Contingent sourcing under supply disruption and competition

With the increasing awareness of the serious consequences of supply disruption risk, firms adopt various kinds of strategies to mitigate it. We consider a supply chain in which two suppliers sell components to two competing manufacturers producing and selling substitutable products. Supplier U is unreliable and cheap, while Supplier R is reliable and expensive. Firm C uses a contingent dual-sourcing strategy and Firm S uses a single-sourcing strategy. We study the implications of the contingent sourcing strategy under competition and in the presence of a possible supply disruption. The time of the occurrence of the supply disruption is uncertain and exogenous, but the procurement time of components is in the control of the firms. We show that supply disruption and procurement times jointly impact the firms’ buying decisions. We characterise the firms’ optimal order quantities and their expected profits under different cases. Subsequently, through numerical computations, we obtain additional managerial insights. Finally, as extensions, we study the impact endogenizing equilibrium sourcing strategies of asymmetric and symmetric firms, and of capacity reservation by Firm C with Supplier R to mitigate disruption.     

Blockchain adoption or contingent sourcing? Advancing food supply chain resilience in the post-pandemic era

In the post-pandemic era, food supply chains and firms therein are facing unprecedented severe challenges, because once infection is detected, numerous products must be recalled or abandoned, and both suppliers and retailers in the supply chain suffer enormous loss. To survive under the pandemic, retailers have adopted different sourcing strategies, such as contingent sourcing, which, in turn, affect the upstream suppliers and hence the resilience of the whole supply chain. With the rapid development of digital technologies, retailers nowadays can utilize blockchain as a reliable and efficient way to reduce product risk and hence advance the resilience of food supply chains by improving product traceability and inspection accuracy, and making sourcing transparent. In this paper, we develop a game-theoretic model to investigate the interrelation between the retailer’s decisions on blockchain adoption and sourcing strategies. We consider that a retailer originally orders from a risky supplier while conducting an imperfect inspection to detect infected products before selling. The retailer may speculatively keep on ordering from the risky supplier or adopt contingent sourcing by ordering from an alternative safe supplier. The retailer also has an option to implement blockchain to improve the inspection accuracy and product traceability. We derive the optimal retail prices under different sourcing strategies with and without blockchain adoption and then analyze the incentives for sourcing strategy and blockchain adoption. Then, we identify the conditions of an all-win situation for food retailer, supplier, supply chain resilience, and consumers with/without government subsidy. Finally, we extend to consider the situation that some consumers have health-safety concerns and preferences for blockchain adoption.     

New flexibility drivers for manufacturing,supply chain and service operations

Increasing product proliferation, customisation, competition and customer expectations, as well as supply side disruptions, pose significant challenges to firm operations. Such challenges require improved efficiency and resilience in manufacturing, service and supply chain systems. New and innovative flexibility concepts and models offer a prospective route to such operational improvements. Several emerging issues in flexibility, such as risk and uncertainty management, environmental sustainability, optimal strategies under competition, optimal operations with strategic consumer behaviours are being examined in this regard. This overview provides a concise review of these critical research issues, and discusses related papers featured in this special issue. Four major flexibility drivers are classified: disruption risks, resilience and the ripple effect in the supply chain; digitalisation, smart operations and e-supply chains; sustainability and closed-loop supply chains; and supplier integration and behavioural flexibility.     

Integrated production–distribution planning in two-echelon systems: a resilience view

Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage’s objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.     

Revealing interfaces of supply chain resilience and sustainability: a simulation study

Dynamics of structures and processes is one of the underlying challenges in supply chain management, where multiple dimensions of economic efficiency, risk management and sustainability are interconnected. One of the substantiated issues in supply chain dynamics is resilience. Resilience has a number of intersections with supply chain sustainability. This paper aims at analysing disruption propagation in the supply chain with consideration of sustainability factors in order to design resilient supply chain structure in regard to ripple effect mitigation and sustainability increase. Ripple effect in the supply chain occurs if a disruption at a supplier cannot be localised and cascades downstream impacting supply chain performance. This simulation-based study helps to identify what sustainability factors mitigate the ripple effect in the supply chain and what sustainability factors enhance this effect. The results indicate that (i) sustainable single sourcing enhances the ripple effect; (ii) facility fortification at major employers in regions mitigates the ripple effect and enhances sustainability; and (iii) a reduction in storage facilities in the supply chain downstream of a disruption-risky facility increases sustainability but causes the ripple effect.     

