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The Importance of Decoupling Recurrent and Disruption Risks in a Supply Chain
Abstract
This paper focuses on the importance of decoupling recurrent supply risk and disruption risk when planning appropriate mitigation strategies. We show that bundling the two uncertainties leads a manager to underutilize a reliable source while over utilizing a cheaper but less reliable supplier. As in Dada et al. (working paper, University of Illinois, Champaign, IL, 2003), we show that increasing quantity from a cheaper but less reliable source is an effective risk mitigation strategy if most of the supply risk growth comes from an increase in recurrent uncertainty. In contrast, we show that a firm should order more from a reliable source and less from a cheaper but less reliable source if most of the supply risk growth comes from an increase in disruption probability. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007
... He showed that disruption mitigation is not possible only through inventory control; supplier's diversification is an effective mitigation strategy in that case too. In contrast to Tomlin's [35] model, Chopra et al. [12] considered two suppliers -one is unreliable due to both random yield and disruption uncertainty, and the other one is perfectly reliable. Their mitigation strategy was to reserve a quantity at the reliable supplier and exercise up to that reserved amount if the first supplier can't fulfill the demand due to random yield or supply disruption. ...
... The optimal decisions of the retailer and the manufacturer in the decentralized model under both revenue sharing contract and price-only contract are shown in Table 3. As mentioned by Chopra et al. [12], if the growth in supply risk comes from increased yield uncertainty of the expensive supplier then the best mitigation strategy is to increase the use of the cheaper supplier. From Table 3 we find that when the growth in supply risk occurs due to increase in yield risk at the expensive supplier, the Table 3. Optimal decisions and total expected profits under SRS and price-only contracts when yield variance σ z and probability of disruption (1 − α) vary. ...
... For a fixed value of σ z , we explore that as the disruption probability (1 − α) increases, the retailer orders less from the manufacturer to hedge the associated risk (as the chance of selling a product becomes lesser with higher uncertainty)under spanning revenue sharing contract. Table 3 illustrates that an increase in the cheaper supplier's disruption probability (1 − α) encourages the manufacturer to increase the use of the expensive supplier to mitigate disruption risk, which is consistent with the results given in [12]. We also examine the effect of (1 − α) on the supply chain's optimal expected profits as shown in Table 3. ...
This paper considers a newsvendor model for a single product to focus on the importance of coordination under demand and supply uncertainties where the raw materials are procured from two unreliable suppliers without any emergency resource; the main supplier (which is cheaper but more unreliable) is prone to random supply disruption and, therefore, it can satisfy all or nothing of the buyer's order, while the backup supplier (which is expensive but less unreliable) is prone to random yield and, therefore, can satisfy only a random fraction of the buyer's order. From the numerical results, we observe that it would be optimal to over-utilize the backup supplier and under-utilize the main supplier if the maximum growth in supply risk results from supply disruption. On the other hand, when the growth in supply risk occurs mainly due to increase in yield risk, the optimal risk mitigation strategy would be to increase the use of the backup supplier and decrease the use of the main supplier. We propose the price only contract and a new revenue sharing contract to mitigate demand and supply uncertainties in the decentralized model, and observe that the revenue sharing contract can fully coordinate the supply chain with win-win outcome for all entities involved in the supply chain.
... The second research stream that this study is pertinent to is contingent sourcing, which is one of the essential strategies used to mitigate supply disruption risks (Tomlin 2006). The literature on contingent sourcing can be categorised into single-period models (Chopra, Reinhardt, and Mohan 2007;Hou, Zeng, and Zhao 2010;Li, Wang, and Cheng 2010;Hou and Zhao 2012;Saghafian and Van Oyen 2012;Chen and Xiao 2014;Chen and Yang 2014;Giri and Bardhan 2015;Gupta, He, and Sethi 2015;Hou, Zeng, and Sun 2017;Köle and Bakal 2017;Chakraborty, Chauhan, and Ouhimmou 2020) and multiple-period models (Tomlin 2006;Chen, Zhao, and Zhou 2012;Schmitt and Snyder 2012;Qi 2013;Qi and Lee 2015). ...
... With regard to the single-period models under contingent sourcing, Chopra, Reinhardt, and Mohan (2007) studied a firm sourcing from the main supplier who is cheaper but is subject to both yield uncertainty and disruption risk, and the backup supplier who is more expensive but perfectly reliable. The firm reserves some capacity from the backup supplier in advance and then decides the order quantity after the main supplier's status is determined. ...
... Li, Wang, and Cheng (2010) examined a retailer sourcing from two unreliable suppliers and from the spot market, which is considered to be a perfectly reliable supplier. Similar to the firm's sourcing scheme in Chopra, Reinhardt, and Mohan (2007), the retailer can source from the spot market after both suppliers' statuses are determined. Subsequent studies (e.g. ...
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.
... The keywords were used to extract the articles related to CSRD. The initial article search also found that the general decoupling studies, for example, economic growth (McKinnon, 2007), stock repurchase (Westphal and Zajac, 2001) and risks decoupling (Chopra et al., 2007), remained researchers' focus until 2009. Studies began examining CSR along with decoupling after 2010 ( € Ahlstr€ om, 2010; Jamali, 2010). ...
... In the second step, the studies on CSR and CSR performance only were removed from the search, as they were not based on the decoupling of CSR. The search also uncovered decoupling articles written in the context of economic growth (McKinnon, 2007), stock repurchase (Westphal and Zajac, 2001) and risks (Chopra et al., 2007), excluding CSR. These articles were also excluded from our analysis. ...
Purpose
This paper aims to synthesize the corporate social responsibility decoupling (CSRD) literature, CSRD's causes and consequences and discuss other organizational attributes examined by CSRD scholars during 2010 and 2020. The authors provide suggestions for a future research agenda in this domain.
Design/methodology/approach
The authors' systematic literature review (SLR) uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to extract CSRD studies. The authors filter collected articles against quality and relevancy criteria and finally review 175 published articles.
Findings
A theme analysis identifies and structures the many themes related to CSRD. The authors discuss the drivers of CSRD and reveal the consequences companies face after CSRD. The authors also provide a comprehensive CSRD discussion in the context of developed and developing economies. CSR communication is also identified as a tool for decoupling and recoupling.
Research limitations/implications
The identified themes provide a thorough illustration of CSRD literature for new CSRD scholars. The authors also provide suggestions for future research, such as examining country-level policy-making and implications of CSRD variance and identifying cultural and economic hurdles to achieving core CSR purposes.
Practical implications
Policymakers and scholars may adopt the approach that CSRD is a misreporting of information similar to accounting fraud. This is particularly relevant given that an increasing number of CSRD scandals indicate that the purpose of bringing change through corporate CSR has not been adopted well by corporations.
Originality/value
The authors' study offers a comprehensive literature review for the period of 2010–2020. The studies identified are structured into meaningful themes which can provide groundwork for future researchers.
... Uncertainty and risks impact both supply chain design and supply chain planning decisions. Recurrent or operational risks and disruptive risks (Tang, 2006;Chopra et al., 2007;Tsai, 2016;Ivanov, 2017;Rezapour et al., 2017) are typically involved in those considerations. Demand and lead-time uncertainty risks are frequently considered operational risks (Kleindorfer and Saad, 2005;Chopra et al., 2007;Acar et al., 2010;Georgiadis et al., 2011;Hora and Klassen, 2013;Meisel and Bierwirth, 2014). ...
... Recurrent or operational risks and disruptive risks (Tang, 2006;Chopra et al., 2007;Tsai, 2016;Ivanov, 2017;Rezapour et al., 2017) are typically involved in those considerations. Demand and lead-time uncertainty risks are frequently considered operational risks (Kleindorfer and Saad, 2005;Chopra et al., 2007;Acar et al., 2010;Georgiadis et al., 2011;Hora and Klassen, 2013;Meisel and Bierwirth, 2014). ...
Purpose
This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.
Design/methodology/approach
The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.
Findings
Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.
Originality/value
This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.
... To deal with such a situation, producers often use a secondary market as an emergency resource to satisfy the unmet demand, and also for salvaging the leftover products. Chopra et al. (2007) reported an incident where a fire took place at the Philips microchip plant in Albuquerque, NM in March 2000 which supplied chips to both Nokia and Ericsson, among whom only Nokia got rid of the shortages in supply with the help of its multi-tiered supplier strategy to obtain chips from other sources. However, the availability of emergence resource in every stage of supply chain is a simplified assumption, particularly when it comes to mitigating demand of the final product of a branded company with specific configuration and features. ...
