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To Act or Wait: The Impact of Disruption Duration Estimates on Manufacturer’s Response Strategies

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... Supply chain resilience became of utmost importance for companies because of the increasing frequency of disruptions and long-term vulnerabilities triggered by the pandemic, semiconductor shortages, and geopolitical tensions (Dotoli et al., 2016;Sawik, 2021;Jeihoonian et al., 2022;Dubey et al., 2023;Hägele et al., 2023). While the broad and growing interest in supply chain resilience among practitioners became visible after the COVID-19 pandemic, the research community started developing the associated body of knowledge long before the pandemic disruptions (Blackhurst et al., 2005;Sheffi and Rice, 2005;Bode et al., 2011;Dubey et al., 2021a;Jin et al., 2024). The proposed resilience capabilities, modeling, and measurement techniques have helped supply chain managers to improve resilience and start preparing for resilience-centric supply chain D. Ivanov / Intl. ...
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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.
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Firms are exposed to a variety of low-probability, high-impact risks that can disrupt their operations and supply chains. These risks are difficult to predict and quantify; therefore, they are difficult to manage. As a result, managers may suboptimally deploy countermeasures, leaving their firms exposed to some risks, while wasting resources to mitigate other risks that would not cause significant damage. In a three-year research engagement with Ford Motor Company, we addressed this practical need by developing a novel risk-exposure model that assesses the impact of a disruption originating anywhere in a firm's supply chain. Our approach defers the need for a company to estimate the probability associated with any specific disruption risk until after it has learned the effect such a disruption will have on its operations. As a result, the company can make more informed decisions about where to focus its limited risk-management resources. We demonstrate how Ford applied this model to identify previously unrecognized risk exposures, evaluate predisruption risk-mitigation actions, and develop optimal postdisruption contingency plans, including circumstances in which the duration of the disruption is unknown.
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The recent coronavirus disease 2019 pandemic has shown that shortages and supply chain disruptions can have catastrophic effects on the real economy. These observations bring about reflections and first-order questions. How can we design supply chain networks that are robust and resilient to demand and supply shocks? Can we quantify the indirect effects caused by buyers’ and suppliers’ defaults in the network? Is it always cost effective to steer the system toward higher buyers’ and suppliers’ diversification? In the paper “Disruption and Rerouting in Supply Chain Networks,” Birge et al. argue that in highly capitalized networks, diversifying demand and supply across a larger number of counterparties may result in a more fragile network. Single-sourcing strategies are optimal for a firm only if the firm’s supplier default probability is low, but they perform worse than multiple-sourcing strategies otherwise.
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The chaotic response of the US Strategic National Stockpile to COVID‐19 during 2020 highlighted the inadequacy of the inventory‐based approaches to disaster response. This paper examines the integration of stockpile inventory, backup capacity, and standby capability to meet the disaster‐related surge in demand in the future. We present a two‐period model of such an integrated system for consumable items with uncertain demand that follows a general probability distribution. Our model incorporates standby capability in period 1 that can be converted to additional capacity for use in period 2, with the conversion yield being deterministic or stochastic. Our main results are: (1) Adding capacity in addition to inventory is beneficial only when the capacity reservation‐related costs are relatively lower than the inventory‐related costs. In this case, adding capacity will decrease the inventory needed in both periods, the shortfall probability, and the total expected cost. (2) Adding capability in period 1 is cost‐effective only when the ratio of capability‐development cost to conversion yield is lower than the capacity reservation cost. In this case, investing in capability results in less inventory and less reserved capacity in period 2. (3) Higher uncertainty in capability conversion yield reduces the attraction of developing capability in period 1. Consequently, less capability would be developed in period 1, while more inventory and capacity would be needed in period 2 in the face of a higher shortfall probability. This article is protected by copyright. All rights reserved
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Companies that experience a disruption in their supply chain often face a difficult decision ‐ either accept the information that they have regarding the duration of the disruption, or invest in collecting better information. This choice is not clear since better information may not be attainable, and if it is attainable, it may not improve operational decision‐making. In light of this dilemma, we collaborate with a multinational division of a Fortune 500 manufacturing firm to develop stochastic linear programming models that quantify the value of disruption duration information. Our models allow us to examine characteristics of the disrupted part that may be associated with the value of better information. We focus on characteristics that are knowable at the outset of the disruption, as those can help the firm decide whether to invest in collecting better information. Using our research partner's supply chain and production data, we find that the value of information can vary materially ‐ from less than 1% to over 99% of the cost of the disruption, underscoring the value of identifying disruptions that are sensitive to information quality. To address this, we use the company's data to identify several part‐related characteristics that influence the value of disruption duration information. These findings can help managers to identify parts in their own supply chains whose impact in a disruption is sensitive to different levels of duration information, and allow them to make informed decisions on whether or not to gather better information when a disruption strikes.