Retailer’s sourcing strategy under consumer stockpiling in anticipation of supply disruptions

We study a retailer’s sourcing strategy under consumers’ stockpiling behaviour and the factors associated with the selection of an optimal strategy in multi-tier supply chains in the presence of supply disruption risk. Stockpiling behaviour occurs when consumers attempt to mitigate the negative impact of a supply shortage. We prove that those behaviours become stronger if consumers have experienced similar problems before and weaker as more inventories are hoarded. Based on these findings, numerical analysis is carried out to compare the superiority of single sourcing versus dual sourcing from retailer’s perspective. Our results suggest that the superiority is highly dependent on factors such as supplier’s volume flexibility, retailer’s purchasing price, and supplier reliability.     

Structural-aware simulation analysis of supply chain resilience

Supply chain resilience (SCRES) refers to the ability of a supply chain (SC) to both resist disruptions and recover its operational capability after disruptions. This paper presents a simulation model that includes network structural properties in the analysis of SCRES. This simulation model extends an existing graph model to consider operational behaviours in order to capture disruption-recovery dynamics. Through structural analysis of a supply chain network (SCN), mitigation strategies are designed to build redundancy, while contingency strategies are developed to prioritise recovery of the affected SCN. SCRES indexes are proposed by sampling SC performance measures of disruption for each plant and aggregating the measures based on the criticality of the plants in the SCN. The applicability of this simulation model is demonstrated in a real-world case study of different disruption scenarios. The application of mitigation and contingency strategies is shown to both improve recovery and reduce the total costs associated with disruptions. Through such simulation-based analysis, firms can gain insight into the SCRES of their existing SCNs and identify suitable strategies to improve SCRES by considering recovery time and costs.     

Literature review on disruption recovery in the supply chain*

Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no survey on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim of relating the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold: operations and supply chain managers can observe which quantitative tools are available for different application areas; on the other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified.     

A two-period model for selection of resilient multi-tier supply portfolio

A two-period decision-making model is developed for selection of resilient supply portfolio in a multi-tier supply chain under disruption risks. The planning horizon is divided into two aggregate periods: before and after the disruption. The resilience of the supply chain is achieved by selection ahead of time primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously, recovery and transshipment portfolios are determined for each disruption scenario and decisions on usage the pre-positioned inventory are made to minimise expected cost or maximise expected service level. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. The findings also indicate that the developed portfolio approach with an embedded network flow structure leads to computationally efficient stochastic mixed integer program with a strong LP relaxation.     

Extracting supply chain maps from news articles using deep neural networks

Supply chains are increasingly global, complex and multi-tiered. Consequently, companies often struggle to maintain complete visibility of their supply network. This poses a problem as visibility of the network structure is required for tasks like effectively managing supply chain risk. In this paper, we discuss automated supply chain mapping as a means of maintaining structural visibility of a company's supply chain, and we use Deep Learning to automatically extract buyer–supplier relations from natural language text. Early results show that supply chain mapping solutions using Natural Language Processing and Deep Learning could enable companies to (a) automatically generate rudimentary supply chain maps, (b) verify existing supply chain maps, or (c) augment existing maps with additional supplier information.     

A portfolio approach to supply chain disruption management

A new, computationally efficient portfolio approach to supplier selection in the presence of supply chain disruption risks is proposed, where the selection of supply portfolios for parts is combined with production scheduling of finished products. Unlike most of reported research on the supply chain risk management which focuses on the risk mitigation decisions taken prior to a disruption, the proposed portfolio approach combines decisions made before, during and after the disruption. The two decision-making approaches are considered: an integrated approach with the perfect information about the future disruption scenarios, and a hierarchical approach with no such information available. In the integrated approach, which accounts for all potential disruption scenarios, the primary supply portfolio that will hedge against all scenarios is determined along with the recovery supply portfolio and production schedule for each scenario. In the hierarchical approach, first the primary supply portfolio is determined, and then, when a primary supplier is hit by a disruption, the recovery supply portfolio is selected. For the integrated and the hierarchical decision-making, mixed integer programming models are developed with the two risk-neutral conflicting objectives that account for both time and cost of recovery: minimising expected cost or maximising expected service level. The findings indicate that for both objectives, the integrated decision-making selects a more diversified primary supply portfolio than the hierarchical approach and when all primary suppliers are shutdown by disruption, a single sourcing recovery portfolio is usually selected.     