... Kazaz (2004) examined production planning and resource ordering for an olive oil producer who experiences both uncertain demand as well as random yield in production, and showed that the optimal amount of production decreases under the presence of an emergency resource, where both the sales price and the purchasing cost depend on production yield. Chopra et al. (2007) developed a single-period model with dual sourcing to integrate two types of supply uncertainty-supply disruption and random yield. Arcelus et al. (2008) developed a newsvendor model where the manufacturer shares the risk of demand uncertainty with the retailer by offering buyback contract, and mitigated his own risk by the availability of the secondary resource. ...
One of the major objectives of modern supply chain management is dealing with the negative impact of decentralization among the involved entities and minimizing double marginalization effect within the chain, especially when the end-customers’ demand is not deterministic. This paper investigates coordination issue in a three-level supply chain with one raw-material supplier, one manufacturer, and one retailer. The retailer determines the retail price, sales effort, and order quantity simultaneously before the selling season starts. Both the supplier and the manufacturer face random yield in production. A composite contract having two components—a contingent buyback with target sales rebate and penalty between the retailer and the manufacturer, and a revenue sharing contract between the manufacturer and the supplier is proposed. The proposed composite contract is shown to achieve supply chain coordination and allows arbitrary allocation of total channel profit among all the chain members. The impact of randomness in both demand and production, and the impact of non-existence of emergency resource for the final product on the performance of the entire supply chain are analyzed. A numerical example is provided to illustrate the developed model and draw some important managerial insights.
... L'identification des risques (Kleindorfer et Saad, 2005 ;Wieland et Wallenburg, 2012 ;Fan and Stevenson, 2018) L'évaluation des risques (Zsidisin et al., 2004 ;Kleindorfer et Saad, 2005 ;Wieland and Wallenburg, 2012) L'atténuation des risques (Chopra et al., 2007 ;Manuj and Mentzer, 2008 ;Wagner and Bode, 2008 ;Azadegan et al., 2020) Le contrôle des risques (Berg et al., 2008 ;Manuj and Mentzer, 2008 ;Wieland and Wallenburg, 2012). ...
... Supply chain management is the cross-department and cross-enterprise integration and coordination of material, information, and capital flow to convert and utilize supply chain resources most sensibly across the entire value chain, from upstream to downstream members (Ivanov et al., 2017). Many risks and disruptions can affect supply chains since they are complex coordinated networks functioning in uncertain contexts (Chopra et al., 2007;Simchi-Levi et al., 2015). Many methods exist for assessing the performance of a supply chain network in the face of disruptions. ...
Disruptions in the supply chain have become more frequent in recent years. The unpredictability and pressure of existing problems are also growing. Dealing with unforeseen events necessitates considering the possibility of supply chain disruption. This paper proposes a strategic model to aid supply chain managers and practitioners in system planning in advance of unforeseen events. First, this study develops a Discrete-time Markov Chain model and defines the steady-state probabilities that measure each state’s likelihood over time. The expected disruption costs are then considered in the steady-state probabilities. This assists in estimating the costs of each state’s and the system’s disruption. In addition, a sensitivity analysis is performed to examine the effects of transition rates on the expected disruption cost for each of the considered groups and the expected disruption cost for the system overall.
... The basic idea is simple-unit cost falls with cumulative production experience (Fine and Porteus 1989, Mazzola and McCardle 1997, Li and Rajagopalan 1998, Gray et al. 2009, Li et al. 2015, Li 2019. From the operations perspective, the literature has studied how supplier learning can help to (i) reduce production cost (Corbett et al. 2005, Bernstein and Kök 2009, Iida 2012, Kim and Netessine 2013, Li 2013, (ii) improve the product quality (Baiman et al. 2000, Balachandran and Radhakrishnan 2005, Chao et al. 2009, Hsieh and Liu 2010, Babich and Tang 2012, Nikoofal and Gümüş 2018, Quigley et al. 2018, Zhang et al. 2019, Chen et al. 2022, and (iii) increase the process reliability (Chopra et al. 2007, Wang et al. 2010, Tang et al. 2013, Nikoofal and Gümüş 2019. ...
Problem definition: We study a procurement problem, where the supplier holds superior cost information and can learn to improve efficiency over time. Despite its prevalence, the supply chain literature provides limited guidance on how to manage learning suppliers with evolving private information. Methodology/results: We use mechanism design. We show that supplier learning has both efficiency and agency effects, it can induce countervailing incentives, and the agency effect can overwhelm the efficiency effect. As a result, (i) supplier learning can hurt profits, (ii) information asymmetry can improve efficiency, (iii) production distortion can go upward, and (iv) ignoring the agency effect of learning can mislead contract design and inflict severe losses. Managerial implications: Our results suggest that previous studies may have overlooked the downside of learning and overestimated the harm of information asymmetry. Moreover, our results help explain when and why firms should overproduce output and disclose private information voluntarily. By highlighting the strategic role of supplier learning, this study sharpens our understanding of supply chain management.
Funding: L. Gao is partly supported by the CoR research grant at University of California, Riverside. W. Zhang is partly supported by the National Natural Science Foundation of China [Grant 71821002].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0285 .
... Maintaining a backup facility to cope with disruptions is one of the most popular strategies being discussed in the extant literature and also one of the most practised strategies (Chopra, Reinhardt, and Mohan 2007;Dada, Petruzzi, and Schwarz 2007;Yin and Wang, 2017). Tang (2006) focuses on maintaining redundant inventories in warehouses and postponing the demands for disruption management. ...
The ripple effect refers to disruption propagation across the supply network affecting its global performance. To cope with it, supply networks should be resilient. This study investigates the drivers of supply network resilience, viewed as adaptive capacity to disruptions, focusing on trust and investigating the moderating role of network topology on the relationship between trust and resilience. We first develop an NK agent-based model of the supply network to simulate resilient performance. Then, a simulation analysis is carried out, to assess the effect of trust on the resilience of supply networks displaying different complex topologies. Our results confirm that trust positively affects supply network resilience; however, across the different topologies, the beneficial effect of trust varies. In particular, we find that trust is beneficial at most for the following topologies: local, small-world, block-diagonal, and random. For centralised, diagonal, and hierarchical topologies improving trust increases resilience at a moderate level. We also find that, as the frequency of disruptions rises, the positive effect of trust on resilience decreases. Managerial implications of the main findings are finally discussed. ARTICLE HISTORY
... They include physically moving products from their place of production to where they are needed, adding value to each shipment in terms of bulk breaking, place, and time (Lambert et al., 1998). Transportation is the most significant area of logistics, as well as having a major impact on the level of customer service and cost structure (Chopra et al., 2007), especially due to the risk of seizure by the police (EuropaPress, 2021;El País, 2021). Transport plays a critical role, connecting producers with drug dealers in consumer markets and ensuring hashish reaches its destination, aiming at minimizing seizing risks and costs. ...
Despite the social, health, law enforcement, and economic importance of illegal drug supply, the lack of information and understanding regarding these supply chains stands out. This paper carries out a disaggregated analysis of the structure of the hashish supply chain from Morocco to Europe to explain the value contributions at each level, the end-price formation, and the supply chain management practices. The methodology adopted is based on a mixed method of data collection where the primary data are gathered from field interviews with cannabis producers and dealers and secondary information is obtained from official statistics, research papers, informational reports, and documentaries. We review supply and value chain frameworks through the lens of cost–benefit analysis. Our main findings show an unequal contribution on the part of the different levels of distribution, with end-user prices increasing by 7000% of the cost of production during the supply chain. The chain also has high variable costs but limited fixed ones, exacerbating the lack of stability and fostering continuous adaptation. We also detect a reluctance to raise end-user prices but a great propensity to change quality. This research may have implications for several stakeholders. In the case of dealers, we find that they have created a supply-push system thanks to their dominant power, leaning on information sharing as a source of resilience. In the case of law enforcement, we delve into the operational functioning of the drug chain and the reasons for its survival. For financial investigation operations, unknown or unrealized economic parameters are quantified. For development agencies, the need to implement alternative development programs for producers is evidenced. Finally, for health authorities, we highlight the consequences of seizures and prohibitions of hashish trafficking on the deterioration of the quality of hashish and the subsequently added health hazards for end-users.
... They used simulations to demonstrate that the optimal strategy to cope with the impact of uncertainty will be different depending on the type of uncertainty. Chopra et al. (2007) separated supply uncertainty into two sub-types; disruption uncertainty, related to unpredictable events causing shortage and stopping operations, and recurrent uncertainty, where the effect is less severe but always present. They also concluded that each subtype should be dealt with independently to effectively reduce uncertainty. ...
Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This paper proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.
... However, as we explain in the end of Section 4 , their arguments for characterizing the optimal solution are incomplete. To the best of our knowledge, all other relevant models in the literature, that is, models that employ the backup strategy used in this paper, deal mainly with primary suppliers that are subject to random disruptions and/or random yield (see Tomlin, 2009;Saghafian & Van Oyen, 2012;Huang & Xu, 2015;Köle & Bakal, 2017 for random disruptions models, Giri, 2011 for a model with a random yield supplier, and Chopra, Reinhardt, & Mohan, 2007;Guo, Zhao, & Xu, 2016;Giri & Bardhan, 2015;Hou, Zeng, & Sun, 2017 for suppliers that may experience both disruptions and random yield). There also exist models in the literature that are related to ours in the sense that they involve suppliers with random capacity and some form of capacity reservation ( Jain & Hazra, 2016;Serel, 2007;2017 ). ...
We consider newsvendor models with a primary supplier whose production is subject to random capacity. In order to hedge against this supply risk the retailer contracts with a reliable backup supplier to reserve capacity in advance, acquiring the option to use it after the delivery from the primary supplier and either before or after the demand realization. For both cases we identify the conditions under which the use of the backup supplier is beneficial and characterize the optimal order and reservation quantities. Furthermore, we examine how these optimal procurement quantities are affected by various model parameters.
... Natural disasters and human-made accidents have increased in high, medium, and low-income countries during the past decades (Coleman, 2006). Natural disasters, terrorism, and other unpredictable events increase the risk uncertainties global supply chains face (Brown et al., 2006;Chopra et al., 2007;Stewart, 1995). Table 1 provides existing literature and findings on risk and Supply chain integration. ...
Globalization created complex supply chain networks that rely heavily on superior performance and efficient operational systems. Global supply chains require integrated information flow across boundaries for the smooth movement of goods and services. The risks in SCM are due to vulnerabilities and uncertainties at the operational levels in the global supply chain, posing a challenge to the rapidly evolving logistics industry. This chapter focuses on the existing literature on supply chain risk management to identify strategies to minimize these risks due to the competitive business environment by implementing risk mitigation, identification, and assessment in the supply chain networks. The main contribution of this chapter is the discussion on the existing literature on risks associated with supply chain integration and perceptions towards different types of supply chain risks
... .] In other words, Beijing is guaranteeing its self-declared right to cyber sovereignty, a concept that is still contested within the international community (Chopra et al., 2007;Patterson, 2011;Iasiello, 2017;Yang and Xu, 2018;Yu, 2017). Another attractive solution China has found to this issue is monitoring communications network platforms communication for malicious activity to achieve improved cybersecurity (Hayden et al., 2004;Nathan, 2012). ...
Purpose
The contribution of this study aims to twofold: First, it provides an overview of the current state of research on cyberattacks on Chinese supply chains (SCs). Second, it offers a look at the Chinese Government’s approach to fighting cyberattacks on Chinese SCs and its calls for global governance.
Design/methodology/approach
A comprehensive literature review was conducted on Clarivate Analytics’ Web of Science, in Social Sciences Citation Index journals, Scopus and Google Scholar, published between 2010–2021. A systematic review of practitioner literature was also conducted.
Findings
Chinese SCs have become a matter of national security, especially in the era of cyber warfare. The risks to SC have been outlined. Cybersecurity regulations are increasing as China aims to build a robust environment for cyberspace development. Using the Technology-organization-environment (TOE) framework, the results show that the top five factors influencing the adoption process in firms are as follows: relative advantage and technological readiness (Technology context); top management support and firm size (Organization context) and government policy and regulations (Environment context).
Research limitations/implications
This review focuses on cyberattacks on Chinese SCs and great care was taken when selecting search terms. However, the author acknowledges that the choice of databases/terms may have excluded a few articles on cyberattacks from this review.
Practical implications
This review provides managerial insights for SC practitioners into how cyberattacks have the potential to disrupt the global SC network.
Originality/value
Past researchers proposed a taxonomic approach to evaluate progress with SC integration into Industry 4.0; in contrast, this study is one of the first steps toward an enhanced understanding of cyberattacks on Chinese SCs and their contribution to the global SC network using the TOE framework.
... Tomlin (2006) assumes that the volume-flexible capacity cannot be available instantaneously; however, the reliable supplier can provide this capacity after a response time with an increase in the unit cost. Chopra, Reinhardt, and Mohan (2007) develop a model for a single-period inventory problem in which a company places orders to a supplier subject to yield uncertainty and disruptions. Meanwhile, the company can also buy capacity from an expensive reliable supplier for a fixed cost. ...
Supply chains are exposed to different risks, which can be mitigated by various strategies based on the characteristics and needs of companies. In collaboration with Ford, we develop a decision support framework to choose the best mitigation strategy against supply disruption risk, especially for companies operating with a small supplier base and low inventory levels. Our framework is based on a multistage stochastic programming model which incorporates a variety of plausible strategies, including reserving backup capacity from the primary supplier, reserving capacity from a secondary supplier, and holding backup inventory. We reflect disruption risk into the framework through decision makers’ input on the time to recover and the disruption probability. Our results demonstrate that relying on the strategy which is optimal when there is no disruption risk can increase the expected total cost substantially in the presence of disruption risk. However, this increase can be reduced significantly by investing in the mitigation strategy recommended by our framework. Our results also show that this framework removes the burden of estimating the time to recover and the disruption probability precisely since there is often a small loss associated with using another strategy that is optimal in the neighbourhood of the estimated values.
... They show how multiple decentralized stocking locations can be beneficial to minimize the risk of supply uncertainty. Chopra et al. (2007) Meena et al. (2011) analyze the problem of selecting the optimal number of suppliers considering different probabilities for the disruption risks, different capacity, and the presence of compensation for suppliers who do not fail. Meena and Sarmah (2013) develop a mixed-integer nonlinear programming model to determine the optimal ordering policy for multiple sourcing under different types of disruption risks. ...
... He shows that reserve capacity is preferred over RMI as a risk mitigation strategy if disruptions are rare but long, whereas RMI is preferred if disruptions are frequent but short. In a similar setting, Chopra et al. (2007) explore how to best use reserve capacity to deal with disruption risk and recurrent risk (demand uncertainty). Dong and Tomlin (2012) study the interplay between reserve capacity and business insurances. ...
We offer the notion of “commons” at different levels—within company, private across company, and government‐sponsored across‐industry sectors—and discuss how the creation of such commons enabled firms to be both efficient during normal times and resilient against the disruptions resulting from COVID‐19. At the same time, there are many proven strategies providing resilience in supply chains. For instance, companies that used multiple channels to improve efficiency when facing day‐to‐day demand‐and‐supply variations found that the structure also offered resilience without additional cost when COVID struck. We discuss how the presence of commons lowers the cost for firms to adopt such resilience‐building supply chain strategies. We discuss factors that impact the creation of these commons and conclude with a number of questions to guide further research into the role of industry commons in facilitating supply chain resilience.
... In addition, as supply chains become global, external factors, in addition to internal factors, increasingly influence supplier risks [14]. The risks of supplier selection can be grouped into recurring risks if risk events are frequent but short, and risks of interruption if risk events are rare but long [24,25]. ...
Supplier risks have attracted significant attention in the supply chain risk management literature. In this article, we propose a new computational system based on the 'Fuzzy Extended Analytic Hierarchy Process (FEAHP)' method for supplier selection while considering the relevant risks. We sought to evaluate the opportunities and limitations of using the FEAHP method in supplier selection and analyzed the support of the system developed through the real case of a Brazilian oil and natural gas company. The computational approach based on FEAHP automates supplier selection by determining a hierarchy of criteria, sub-criteria, and alternatives. First, the criteria and sub-criteria specific to the selection problem were identified by the experts taking the relevant literature as a starting point. Next, the experts performed a pair-wise comparison of the predefined requirements using a linguistic scale. This evaluation was then quantified by calculating the priority weights of criteria, sub-criteria, and alternatives. The best decision alternative is the one with the highest final score. Sensitivity analysis was performed to verify the results of the proposed model. The FEAHP computer approach automated the supplier selection process in a rational, flexible, and agile way, as perceived by the focal company. From this, we hypothesized that using this system can provide helpful insights in choosing the best suppliers in an environment of risk and uncertainty, thereby maximizing supply chain performance.