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An outbreak of deadly COVID-19 virus has not only taken the lives of people but also severely crippled the economy. Due to strict lockdown, the manufacturing and logistics activities have been suspended, and it has affected the demand and supply of various products as a result of restrictions imposed on shopkeepers and retailers. Impacts of COVID-19 are observed ubiquitously in every type of units from different sectors. In this study, a simulation model of the public distribution system (PDS) network is developed with three different scenarios to demonstrate disruptions in the food supply chain. Difficulties have been increased in matching supply and demand in a vast network of PDS because of changing scenarios with the growth of infected cases and recovery. This paper also highlights the importance of a resilient supply chain during a pandemic. Our proposed simulation model can help in developing a resilient and responsive food supply chain to match the varying demand, and then further assist in providing decision-making support for rerouting the vehicles as per travel restrictions in areas. Paper has been summarised with significant highlights and including future research scope for developing a more robust food supply chain network.
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A multi-portfolio approach and a scenario-based stochastic mixed integer program are developed for risk-averse selection of resilient supply and demand portfolios in a geographically dispersed multi-tier supply chain network under disruption risks. The resilience of the supply chain is improved by selection of primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously for each disruption scenario, recovery and transshipment portfolios are determined and decisions on usage the pre-positioned inventory are made to minimize conditional cost-at-risk or maximize conditional service-at-risk. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. In particular, the impact of unit penalty for unfulfilled demand for products on resilience of the risk-averse supply portfolio is investigated. The findings also indicate that the developed multi-portfolio approach forms an embedded network flow structure that leads to computationally efficient stochastic mixed integer program with a very strong LP relaxation.
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We consider optimization problems related to the scheduling of multi-echelon assembly supply chain (MEASC) networks that have applications in the recovery from large-scale disruptive events. Each manufacturer within this network assembles a component from a series of sub-components received from other manufacturers and, due to high qualification standards, each sub-component of the manufacturer is single sourced. Our motivating industries for this problem are defense aircraft and biopharmaceutical manufacturing. We develop scheduling decision rules that are applied locally at each manufacturer and are proven to optimize two industry-relevant global recovery metrics: (i) minimizing the maximum tardiness of any order of the final product of the MEASC network (ii) and minimizing the time to recover from the disruptive event. Our approaches are applied to a data set based upon an industrial partner’s supply chain to show their applicability as well as their advantages over integer programming models. The developed decision rules were proven to be optimal, faster, and more robust than the equivalent IP formulations. In addition, they provide conditions under which local manufacturer decisions will lead to globally optimal recovery efforts. These decision rules can help managers to make better production and shipping decisions to optimize the recovery after disruptions and quantitatively test the impact of different pre-event mitigation strategies against potential disruptions. They can be further useful in MEASCs with or expecting a large amount of backorders.
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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.
Article
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.
Article
We consider an infinite horizon, continuous review inventory model with deterministic stationary demand where supply is subject to disruption. The supply process alternates between two states randomly: one in which it functions normally (ON-period) and one in which it is disrupted (OFF-period). In this setting, we seek the value of disruption information which enables the buyer to place “disruption orders” at the beginning of OFF-periods. Utilizing renewal theory, we derive the total expected cost and characterize the optimal regular order-up-to level together with the order-up-to level for disruption orders. We also conduct an extensive numerical analysis and compare the results with the model with no opportunity of disruption orders. We observe that if the shortage cost is relatively high, and the disruption risk is significant (in terms of duration and/or frequency), placing a disruption order reduces the expected total cost significantly.
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Supply chain disruptions can occur at any node if there is a vast array of triggers. A common trigger for manufacturing disruptions is supply interruptions. This paper considers a single-stage supply chain with single manufacturer sourcing from a single supplier, where supply disruptions lead to a production pause and the demand is deterministic. In view of different hitting times and durations of disruptions, this paper compares and selects proactive and reactive strategies for supply disruption management via a cost minimization model. Based on the comparison, two types of dynamic strategies are proposed to guide the mitigation approaches as the disruptions continue. One is a dynamic reactive strategy for a non-prevention system and is called passive-backup, and the other is a dynamic combination strategy that contains reactive and proactive strategies for the prevention system and is called recovery-backup. How the lead time of backup sources, disruption starting time, cost of lost sales, backup costs and backorder rate impact the dynamic strategies is also explored in this paper.