‘A blessing in disguise’ or ‘as if it wasn’t hard enough already’: reciprocal and aggravate vulnerabilities in the supply chain

We investigate the interrelations of structural and operational vulnerabilities in the supply chain (SC) using discrete-event simulation for a real life case study. We theorise a notion of SC overlays and explore conditions surrounding their appearance. Such overlays occur if the negative consequences of changes in a SC structure as a result of a disruption are either amplified or mitigated by changes in the operational environment. We hypothesise that these overlays can be both reciprocal (i.e. complementary or mitigating) and aggravate (i.e. concurrent or enhancing). Our approach can be used for an efficient management of SC resilience capabilities by varying their levels over time. We show different ripple and bullwhip effect profiles, which lead to either reciprocal or aggravate overlays, and then we develop recommendations on the overlay-driven dynamic variation of resilience capability levels in order to enhance both SC resilience and efficiency through dynamic redundancy allocation. The results can be of value in selecting and deploying operational policies at the right time and scale during and after the recovery periods. Restricting analysis to the disruption period only and ignoring operational dynamics after capacity recovery can result in misleading or inefficient SC resilience and recovery policies.     

Information management strategies and supply chain performance under demand disruptions

The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency–response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.     

Random yield and coordination mechanisms of a supply chain with emergency backup sourcing

This paper considers a supply chain in which a buyer purchases finished items from a contracting supplier to satisfy a stochastic market demand, where the supplier’s production is subject to random yield. We assume that the buyer can make up the shortage by sourcing from an emergency backup supplier. We develop two Stackelberg game models, i.e. buyer-Stackelberg (BS) model and supplier-Stackelberg (SS) model, and find that the decentralised BS model results in a higher stocking factor of supplier’s input than the decentralised SS model. Compared with BS model, the buyer in SS model performs more explicit order plan, and we find that only when the actual yield of the supplier is insufficient, the buyer would use emergency backup sourcing to make up the shortage. When the manufacturing operation of the supplier is in the good state, the buyer only orders a certain amount and has some leftover. When the actual yield of the supplier is moderate, the buyer uses up every item produced from the supplier regardless of the yield rate. Comparing both channel structures, SS operation is a more effective way of controlling both inventory cost and backup sourcing cost, and it can be beneficial for each player as well as for the whole channel. Finally, we develop the coordination mechanism for each channel to investigate the issues of risk handling and risk sharing for uncertain demand and uncertain yield.     

The impact of supply chain disruption on the closed-loop supply chain configuration profit: a study of sourcing policies

A closed-loop supply chain configuration (CLSCC) encompasses the decisions related to the optimal selection of options at each stage of a closed-loop supply chain (CLSC) for the introduction and reconstruction of new products. The extant literature ignores the impact of supply chain disruptions on CLSCC. An attempt is made to fill this gap in this study. Thus, an integrated multi-sourcing CLSCC optimisation model for new and reconstructed products is developed. The optimisation model presented is a mixed-integer nonlinear programming (MINLP) model. Based on a real-world case study of an auto-parts manufacturer in India presented, a comprehensive set of computational experiments, scenario analyses are conducted. The key finding/observation that resulted from our computational experiments is that multi-sourcing generates higher net present value of total profit compared to single sourcing under the risk of supply chain disruption. Several other observations and managerial insights are drawn from computational experiments, and scenario analyses. Firms interested in configuring their CLSC under the risk of supply chain disruption may use the study's outcomes to understand the profit impact of various CLSCC parameters, individually and in combination.     