... Maintaining a backup facility to cope with disruptions is one of the most popular strategies being discussed in the extant literature and also one of the most practised strategies (Chopra, Reinhardt, and Mohan 2007;Dada, Petruzzi, and Schwarz 2007;Yin and Wang, 2017). Tang (2006) focuses on maintaining redundant inventories in warehouses and postponing the demands for disruption management. ...
The ripple effect refers to disruption propagation across the supply network affecting its global performance. To cope with it, supply networks should be resilient. This study investigates the drivers of supply network resilience, viewed as adaptive capacity to disruptions, focusing on trust and investigating the moderating role of network topology on the relationship between trust and resilience. We first develop an NK agent-based model of the supply network to simulate resilient performance. Then, a simulation analysis is carried out, to assess the effect of trust on the resilience of supply networks displaying different complex topologies. Our results confirm that trust positively affects supply network resilience; however, across the different topologies, the beneficial effect of trust varies. In particular, we find that trust is beneficial at most for the following topologies: local, small-world, block-diagonal, and random. For centralized, diagonal, and hierarchical topologies improving trust increases resilience at a moderate level. We also find that, as the frequency of disruptions rises, the positive effect of trust on resilience decreases. Managerial implications of the main findings are finally discussed.
This chapter is devoted to the principles and applications of supply chain analytics to resilience analysis and stress testing the supply networks. First, we learn the major terminology, principles, and methods of supply chain resilience analytics. Subsequently, we consider a case study for supply chain resilience analysis using anyLogistix software. We conclude the chapter with an introduction to digital supply chain twins using the example of anyLogistix and resilience modeling.
Problem definition: Dual sourcing and contingent sourcing are important risk-mitigation strategies to manage supply chain risks, including transportation-related losses of inbound orders. Contingent sourcing as a means of managing transportation risk is made possible by shipment information realized at in-transit inspection points or through shipment monitoring technologies. We examine the impact of contingent sourcing and shipment information in a setting where a buyer can source from two competing suppliers. One supplier (unreliable) has a long transportation lead time and is prone to in-transit yield loss; the other supplier (reliable) has a short, but nonzero, lead time with no yield loss. Methodology/results: We analyze a multistage game-theoretical model in which the two suppliers compete on wholesale prices and then, the buyer determines initial order quantities. Later, the buyer can place an emergency order with the reliable supplier based on shipment information, which reveals (possibly imperfectly) the status of the in-transit order from the unreliable supplier. We show that the buyer will adopt one of four possible sourcing strategies: (1) initially source only from the unreliable supplier but resort to the reliable supplier contingent on the updated shipment information, (2) diversify its initial order across the two suppliers but resort to the reliable supplier if needed, (3) diversify its initial order and not engage in contingent sourcing, or (4) sole source from the reliable supplier. Interestingly, contingent sourcing may or may not benefit the buyer because it may soften the competition between suppliers. Moreover, the buyer’s profit may not be monotonic in the accuracy of shipment information. Managerial implications: The buyer must design its supply base so that the unreliable supplier is particularly cost efficient if the buyer is to benefit from the possibility of contingent sourcing. The buyer may not always benefit from operational improvements that enhance shipment information accuracy because they may soften supplier competition.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0540 .
This paper investigates a supply chain governed by a flat penalty service‐level contract in which missing the target fill rate can lead to costly operational disruption. We focus on near‐miss bias: (1) the preference for near‐miss events, that is, risky production quantities that reach the target but narrowly avoid disruption; and (2) riskier decision‐making due to such preferences. We propose a reference‐dependent behavioral model that explains the near‐miss bias. The findings of a laboratory experiment show that production quantities are evaluated based on realized profits and are below the optimal model prediction. Contracts associated with lower perceived severity, that is, the ratio of flat penalty to wholesale price, result in lower production quantities than those with higher perceived severity, even though the standard model does not predict any effect. A structural estimation analysis indicates that the behavioral model performs better than the standard model in terms of predictive accuracy and goodness of fit. Our analysis provides insights for managers who design supply chain contracts in settings with considerable risk of disruption due to a shortage of critical parts.
In the face of the complex market environment and the in-depth development of globalization, ensuring the supply chain's stability and cost leadership by developing a differentiated dual-source procurement strategy is extremely important. The present study contemplates the supply chain as the research object. It also considers disruption uncertainty and decision-makers’ risk attitudes. To minimize costs and shortages, we build a dual-objective robust optimization model for a dual-sourcing strategy under interruption risk. The authors aim to explore efficient and robust planning under supply disruption uncertainty in dual-sourcing strategies by different risk attitudes. The research employs the augmented ε-constraint method to solve the proposed model by transforming the model into a single-objective mixed-integer programming model. Additionally, our study uses enterprise examples to prove the feasibility and effectiveness of the model. Moreover, it conducts risk attitude analysis, dual objective function trade-off analysis, and superiority analysis of procurement strategies. Finally, we put forward some management insights to provide a new decision-making idea for real supply chain enterprises in the case of supply interruption risk.
Supply chain disruptions pose significant risks to production systems. Unfortunately, the number of publications that study the real-life impacts of supply chain disruptions on production systems is extremely small, and the question of how these disruptions affect production systems has not been fully investigated. This research seeks to fill this gap using assembly-line data from a vehicle manufacturer in China. With these data, the authors estimated econometric models to assess the impacts of minor disruptions on production using two different metrics: daily output in terms of number of vehicles manufactured, and the disruption duration in minutes. Time-dependent effects were also considered to assess the extent to which the effects of disruptions on daily output and disruption duration change over time. The resulting models were analysed to identify which disruption factors are the most impactful in terms of daily output and disruption duration.
The flows of data and methods for their revision are different and depend on the needs of the transport business. Various parameters are communicated and regularly transmitted during cargo delivery. The set of data is compiled for each point in time and includes all the information and review of big data for making decisions about the transportation of goods.The author divided the description into two parts. The beginning of the chapter is devoted to the collection of data from various sources, and the second part is devoted to the revision of data sets. Finally, the chapter ends with issues of trust and confidentiality in cases of data analysis.KeywordsData collectionData analysisGrubb testBig dataOperational data revisionConflicts resolutionDecision hierarchyTrust and privacy data review
Researchers pay more attention to the process of managing the delivery of goods. An overview of the latest scientific articles is presented to identify optimization methods applied in the process of cargo delivery management and analyse the risks covered. The chapter also gives a review of the application of different decision support techniques including several attributes in studies on the transportation of freights. A recent analysis of cargo delivery management includes some metaheuristic algorithms. However, the research potential on the topic is quite high. Research in the literature shows that research in mathematics and science involves a variety of methods. Next, the methods involved in the process are explained and a set of metaheuristic techniques capable to improve these processes is proposed. In the chapter, the application of algorithms: genetic, evolutionary, and other which are used to optimize transportation processes is demonstrated.KeywordsMicro-risksCause-and-effect modellingMulti-attribute methods of decision supportThe process of cargo delivery managementMetaheuristics for optimization
This chapter aims to analyze the efficiency and productivity performance of all the standalone health insurance companies for the period 2014-15 to 2018-19 using slacks-based measure of data envelopment analysis based on secondary data collected from the Insurance Regulatory and Development Authority’s annual reports. The study brings into light the operating characteristics, efficiencies and productivity of the Indian standalone health insurance companies during the period 2015-2019 and therefore holds important insights for policy makers and practitioners as well as for the decision makers. This study has found that on constant returns to scale, the average efficiency of standalone health insurers was 64.78% and there was a 30.19% variation in the efficiency levels during the study period. Excessive operating expenses, Commission expenses and equity capital were found the main reasons for inefficiency. The results further indicated that the total factor productivity increased at an average rate of 19.11% per year during the entire study period. On average, this improvement was ascribed to the technological improvement of 12.11% and efficiency improvement of 6.19% per year during the study period.
This article examines disruption risks at fulfillment centers and develops risk mitigation strategies based on inventory stocking and delivery decisions. It considers a Fortune 150 firm whose delivery operations are designed to fulfill the orders from contracted business customers within the next day. The firm promises its customers that the probability of late deliveries exceeding a certain threshold will be limited. We coin this requirement as the service‐at‐risk (SaR) constraint. The firm proactively determines the inventory amount to be kept in each fulfillment center. If a disruption occurs, the firm determines the best way to deliver orders from its operational fulfillment centers and vendors under disruption length and demand uncertainty to minimize additional costs and satisfy the SaR constraint. This article makes four main contributions. First, we find a surprising result that total inventory commitment can decrease with risk aversion when there exists a disruption possibility that impacts two nearby facilities together. Using actual data from the motivating firm, the numerical analysis demonstrates that this phenomenon exists in practice. Second, we define a new metric: The Risk Dispersion Index (RDI), which measures the dispersion in risk exposure across fulfillment centers. It leads to a lower and more balanced risk exposure in the firm's delivery operations. Third, we find that a facility may elect to abandon its own customers to serve the customers of a disrupted facility; this behavior becomes more prominent under risk aversion. Fourth, the introduction of demand uncertainty leads to a smaller inventory commitment for a risk‐neutral retailer.