Article
To mitigate supply disruption risks, some manufacturers consider a flexible sourcing strategy, where they have an option of sourcing from multiple suppliers, including regular unreliable suppliers and backup reliable ones. Our objective is to evaluate the costs and benefits associated with flexible sourcing when suppliers are strategic price setters. We show that when each supplier announces a single (wholesale) price, such a game leads to a conflict of incentives and is not realistic in most practical settings. Therefore, we focus on a contingent-pricing game, with wholesale prices contingent on the manufacturer’s sourcing strategy. We describe the resulting equilibrium outcomes corresponding to the manufacturer’s different sourcing and inventory strategies. We show that in equilibrium, inventories are carried either by the manufacturer or by the unreliable supplier, but not both. The manufacturer does not necessarily benefit from the existence of a backup supplier and, in fact, is typically worse off. Similarly, supply chain performance may degrade. Thus, an up-front commitment to sole sourcing may be beneficial. Interestingly, suppliers may benefit from flexible sourcing even though the manufacturer does not. The main results extend to cases when partial backup sourcing is allowed, both suppliers are unreliable, recovery times are non-memoryless, an unreliable supplier may provide a richer set of contingent prices, or the supplier may be risk averse. The online supplementary document is available at https://doi.org/10.1287/mnsc.2016.2626. This paper was accepted by Yossi Aviv, operations management.
Article
When supply disruption occurs following major disasters, many supply chains tend to breakdown due to stock-outs or lost sales and take a long time to recover. However, by keeping one or more emergency sources of supply, some supply chains continue to function smoothly, satisfying consumer demand even after a major disaster. In this study, we use the game-theory-based framework to model a supply chain with random and price dependent demand in a competitive environment where suppliers are prone to disruption. To mitigate the negative effect of supply disruption, a backup supplier is incorporated into the proposed model as an emergency source of supply. Further, to enable supply chain coordination, two coordinating mechanisms are addressed. In our study, we investigate how these coordinating contracts work in a supply chain under risk and competitive environment. Finally, we perform a comprehensive numerical study to show the impact of the model parameters on the equilibrium solutions and to signify the performance of the proposed coordination contracts.
Article
This paper studies the real-time crew rescheduling problem in case of large-scale disruptions. One of the greatest challenges of real-time disruption management is the unknown duration of the disruption. In this paper we present a novel approach for crew rescheduling where we deal with this uncertainty by considering several scenarios for the duration of the disruption. The rescheduling problem is similar to a two-stage optimization problem. In the first stage, at the start of the disruption, we reschedule the plan based on the optimistic scenario (i.e., assuming the shortest possible duration of the disruption), while taking into account the possibility that another scenario will be realized. We require a prescribed number of the rescheduled crew duties (a sequential list of tasks which have to be performed by a single crew member) to be recoverable. The true duration of the disruption is revealed in the second stage. By the recoverability of the duties, we expect that the first stage solution can easily be turned into a schedule that is feasible for the realized scenario. We demonstrate the effectiveness of our approach by an application in real-time railway crew rescheduling. The ideas of this paper generalize to certain vehicle rescheduling and manufacturing problems where timetabled tasks which have a fixed start and end location are to be carried out by a given number of servers. We test our approach on a number of instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. The numerical experiments show that the approach indeed finds schedules which are easier to adjust if it turns out that another scenario than the optimistic one is realized for the duration of the disruption.
Article
We focus our research on a supply chain involving one buyer and two independent suppliers of the same product. The main supplier is prone to supply disruption and recurrent supply uncertainty, and the backup supplier is perfectly reliable but supply goods at higher prices. Three kinds of backup contracts between the buyer and the backup supplier are investigated to mitigate supply risks: A capacity reservation contract, a make-to-order contract, and a buy-back contract. Models are developed to study how the buyer's expected profit and optimal decisions related to each contract change with the supply risks. We also examine the sensitivity of various cost parameters on the optimal decisions, and compare the values of three backup contracts for the buyer. Furthermore, we present how these results differ from those obtained in the analysis with demand uncertainty considered. Our study provides managerial insights into the positive effects of different backup contracts on the buyer's expected profit in the events of unexpected disruption.
