is to illustrate the foundation of humanitarian supply chain resilience through the development of an adapted Kraljic portfolio model. Design/methodology/approach – Action research was used to adapt and validate the Kraljic portfolio model to the development aid context. The research team worked with a humanitarian aid agency in developing criterions and used Analytical Hierarchical Process (AHP) in weighting those key criterions. Findings – The adapted portfolio model was able to evaluate purchases done by the aid agency by incorporating different perspectives related to the strategic importance of purchase and supply vulnerability. In particular, development aid programmes require large supplies annually. Better classification offers improved visualisation of purchases, leading to a more precise adoption of mitigation strategies and policies to minimise supply disruption risks. Research limitations/implications – Adapting the Kraljic portfolio model is a stepping-stone to building humanitarian supply chain resilience. The proposed humanitarian supply chain resilience framework is based on the foundation that current humanitarian supply chain needs to be re-engineered. In order to re-engineer, the supply base strategy must first be revisited. Practical implications – Many aid agencies do not have a holistic view on their purchases and commonly apply a transactional classification of purchases that only considers the consumption values. Purchasing strategies mostly focus on cost minimisation, whereas risk mitigations have been disregarded. The proposed portfolio model overcomes these drawbacks. Societal impact may be limited but development aid agencies will be able to offer more reliable aid delivery as part of their mandate. Originality/value – The proposed portfolio model is among the first tool to guide humanitarian aid agencies to develop procurement strategies to alleviate supply disruptions and increase development aid programmes resilience.

Market demand is becoming increasingly time-sensitive in competitive environments. Hence, supply disruptions will have a more serious impact on the profits of supply chains. This study applies a Stackelberg competition between a single supplier and a single manufacturer in a time-sensitive supply chain in a cloud manufacturing environment. We aim to address the supplier’s production capacity recovery issues and the manufacturer’s incentive decision issues after supply disruption. We find that the supplier is in a weak position when the information is symmetrical. The manufacturer can encourage the supplier to shorten the recovery time by raising the unit wholesale price. When the supplier’s unit production cost remains unchanged but the unit wholesale price increases, the profit of the supplier first increases and then decreases. In addition, under the centralized decision-making setting, the optimal recovery time of the supplier is shorter and the optimal unit market price of the product is lower than that under decentralized decision-making. We further find that resource sharing can shorten the optimal recovery time, but it does not necessarily play an incentivizing role.

After reviewing the problems of supply disruption caused by worldwide public health events, a two-echelon dual-marketing-channel supply model is presented in this paper, whose participants are one pharmaceutical company with disruption risks, one pharmaceutical company without disruption risks (NDPC), and one pharmaceutical retailer (PR). Their marketing consists of online and offline marketing channels. The PR signs an option contract with the NDPC to secure an adaptive and continuous pharmaceutical supply. The model is divided into three sub-models: single coalition structure, double coalition structure, and multi-party coalition structure, whose coalitional strategies and profits are investigated respectively. Their optimal decisions under disruption risks also are compared, and their pharmaceutical quality and process resilience are discussed. The merit of the model is to combine option contracts, government double subsidies, and business interruption insurance in the supply chain. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to test the impact of the coronavirus (Covid-19) pandemic on large firms, with regard to a supply disruption, productivity, customer satisfaction and firm performance. Design/methodology/approach – A cross-sectional survey design and stratified sampling technique were implemented for employee selection and data gathering. Confirmatory factor analysis (CFA) was used to examine the data and model fitness, while the structural equation model was used for hypotheses testing. Findings – The pandemic triggered supply disruptions, but did not significantly impact the productivity of manufacturing firms directly. However, supply disruption positively and significantly impacted productivity. Organisational productivity had no significant impact on customer satisfaction, however, when mediating the relationship between Covid-19 and customer satisfaction, it produces a positive indirect effect. Finally, Covid-19 and supply disruption when mediated by organisational productivity both had negative significant relationships on performance. Research limitations/implications – Having a unique model, it creates a trail for future researchers to explore further. Though customer satisfaction was expected to be affected by the pandemic, it is interesting to find out that customer satisfaction when mediated by organisational productivity was positively influenced. Practical implications – Disruptions are inevitable, managers must balance the pursuit of customer satisfaction and productivity so that one does not erode the other. Emphasis must be channelled towards managing the productivity of the firm to maintain customer satisfaction during these uncertain times. Deliberate steps like manufacturing flexibility investments should be initiated. Originality/value – The first study to examine Covid-19, supply disruption, customer satisfaction, organisational productivity and performance in the Nigerian manufacturing sector.

In this paper, we study how and when competing firms cooperate horizontally to manage supply disruption risks. Specifically, we consider a system consisting of two competing retailers that sell the same product in a market where the price depends on the total quantity of products on hand. Each retailer has its own supply disruption probability. In terms of complete information, we show that they should cooperate to manage supply disruption risk and design a cooperation mechanism using cooperative bargaining. Furthermore we study the cooperation problem in the case of private information. We model the problem as a dynamic bargaining model in which the informed retailer’s supply disruptions are either high or low. In the dynamic negotiation process, the informed retailer can signal information through their offers. With the uninformed retailer’s acceptance criteria, we characterize the perfect Bayesian equilibrium of the bargaining game. We show that the uninformed retailer may reach an agreement with either the high type or the low type first, or with both simultaneously. We find that retailer’s surplus with private information may be higher than that with complete information.

Due to the prevalence of supply disruptions disturbing supply chain and logistics operations, a firm should tackle such disruptions using appropriate approaches. In this research work, a dual-sourcing approach is applied by a firm. The main decision regarding the firm sourcing from two unreliable suppliers is to derive an optimal ordering policy that maximizes the firm’s expected profit. Considering the stochastic ordering yield bridges a gap in the literature. The firm’s expected profit function is analytically formulated, and the viability of the developed mathematical expression is illustrated using numerical experiments and sensitivity analyses. Through the sensitivity analyses, the importance of considering the stochastic ordering yield is confirmed, as the optimal ordering policy varies on the variance of the ordering yield. Furthermore, failing to consider the stochastic ordering yield leads to incorrect conclusions regarding the optimal ordering policy; in addition, the increase in the variance of the ordering yield prevents the firm from benefiting from incremental profit. [ABSTRACT FROM AUTHOR]

This paper studies a continuous-review stochastic inventory problem for a firm facing random demand and random supply disruptions. The supplier experiences operational (on) and disrupted (off) periods with exponentially distributed durations. The firm adopts an order-up-to level policy during the on period and additionally can release an emergency order based on the inventory level just before disruption. This inventory policy is described by a continuous-time Markov chain model. We analyze the model for two different lead time scenarios and suggest solution approaches yielding the optimal policy parameters. In a numerical study, we explore the value of exercising such a policy and show that an emergency ordering opportunity at the disruption time brings substantial cost savings in cases with high lost sales cost, long off period, and low percentage of supplier's availability. [ABSTRACT FROM AUTHOR]