The global selection of production sites is a very complex task of great strategic importance for Original Equipment Manufacturers (OEMs), not only to ensure their sustained competitiveness, but also due to the sizeable long-term investment associated with a production site. With this in mind, this work develops a process model with which OEMs can select the most appropriate production site for their specific production activity in practice. Based on a literature analysis, the process model is developed by determining all necessary preparation, by defining the properties of the selection process model, providing all necessary instructions for choosing and evaluating location factors, and by laying out the procedure of the selection process model. Moreover, the selection process model includes a discussion of location factors which are possibly relevant for OEMs when selecting a production site. This discussion contains a description and, if relevant, a macroeconomic analysis of each location factor, an explanation of their relevance for constructing and operating a production site, additional information for choosing relevant location factors, and information and instructions on evaluating them in the selection process model. To be successfully applicable, the selection process model is developed based on the assumption that the production site must not be selected in isolation, but as part of the global production network and supply chain of the OEM and, additionally, to advance the OEM’s related strategic goals. Furthermore, the selection process model is developed on the premise that a purely quantitative model cannot realistically solve an OEM’s complex selection of a production site, that the realistic analysis of the conditions at potential production sites requires evaluating the changes of these conditions over the planning horizon of the production site and that the future development of many of these conditions can only be assessed with uncertainty.
Given the requirements of international businesses, this research addresses the supply chain network design problem in the real-world situation by considering four critical features: sustainability, resiliency, responsiveness, and globalization. For this purpose, a multi-objective mathematical model is proposed that minimizes the environmental impacts and the total costs and maximizes the social impacts while considering the resilience and responsiveness of the global supply chain. Then, the modified fuzzy robust stochastic method is employed to tackle the uncertainty. This study selects one of the most important medical devices during the pandemic (COVID-19) namely the blood bank refrigerator as a case study. Afterwards, the proposed multi-objective model is solved by developing a novel method named as augmented lexicographic weighted Tchebycheff method. Based on the obtained results, an increase in the responsiveness level of the supply chain can lead to increasing the sustainability dimensions, including job opportunities, safety, carbon emission, and economic aspects. Moreover, an increase in demands harms the economic, environmental, and responsiveness targets. The demand has a pivotal role in selecting resilience strategy, as well.
Purpose
Due to uncertainty in supply chains caused by the coronavirus disease 2019 (COVID-19), organizations are adjusting their supply chain design to address challenges faced during the pandemic. To safeguard their operations against disruption in order quantities, supply chain members have been looking for alternate suppliers. This paper considers a two-level supply chain consisting of a manufacturer and two suppliers of a certain type of components required for the production of a finished product. The primary supplier (supplier A) is unreliable, in the sense that the quantity delivered is usually less than the ordered quantity. The proportion of the ordered quantity delivered by supplier A is a random variable with a known probability distribution. The secondary supplier (supplier B) always delivers the order in its entirety at a higher cost and can respond instantaneously. In order for supplier B to respond instantaneously, the manufacturer is required to reserve a certain quantity at an additional cost. Once the quantity received from the main supplier is observed, the manufacturer may place an order not exceeding the reserved quantity.
Design/methodology/approach
A mathematical model describing the production/inventory situation of the supply chain is formulated. The model allows the determination of the manufacturer's optimal ordering policy.
Findings
An expression for the expected total cost per unit time function is derived. The optimal solution is determined by solving a system of nonlinear equations obtained by minimizing the expected total cost function.
Practical implications
The proposed model can be used by supply chain managers aiming at identifying various ways of handling the uncertainty in the flow of supplies across the chain.
Originality/value
This proposed model addresses a gap in the production/inventory literature.
En dépit de leur caractère distribué, les chaînes logistiques peuvent se révéler très performantes dans les conditions idéales de production et d’échange. Toutefois, leur complexité les rend de plus en plus fragiles. Cette thèse propose des modèles et des méthodes pour l’analyse des risques, de façon à renforcer la robustesse et la résilience des chaînes logistiques. Pour nous aider à mieux positionner nos travaux et à tirer les caractéristiques essentielles des chaînes logistiques, nous avons analysé ce domaine suivant une démarche ontologique à l’aide de la méthode KOD. En nous appuyant sur un état de l’art du domaine des risques dans les chaînes logistiques, et sur les bases de cas réels, nous avons identifié les indicateurs des vulnérabilités les plus significatifs. A partir des connaissances extraites, et des modèles mathématiques proposés dans la littérature, nous avons construit un modèle de chaîne logistique multi-étages à l’aide de modèles ARIMA intégrant l’aspect aléatoire de la demande. Pour adapter ce modèle aux situations de vulnérabilité et de risques, nous avons ajouté des contraintes de capacité et de positivité sur les commandes et sur les stocks. Sous l’effet d’événements dangereux, certaines contraintes du système peuvent être atteintes et par conséquence, son évolution peut s’écarter fortement de la dynamique nominale. Nous avons proposé des indicateurs de vulnérabilités comme des indicateurs de fréquence des retards de livraison, ou de surcoût d’immobilisation de produits. Enfin, l’occurrence d’événements dangereux a été représentée par des scénarios. Nous avons alors obtenu des résultats de simulation sous MATLAB, qui nous ont permis d’évaluer leurs conséquences pour différentes configurations du système, en particulier sous perturbation des flux d’informations (demande) et des flux physique (qualité de produits approvisionnés).
The study of supply chain (SC) resilience as a research perspective is in an incipient state. Nevertheless, there is a tremendous amount of literature concerning SCs under risk and uncertainty. This paper presents a review of the quantitative models for SC resilience using bibliometric and network analyses. The study identified 3672 articles and provided statistical measurements of science, scientists, and scientific activities. Additionally, the analysis highlights the inter-temporal dimensions of decision making and classifies articles based on their usability in real-world applications. Systematic mapping using co-citation and the PageRank algorithm resulted in seven key research themes, and a microlevel analysis of these themes provides prospective research directions. This involved examining the contributions of individual articles with respect to their scope, value proposition, risk-type consideration, methodology and technique used, and their industry applications. The thematic analysis and extensive future research directions leverage the insights and potential of this review article.
Supply chain literature has amply explored the effect of different resilience strategies in the face of supply chain disruptions. Firms often apply a multitude of resilience strategies in tandem. Such strategies can vary from slack inventory to volume flexibility and responsiveness to backup capacity. Yet, there is a lack of empirical or analytical evidence in how the combination of resilience strategies affects firm capabilities in the face of supply chain disruptions. In this paper, we use simulation modelling techniques to determine the effect of different combinations of resilience strategies in a systematic and stepwise manner. Our modelling considers different disruption attributes (capacity or delay), their effect (severity and likelihood), and their origin (i.e. upstream or downstream). A number of interesting observations are made. First, combining resilience strategies is not always beneficial and can occasionally have detrimental effects. Moreover, resilience strategies that are beneficial at the node (firm) level may prove ineffective at the system level. We also find that inventory and volume flexibility are strategies that combine well with others. The study offers several contributions to research and management in supply chain disruption and the study of resilience.
This chapter is devoted to risk management in the supply chain. The chapter starts with fundamental definitions of uncertainty and risks. Subsequently, risk classifications are presented. Decisions in the scope of supply chain risk management are discussed. Reasons for different supply chain risks and the mitigation strategies are shown. The special focus is directed towards the disruption risks and the ripple effect in supply chains. We explain how to measure the exposure of a supply chain to the ripple effect. Finally, supply chain resilience and viability concepts are presented.