Article
This paper proposes an integrated emergency ordering and production planning scheme for a multi-item, multi-product problem in which each product is composed of several ingredients. Each item can be supplied from both cheap unreliable suppliers prone to yield uncertainty and expensive reliable suppliers. A two-stage decision-making process is proposed in which orders are placed to the unreliable suppliers during the first stage and an emergency order can be placed in the second stage. In addition, a flexible backup ordering contract between the buyer and emergency supplier is proposed. A similar two-stage decision-making process is considered for production planning, where in the first stage, the main production plan is determined and in the second stage, the decision about a limited increase in the production plan is made as an emergency decision. An integrated ordering and production planning decision process is proposed for the problem. The value of emergency decisions, including the value of emergency ordering and the value of emergency production planning evaluates the effectiveness of the emergency decisions. Due to the staggering size of the problem, sample average approximation method is used to solve the problem.
Article
We consider an assembly system with a single end product and a general assembly structure, where one or more of the component suppliers or (sub)assembly production processes is subject to random supply disruptions. We present a method for reducing the system to an equivalent system with some subsystems replaced by a series structure. This reduction simplifies the computation of optimal ordering policies and can also allow for comparison of disruption impacts across systems with different supply chain structures. We identify conditions under which a state-dependent echelon base-stock policy is optimal. Based on this result, we propose a heuristic policy for solving the assembly system with disruptions and test its performance in numerical trials. Using additional numerical trials, we explore a variety of strategic questions. For example, contrary to what is typically observed in systems without disruptions, we find that choosing a supplier with a longer lead time can sometimes yield lower system costs. We also find that backup supply is more valuable for a supplier with a shorter lead time than one with a longer lead time. In addition, because of component complementarities, we find that choosing suppliers whose disruptions are perfectly correlated yields lower system costs than choosing suppliers whose disruptions are independent, in contrast to the strategy that is typically preferred when choosing backup suppliers for a single product. This paper was accepted by Gérard P. Cachon, operations management.
Article
This article considers a supply chain for a single product involving one retailer and two independent suppliers, when the main supplier might fail to supply the products, the backup supplier can always supply the products at a higher price. The retailer could use the backup supplier as a regular provider or a stand-by source by reserving some products at the supplier. A backup agreement with penalty scheme is constructed between the retailer and the backup supplier to mitigate the supply disruptions and the demand uncertainty. The expected profit functions and the optimal decisions of the two players are derived through a sequential optimisation process. Then, the sensitivity of two players’ expected profits to various input factors is examined through numerical examples. The impacts of the disruption probability and the demand uncertainty on the backup agreement are also investigated, which could provide guideline for how to use each sourcing method.
Article
The globalization of markets and geographic dispersion of production facilities, combined with a heavy outsourcing of supply chain processes, have substantially increased the exposure of supply chains to supply lead-times of long and uncertain nature. In this paper, we study the potential use of two contingency strategies on top of the conventionally used time buffer—statically planned safety lead-time (SL)—approach to deal with the lead-time uncertainty. These are (1) the ex-ante planning for disruption safety stock (DSS) to be released when a “disruption” (in this case, late delivery of the order) occurs; and (2) the ex-post dynamic emergency response (DER), which dynamically decides on the timing and size of an emergency order to be placed. Our work elaborates on the optimal parameter setting for these strategies, compares their added values when used to complement the traditional SL approach, and examines how the use of the contingency strategies affects the SL and corresponding cycle length of a periodic review system. Our research finds that: (1) the above contingency strategies reduce the reliance on the SL and are cost effective when the coefficient of variation (CV) of the uncertain lead-time is high; (2) it is important to re-optimize the SL to account for the contingency plans; and (3) re-optimization of the cycle length to account for the presence of the contingency responses, as opposed to using an EOQ-determined cycle length, does not significantly improve the cost performance. However, such re-optimization does well in the SL approach when the CV of the uncertain lead-time is high.
Article
We consider a continuous-review inventory model for a firm that faces deterministic demand but whose supplier experiences random disruptions. The supplier experiences "wet" and "dry" (operational and disrupted) periods whose durations are exponentially distributed. The firm follows an EOQ-like policy during wet periods but may not place orders during dry periods; any demands occurring during dry periods are lost if the firm does not have sufficient inventory to meet them. This paper introduces a simple but effective approximation for this model that maintains the tractabil-ity of the classical EOQ and permits analysis similar to that typically performed for the EOQ. We provide analytical and numerical bounds on the approximation error in both the cost function and the optimal order quantity. We prove that the optimal power-of-two policy has a worst-case error bound of 6%. Finally, we demonstrate numerically that the results proved for the approximate cost function hold, at least approximately, for the original exact function.
The value of flexible backup suppliers and disruption risk information: Newsvendor analysis with re-course
  • S Saghafian
  • M P Van Oyen