Managing supply disruption through procurement strategy and price competition

When facing supply disruptions, the emergency procurement strategy and the optimal allocation procurement strategy are widely used strategies to manage supply risks. In this paper, buyers use these types of procurement strategies under the threat of supply disruption and engage in price competition. The structural properties of the procurement strategies are characterised by their reliability thresholds. We find that reliability thresholds play a critical role in buyer procurement strategy choices, which are related to the sales price, underage cost and differentials in unit procurement cost. A solution procedure is proposed to determine the equilibrium strategy profile. The effects of reliability levels and costs on the equilibrium prices, expected profits and equilibrium strategy profiles are explored. We extend the basic model to investigate the case of symmetric competition where buyers can freely choose their procurement strategy. The results show that in most cases, the competing buyers will choose the same strategy, whether an optimal allocation strategy with single sourcing or an emergency procurement strategy with dual sourcing. In a special parameter setting, the buyers will choose either strategy because they yield identical expected profits; this leads to multiple equilibria. We also find the equilibrium to be Pareto efficient.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.     

供应中断风险下供应链恢复能力投资决策及协调

摘要： 〖HTSS〗为了减缓供应商生产运营中断对制造商带来的供应中断风险,制造商需要在突发事件前优化供应链恢复能力投资水平。引入CVaR来刻画供应链在突发事件下的应急目标,建立了一定置信水平控制下的供应链恢复能力投资的决策模型,进而揭示了制造商风险规避程度对供应链最优恢复能力投资水平的影响。分权供应链下,制造商运用基于绩效的契约来激励供应商投资合适的恢复能力,以协调供应链。结论表明,CVaR可以恰当描述供应链恢复的能力投资行为,当风险规避程度越高,供应链最优恢复能力投资越多;当单位中断时间的惩罚系数的条件期望值等于单位中断时间的商誉成本的条件期望值时,供应链进入协调状态。     

Digitalization for supply chain resilience and robustness: The roles of collaboration and formal contracts

Black swan events such as the coronavirus (COVID-19) outbreak cause substantial supply chain disruption risks to modern companies. In today’s turbulent and complex business environment, supply chain resilience and robustness as two critical capabilities for firms to cope with disruptions have won substantial attention from both the academia and industry. Accordingly, this study intends to explore how digitalization helps build supply chain resilience and robustness. Adopting organizational information processing theory, it proposes the mediating effect of supply chain collaboration and the moderating effect of formal contracts. Using survey data of Chinese manufacturing firms, the study applied structural equation modelling to test the research model. Results show that digitalization has a direct effect on supply chain resilience, and supply chain collaboration can directly facilitate both resilience and robustness. Our study also indicates a complementary mediating effect of supply chain collaboration on the relationship between digitalization and supply chain resilience and an indirect-only mediation effect on the relationship between digitalization and supply chain robustness. Findings reveal the differential roles of digitalization as a technical factor and supply chain collaboration as an organizational factor in managing supply chain disruptions. Paradoxically, formal contracts enhance the relationship between digitalization and supply chain resilience but weaken the relationship between supply chain collaboration and supply chain resilience. The validation of moderating effects determines the boundary conditions of digitalization and supply chain collaboration and provides insights into governing supply chain partners’ behavior. Overall, this study enhances the understanding on how to build a resilient and robust supply chain.     

Scheduling of recovery actions in the supply chain with resilience analysis considerations

Supply chain engineering models with resilience considerations have been mostly focused on disruption impact quantification within one analysis layer, such as supply chain design or planning. Performance impact of disruptions has been typically analysed without scheduling of recovery actions. Taking into account schedule recovery actions and their duration times, this study extends the existing literature to supply chain scheduling and resilience analysis by an explicit integration of the optimal schedule recovery policy and supply chain resilience. In particular, we compute a schedule optimal control policy and analyse the performance of this policy by varying the perturbation vector and representing the outcomes of variations in the form of an attainable set. We propose a scheduling model that considers the coordination of recovery actions in the supply chain. Further, we suggest a resilience index by using the notion of attainable sets. The attainable sets are known in control theory; their calculation is based on the schedule control model results and the minimax regret approach for continuous time parameters given by intervals. We show that the proposed indicator can be used to estimate the impact of disruption and recovery dynamics on the achievement of planned performance in the supply chain.