This paper develops an integrated methodology aimed at diagnosing supply chain resilience in terms of (1) internal dynamic capabilities of an enterprise, and (2) resilience of its suppliers. In addition, unlike other research, it integrates the suppliers’ resilience evaluation into the order size allocation plan. Multi-attribute decision making (MADM) algorithms were employed to quantify the relative importance to evaluate the internal and external resilience of an enterprise. Furthermore, the MADM output was combined with a multi-objective programming model formulated to solve the order size problem considering economic and resilience objectives. The applicability of the developed methodology is demonstrated via a dairy manufacturing enterprise that suffered from disruptions attributed to COVID-19. The results translate the enterprise’s non-viable manufacturing due to its poor external and internal resilience profiles. It is emphasized that if an enterprise fails to develop internal capabilities such as readiness and sensing, the enterprise could also fail in managing external resilience. A resilient supply chain requires a blend of internal and external resilience. This work represents the first quantitative attempt to provide a unified methodology for identifying and measuring internal and external resilience.
Purpose
The purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products.
Design/methodology/approach
The environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis.
Findings
The adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies.
Practical implications
The extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system.
Social implications
The integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.
Originality/value
Previous research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.
In this paper, we consider a closed-loop supply chain (CLSC) consisting of two suppliers, one manufacturer, one risk-averse retailer and one fair-caring third-party in the presence of supply disruption. We focus on establishing a dynamic Stackelberg game model with bounded rational expectation and analyzing the game evolution process. The effects of key parameters on the Nash equilibrium solutions and their stability are investigated, as well as the complex dynamical behaviors of the CLSC system are explored by using the stability region, bifurcation graph, the largest Lyapunov exponent (LLE), strange attractors, etc. Moreover, the performance of channel members under different values of parameters is researched by utilizing the (average) expected profits or utilities index. The analysis results reveal that the excessive fast adjustment speed of the manufacturer will lead to the system losing stability and falling into chaos. Also, the retailer’s risk aversion and the third party’s fairness concerns have a destabilization effect on the Nash equilibrium point, while the possibility of supply disruption has different effects on the scope of the adjustment speed of decision variables of the manufacturer. Furthermore, in most cases, an over the top adjustment speed of the manufacturer is disadvantageous to all the channel members for more expected profits, but the third-party can achieve a better performance when the system is in periodic state. Finally, the time-delay feedback control method is proposed to eliminate the system chaos.
Manufacturers will adopt different strategies to deal with the occurrence of supply disruptions. In this paper, we conduct an analytical game-theoretical study to consider a supply chain with two suppliers (a reliable supplier and an unreliable supplier) and two manufacturers (manufacturers 1 and 2). Manufacturer 2 only orders products from the reliable supplier (Supplier R), while Manufacturer 1 can choose one of the two purchasing strategies: ‘contingent dual sourcing’ or ‘stable dual sourcing’. We study the optimal purchasing strategy of Manufacturer 1 and Supplier R under supply disruptions and competition. We find that Manufacturer 1 prefers the ‘stable dual sourcing’ strategy only if the proportion of order quantity from the unreliable supplier and the potential market size are relatively large, and Supplier R prefers the ‘contingent dual sourcing’ strategy only if the proportion of order quantity from the unreliable supplier is large, the potential market size and disruption probability is not too large.
This chapter is devoted to risk management in the supply chain. The chapter starts with fundamental definitions of uncertainty and risks. Subsequently, disruption risk classifications are presented. Decisions in the scope of supply chain disruption risk management are discussed. The special focus is directed toward disruption risks and the ripple effect in supply chains. The concepts of disruption overlays and disruption tails are explained. Finally, pandemics as a specific type of supply chain disruptions (i.e., a super disruption) are considered using the example of the coronavirus (COVID-19/SARS-CoV-2) outbreak.
Micro, Small, and Medium Enterprises are quite significant in maintaining an
appreciable growth rate and in generating employment opportunities. They have
played a great role in ensuring socialistic goals like income equality, poverty
eradication, employment generation, and balanced regional development. It
contributed much to the industrial development in rural areas. On this consideration,
the study has been taken to analyze the growth and status of the MSMEs sector in the
Udupi district of Karnataka state. The data for the present study has been collected
from various secondary sources. This study focuses on principal characteristics of
MSMEs number of units, employment, and investments are analyzed.
This paper reviews the literature on inventory models with multiple sourcing options and presents a typology for classification. By means of the classification, the progression of the literature (policies and modeling assumptions) is illustrated, the main decision trade-offs in multiple sourcing are identified and avenues for future research are pointed out. Multiple sourcing decision models trade off the added costs of backup sourcing against higher inventory or shortage costs under single sourcing. The value of multiple over single sourcing is found to increase in the uncertainty to be buffered, in inventory holding and shortage costs, as well as in the constraints of the primary source. The literature evolved from small, restrictive models to larger problems and more realism. Accordingly, replenishment policies progressed from optimal policies to more heuristic decision rules. Three areas for future research are suggested for moving the field forward and towards more practical applicability. (1) Further integration of model aspects such as the extension of replenishment policies to more than two suppliers and to multi-echelon models. (2) Focusing on supply chain resilience with decision making disruption events or demand spikes under consideration of risk preferences. (3) Utilizing industry data in machine learning and data-driven methodologies.
We focus on the optimal use of risk mitigation inventory (RMI) and reserve capacity to manage disruption risk in serial multi‐stage supply chains where product transformation occurs at each stage.We find that under reasonable conditions it is better to hold more RMI downstream than upstream even when the upstream holding costs are lower. We also find that it is often optimal to hold more reserve capacity downstream than upstream. While in one‐stage supply chains RMI and reserve capacity always behave as substitutes, it turns out that in multi‐stage serial supply chains the interplay between RMI and reserve capacity is more nuanced. We find that echelon RMI and reserve capacity at each stage are substitutes. In contrast, RMI at a stage complements reserve capacity at the adjacent downstream stage.
Natural disasters, labor disputes, terrorism and more mundane risks can seriously disrupt or delay the flow of material, information and cash through an organization's supply chain. The authors assert that how well a company fares against such threats will depend on its level of preparedness, and the type of disruption. Each supply-chain risk - to forecasts, information systems, intellectual property, procurement, inventory and capacity - has its own drivers and effective mitigation strategies. To avoid lost sales, increased costs or both, managers need to tailor proven risk-reduction strategies to their organizations. Managing supply-chain risk is difficult, however. Dell, Toyota, Motorola and other leading manufacturers excel at identifying and neutralizing supply-chain risks through a delicate balancing act: keeping inventory, capacity and related elements at appropriate levels across the entire supply chain in a rapidly changing environment. Organizations can prepare for or avoid delays by "smart sizing" their capacity and inventory. The manager serves as a kind of financial portfolio manager, seeking to achieve the highest achievable profits (reward) for varying levels of supply-chain risk. The authors recommend a powerful what if? team exercise called stress testing to identify potentially weak links in the supply chain. Armed with this shared understanding, companies can then select the best mitigation strategy: holding "reserves," pooling inventory, using redundant suppliers, balancing capacity and inventory, implementing robust backup and recovery systems, adjusting pricing and incentives, bringing or keeping production in-house, and using Continuous Replenishment Programs (CRP), Collaborative Planning, Forecasting and Replenishment (CPFR) and other supply-chain initiatives.
Today's marketplace is characterised by turbulence and uncertainty. Market turbulence has tended to increase for a number of reasons. Demand in almost every industrial sector seems to be more volatile than was the case in the past. Product and technology life-cycles have shortened significantly and competitive product introductions make life-cycle demand difficult to predict. At the same time the vulnerability of supply chains to disturbance or disruption has increased. It is not only the effect of external events such as wars, strikes or terrorist attacks, but also the impact of changes in business strategy. Many companies have experienced a change in their supply chain risk profile as a result of changes in their business models, for example the adoption of “lean” practices, the move to outsourcing and a general tendency to reduce the size of the supplier base. This paper suggests that one key element in any strategy designed to mitigate supply chain risk is improved “end-to-end” visibility. It is argued that supply chain “confidence” will increase in proportion to the quality of supply chain information.
On the morning of September 11th, 2001, the United States and the Western world entered into a new era - one in which large scale terrorist acts are to be expected. The impacts of the new era will challenge supply chain managers to adjust relations with suppliers and customers, contend with transportation difficulties and amend inventory management strategies. This paper looks at the twin corporate challenges of (i) preparing to deal with the aftermath of terrorist attacks and (ii) operating under heightened security. The first challenge involves setting certain operational redundancies. The second means less reliable lead times and less certain demand scenarios. In addition, the paper looks at how companies should organize to meet those challenges efficiently and suggests a new public-private partnership. While the paper is focused on the US, it has worldwide implications.
In this paper we develop an approximate model of an inventory control system in which there exist two options for resupply, with one having a shorter lead time. Because the optimal policy appears to be extremely complex, we consider a reasonable extension of the standard (Q, R) policy to allow for two different lot sizes Q<sub>1</sub> and Q<sub>2</sub>, and two different reorder levels, R<sub>1</sub> and R<sub>2</sub>. Expressions for the expected on hand inventory and the expected backorders are developed and a procedure for determining the policy parameters is given. The model is validated by simulation, and calculations are included which compare the average annual cost with and without emergency ordering.
The purpose of this paper is to investigate an one-supplier–one-retailer supply chain that experiences a disruption in demand during the planning horizon. While demand uncertainty has long been a central research issue in supply chain management, little attention has been given to disruptions once the production plan has been made. In this paper, we show that changes to the original plan induced by a disruption may impose considerable deviation costs throughout the system. One of our general goals is to analyze these costs.When the production plan and the supply chain coordination scheme are designed in a static manner, as is most often the case, both will have to be adjusted under a disruption scenario. Using wholesale quantity discount policies, we derive conditions under which the supply chain can be coordinated so that the maximum potential profit is realized. Our results are applicable for both centralized and decentralized decision-making.
We study a single-product setting in which a firm can source from two suppliers, one that is unreliable and another that is reliable but more expensive. Suppliers are capacity constrained, but the reliable supplier may possess volume flexibility. We prove that in the special case in which the reliable supplier has no flexibility and the unreliable supplier has infinite capacity, a risk-neutral firm will pursue a single disruption-management strategy: mitigation by carrying inventory, mitigation by single-sourcing from the reliable supplier, or passive acceptance. We find that a supplier’s percentage uptime and the nature of the disruptions (frequent but short versus rare but long) are key determinants of the optimal strategy. For a given percentage uptime, sourcing mitigation is increasingly favored over inventory mitigation as disruptions become less frequent but longer. Further, we show that a mixed mitigation strategy (partial sourcing from the reliable supplier and carrying inventory) can be optimal if the unreliable supplier has finite capacity or if the firm is risk averse.
Contingent rerouting is a possible tactic if the reliable supplier can ramp up its processing capacity, that is, if it has volume flexibility. We find that contingent rerouting is often a component of the optimal disruption-management strategy, and that it can significantly reduce the firm’s costs. For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare.
How can you plan for every crisis that might occur, even for ones you can't imagine? The task seems so daunting and so limitless that many firms don't even start. In fact, as the authors' 20 years of research shows, three out of four Fortune 500 companies are prepared to handle only the types of calamities they've already suffered, and not even all of those. That's unfortunate because the research also shows that crisis-prepared companies fare better financially, have stronger reputations, and ultimately stay in business longer than their crisis prone counterparts. Crisis-prepared companies use a systematic approach to focus their efforts. In addition to planning for natural disasters, they divide man-made calamities into two sorts--accidental or "normal" ones, like the Exxon Valdez oil spill, and deliberate or "abnormal" ones, like product tampering. Then they take steps to broaden their thinking about such potential crises. They consider threats that would be common in other industries, for instance. And they seek input from outsiders such as investigative journalists and even reformed criminals. But if these companies think broadly about possible threats, they think narrowly about implementation. Each year, smart companies focus their resources and attention on a few facilities picked at random, just as airlines conduct detailed security checks on just a few passengers for each flight. That reduces the probability of an attack on the entire organization even as it allows the business to migrate steadily to a higher level of crisis readiness. Crisis-prepared companies know that disasters cannot be managed through cost-benefit analyses. It is precisely because the effects of a disaster cannot be predicted or controlled that smart companies focus their efforts on preventing crises rather than containing them after the fact.
We consider the problem of a newsvendor that is served by multiple suppliers, where any given supplier may be unreliable. By unreliable we simply mean that the marginal amount received from a supplier is no more than, and typically is less than, the marginal amount ordered from the supplier. In this setting, the newsvendor needs to determine (1) whether or not to place an order with a given supplier, and (2) if so, then for how much? To address these questions, we develop a general framework in which the newsvendor can diversify its risk of inadequate delivery amounts by spreading its orders among any number and combination of available suppliers that differ in terms of cost and (delivery) reliability. Ultimately, we find that the newsvendor model with unreliable suppliers has the same structural properties as a newsvendor model in which all suppliers are reliable but have limited capacity. Our resulting contribution is two?fold: First, we establish properties of the optimal solution and develop corresponding insights into the trade?off between cost and reliability. Second, we perform comparative statics on the optimal solution, with a particular emphasis on investigating how changes in suppliers cost or reliability affect the newsvendor's ordering decisions and customer service level.
We study a single-product setting in which a firm can source from two suppliers, one that is unreliable and another that is reliable but more expensive. Suppliers are capacity constrained, but the reliable supplier may possess volume flexibility. We prove that in the special case in which the reliable supplier has no flexibility and the unreliable supplier has infinite capacity, a risk-neutral firm will pursue a single disruption-management strategy: mitigation by carrying inventory, mitigation by single-sourcing from the reliable supplier, or passive acceptance. We find that a supplier's percentage uptime and the nature of the disruptions (frequent but short versus rare but long) are key determinants of the optimal strategy. For a given percentage uptime, sourcing mitigation is increasingly favored over inventory mitigation as disruptions become less frequent but longer. Further, we show that a mixed mitigation strategy (partial sourcing from the reliable supplier and carrying inventory) can be optimal if the unreliable supplier has finite capacity or if the firm is risk averse. Contingent rerouting is a possible tactic if the reliable supplier can ramp up its processing capacity, that is, if it has volume flexibility. We find that contingent rerouting is often a component of the optimal disruption-management strategy, and that it can significantly reduce the firm's costs. For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare.
There are two broad categories of risk affecting supply chain design and management: (1) risks arising from the problems of coordinating supply and demand, and (2) risks arising from disruptions to normal activities. This paper is concerned with the second category of risks, which may arise from natural disasters, from strikes and economic disruptions, and from acts of purposeful agents, including terrorists. The paper provides a conceptual framework that reflects the joint activities of risk assessment and risk mitigation that are fundamental to disruption risk management in supply chains. We then consider empirical results from a rich data set covering the period 1995-2000 on accidents in the U.S. Chemical Industry. Based on these results and other literature, we discuss the implications for the design of management systems intended to cope with supply chain disruption risks.
The problem of determining the optimal ordering policies under stochastic demand is examined when two supply options, air and surface, are available, with different costs and different delivery times. Assuming mild conditions on the holding-penalty cost functions, linear ordering costs and backlogging, some sufficient conditions are obtained to show when it is optimal to order nothing by air and others to show when it is optimal to order nothing by surface. Explicit formulas are derived for the optimal orders in the case when air delivery time is kappa periods and surface delivery time is kappa plus 1 periods, respectively, under more general conditions than before.
This paper reviews the literature on quantitatively-oriented approaches for determining lot sizes when production or procurement yields are random. We discuss issues related to the modelling of costs, yield uncertainty, and performance in the context of systems with random yields. We provide a review of the existing literature, concentrating on descriptions of the types of problems that have been solved and important structural results. We identity a variety of shortcomings of the literature in addressing problems encountered in practice, and suggest directions for future research.
Existing production/inventory models with random (variable) yield take the yield distribution as given. This work takes a step towards selecting the optimal yield randomness, jointly with lot sizing decisions. First, we analyze an EOQ model where yield variance and lot size are to be selected simultaneously. Two different cost structures are considered. Secondly, we consider source diversification (‘second sourcing’) as a means of reducing effective yield randomness, and trade its benefits against its costs. Conditions for the superiority of diversification between two sources with distinct yield distributions over a single source are derived. The optimal number of identical sources is also analyzed. Some comments on the congruence of the results with recent JIT practices are provided.
The aim of the paper is to find optimum policies in some n-stage and infinite-stage inventory models. The following cases are considered: 1) the delivery lag is a random variable with two values 0 and 1; 2) the inventories have a high rate of obsolence and can be used only for one period; 3) there are two kinds of shipments, one a routine shipment with a one-period delivery lag and the other a priority shipment without any lag but at a higher price. If all the costs are directly proportional to the amount of the commodity and the unsatisfied demands can be backlogged and satisfied when the commodity becomes available, the optimum policies are those with constant levels. If in the case I the ordering cost is a convex function, the optimum policy is a $(s,S)$ policy.
Purchasing organizations are exposed to risk in their interactions with suppliers, whether it is recognized and managed, addressed in a cursory manner, or altogether ignored. In order to understand the supply risk that exists, purchasing organizations can proactively assess the probability and impact of supply risk in advance, or reactively discover risk after a detrimental event occurs. The purpose of this study is to explore, analyze, and derive common themes on supply risk assessment techniques. Findings from this research indicate that purchasing organizations can assess supply risk with techniques that focus on addressing supplier quality issues, improving supplier processes, and reducing the likelihood of supply disruptions. From an agency theory perspective, these risk assessment techniques facilitate the obtaining of information by purchasing organizations to verify supplier behaviors, promoting goal congruence between buying and selling firms, and reducing outcome uncertainty associated with inbound supply.
There are two broad categories of risk affecting supply chain design and management: (1) risks arising from the problems of coordinating supply and demand, and (2) risks arising from disruptions to normal activities. This paper is concerned with the second category of risks, which may arise from natural disasters, from strikes and economic disruptions, and from acts of purposeful agents, including terrorists. The paper provides a conceptual framework that reflects the joint activities of risk assessment and risk mitigation that are fundamental to disruption risk management in supply chains. We then consider empirical results from a rich data set covering the period 1995–2000 on accidents in the U. S. Chemical Industry. Based on these results and other literature, we discuss the implications for the design of management systems intended to cope with supply chain disruption risks.
Supply chain management is becoming an increasingly important issue, especially when in most industries the cost of materials purchased comprises 40-60% of the total sales revenue. Despite the benefits cited for single sourcing in the popular literature, there is enough evidence of industries having two/three sources for most parts. In this paper we address the operational issue of quantity allocation between two uncertain suppliers and its effects on the inventory policies of the buyer. Based on the type of delivery contract a buyer has with the suppliers, we suggest three models for the supply process. Model I is a one-delivery contract with all of the order quantity delivered either in the current period with probability beta, or in the next period with probability 1 - beta. Model II is also a one-delivery contract with a random fraction of the order quantity delivered in the current period; the portion of the order quantity not delivered is cancelled. Model III is similar to Model Il with the remaining quantity delivered in the next period. We derive the optimal ordering policies that minimize the total ordering, holding and penalty costs with backlogging. We show that the optimal ordering policy in period n for each of these models is as follows: for x greater-than-or-equal-to u(n)BAR order nothing; for v(n)BAR less-than-or-equal-to X < u(n)BAR, use only one supplier; and for x < v(n)BAR, order from both suppliers. For the limiting case in the single period version of Model I, we derive conditions under which one would continue ordering from one or the other or both suppliers. For Model II, we give, sufficient conditions for not using the second (more expensive) supplier when the demand and yield distributions have some special form. For the single period version of Models II and III with equal marginal ordering costs we show that the optimal order quantities follow a ratio rule when demand is exponential and yields are either normal or gamma distributed.
When supply lead times are uncertain, the simultaneous procurement from two sources offers savings in inventory holding and shortage costs. Economies are achieved if these savings outweigh the increase in ordering costs. In this paper we analyze dual sourcing in the context of the "reorder point, order quantity" inventory model with constant demand and stochastic lead times and compare it with single sourcing. Two cases are studied, using the uniform and the exponential distributions, which may be thought of as two extreme ways of representing stochastic lead times. In our two-vendor model, the order quantity is split equally between the two vendors and the split orders are placed simultaneously when the inventory position reaches the reorder level. A comparison of the total expected costs suggests that when the uncertainty in the lead times is high and the ordering costs are low, dual sourcing could be cost effective.
This paper considers a continuous-review stochastic inventory problem with random demand and random lead-time where supply may be disrupted due to machine breakdowns, strikes or other randomly occurring events. The supplier availability is modelled as a semi-Markov process (more specifically, as an alternating renewal process). The standard (q, r) policy is used when the supplier is available (ON), i.e., when the inventory position reaches the reorder point r, q units are ordered to raise the inventory position to the target level of R = q + r. The form of the policy changes when the supplier becomes unavailable (OFF) in which case orders cannot be placed when the reorder point r is reached. However, as soon as the supplier becomes available again one orders enough to bring the inventory position up to the target level of R. The regenerative cycles are identified by observing the inventory position process. We construct the average cost per time objective function using the renewal reward theorem. It is assumed that the duration of the ON period is Ek (i.e., k-stage Erlangian) and the OFF period is general. In analogy with queuing notation we call this an Ek/G system. By employing the ‘method of stages’, we obtain a problem with a larger state space for the ON/OFF stochastic process; but the resulting ON process can now be analyzed using Markovian techniques. For asymptotic values of q, the objective function assumes a particularly simple form which is shown to be convex under mild restrictions on the density functions of demand. Numerical examples illustrate the results.
In this paper we consider the case of random yield and diversification in two different inventory models. It is assumed that two sources (suppliers) exist who ship an amount which is a random function of the amount requisitioned. Since they may charge different prices and their reliability (in terms of the variance of the yield random variable) may be different, diversification may be more desirable than using a single supplier. We analyze this problem for both the EOQ model and the newsboy model. Analytic results for the optimal order quantities and the minimum cost are obtained for the EOQ model. Convexity of the objective function is also discussed. We develop the necessary conditions for the newsboy model and propose an approximate solution technique. The concavity of the expected profit function is shown. Numerical examples are provided for both models.
This paper considers a stochastic inventory model where the quantity ordered sometimes may not be available due to strikes, etc. We represent the supplier's availability process as a two-state continuous time Markov chain where one state corresponds to availability and the other state corresponds to unavailability of the supplier. The problem is to determine the reorder point, the order quantity when the system is found in ON state, and how long to wait before the next order if system is in OFF state. It is assumed that the state of the system is identified at a cost. Using the renewal reward theorem we construct the objective function as the long-run average cost. Numerical . sensitivity analysis results are provided. We also analyze the problem when the yield (i.e., amount received, if available) is random, and discuss an example with Beta distributed yield.
We develop an optimal inventory policy for EOQ inventory systems which may have a disruption in either supply or demand. The start of the disruption is known a priori and it lasts a random length of time. We describe the structure of an optimal policy and present a procedure for its computation, along with numerical results.
We consider the case of a first-time interaction between a buyer and a supplier who is unreliable in delivery. The supplier declares her estimate of the ability to meet the order obligations, but the buyer may have a different estimate, which may be higher or lower than the supplier's estimate. We derive the Nash bargaining solution and discuss the role of using a down-payment or nondelivery penalty in the contract. For the case of buyer overtrust, the down-payment contract maximizes channel profits when the supplier's estimate is public information. If the supplier's estimate is private information, a nonsymmetric contract is shown to be efficient and incentive compatible. For the case of buyer undertrust, the contract structure is quite different as both players choose not to include down-payments in the contract. When delivery estimates are public information, a nondelivery penalty contract is able to maximize channel profits if the buyer uses the supplier's estimate in making the ordering decision. If estimates are private information, channel profits are maximized only if the true estimates of both players are not far part. We also discuss the effect of different risk profiles on the nature of the bargaining solution. In three extensions of the model, we consider the following variants of the basic problem. First, we analyze the effect of early versus late negotiation on the bargaining solution. Then, we study the case of endogenous supply reliability, and finally, for the case of repeated interactions, we discuss the impact of updating delivery estimates on the order quantity and negotiated prices of future orders.
In this paper, we develop a framework to classify supply chain risk management problems and approaches for the solution of these problems. We argue that risk management problems need to be handled at three levels strategic, operational and tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions and disasters. To handle unforeseen events in the supply chain there are two obvious approaches: (1) to design chains with built in risk-tolerance and (2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and also the associated consequences and impacts from these events. We can then focus our efforts on mapping out the propagation of events in the supply chain due to supplier non-performance, and employ our insight to develop two mathematical programming based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design.
In this paper, we develop a framework to classify supply chain risk-management problems and approaches for the solution of these problems. We argue that risk-management problems need to be handled at three levels: 1) strategic, 2) operational, and 3) tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions, and disasters. To handle unforeseen events in the supply chain, there are two obvious approaches: 1) to design chains with built-in risk tolerance and 2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and the associated consequences and impacts from these events. Having described these approaches, we then focus our efforts on mapping out the propagation of events in the supply chain due to supplier nonperformance, and employ our insight to develop two mathematical programming-based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design
Flu vaccine policy becomes issue for Bush
New York Times, Flu vaccine policy becomes issue for Bush,
New York Times, October 20, 2004.
Can suppliers bring down your firm? Sunday Times
Sunday Times, Can suppliers bring down your firm? Sunday
Times, November 23, 2003.
Viswanadham A conceptual and analytical framework for the management of risk in supply chains IEEE Trans Automation Syst Eng submitted
- R Gaonkar