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.     

On the risk-averse optimization of service level in a supply chain under disruption risks

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.     

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.     

On the robust decision-making in a supply chain under disruption risks

This paper considers a robust decision-making problem associated with supplies of parts and deliveries of finished products in a customer driven supply chain under disruption risks. The robustness refers to an equitably efficient performance of a supply chain in average-case as well as in the worst-case, which reflects the decision-makers common requirement to maintain an equally good performance of a supply chain under different conditions. Given a set of customer orders for products, the decision-maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers and schedule the orders over the planning horizon, to equitably optimise average and worst-case performance of the supply chain. The supplies are subject to independent random local and regional disruptions. The obtained combinatorial stochastic optimisation problem is formulated as a mixed-integer program with conditional value-at-risk as a risk measure. The ordered weighted averaging aggregation of the expected value and the conditional value-at-risk of the selected optimality criterion is applied to obtain a robust solution. The risk-neutral, risk-averse and robust solutions that optimise, respectively average, worst-case and equitable average and worst-case performance of a supply chain are determined and compared for cost and customer service level objective functions. Numerical examples and computational results, in particular comparison with the mean-risk approach, are presented and some managerial insights are reported.     

Maintenance supplier selection considering life cycle costs and risks: a fuzzy goal programming approach

In this study, we address a new variant of supplier selection problem named maintenance supplier selection problem faced by a manufacturer. The production system consists of different multi-component equipments whose maintenance activities require several components (parts) each of which could be provided by multiple suppliers. A multi-objective mathematical model is developed to decide about the supply base of each part as well as the purchasing quantity of each part from each selected supplier. The model accounts for the total life cycle costs of purchased parts and various risks threatening the candidate suppliers. A fuzzy/soft lexicographic goal programming approach with soft priorities between objectives is proposed to enable the decision-maker to make preferred trade-offs between objectives by which the effects of various risks in each phase of life cycle of procured parts are investigated. The capability and effectiveness of the proposed model is validated through a case study. Some sensitivity analyses are also carried out for investigating the impact of cost, risk and objectives’ priorities on the final preferred compromise solution. Finally, some managerial insights and concluding remarks are provided.     

Evaluation mechanism for structural robustness of supply chain considering disruption propagation

This paper aims to develop a novel evaluation mechanism for assessing the structural robustness of a supply chain considering disruption propagation. Disruption propagation means that the impact of risks propagates to the whole supply chain along the connected structure. Based on the propagation model, a structural robustness evaluation mechanism is devised by integrating two quantitative metrics, average path length and in degree-out degree. To validate the proposed mechanism, the result of the quantitative assessment of the structural robustness on random networks is compared with the probability of network disruption due to the random risk. From the results of the statistical verifications and sensitivity analysis, it can be said that the proposed mechanism is better at explaining the robustness of a supply chain. In other words, all components of a network, such as nodes and arcs, and their relationships should be considered altogether, in order to more accurately measure the robustness. It may be possible to apply the proposed mechanism to the very first step of designing the supply chain. Especially, in the case of it being hard to redesign a supply chain structure after practically launching and operating the designed network, the proposed mechanism may be utilised to verify whether the planned supply chain is robust to risks or not.     

Supply chain resilience for single and multiple sourcing in the presence of disruption risks

This paper investigates the use of sourcing strategies to achieve supply chain resilience under disruptions. The coping strategies considered are single and multiple sourcing, backup supplier contracts, spot purchasing, and collaboration and visibility. Collaboration and visibility, which affect suppliers’ recovery capabilities and a buyer’s warning capability, have not been similarly modelled in the past. A scenario-based mathematical model is developed such that it considers objectives under uncertainties including disruption risks and operational risks. A broad numerical study examines its output for various risk attitudes in a decision-maker, ranging from risk neutral to risk averse. The sensitivity of procurement strategies to other key parameters such as recovery and warning capabilities is examined. One of the major findings is that buyer’s warning capability plays a vital role in enhancing supply chain resilience. We seek to build on these efforts to further support disruption planning and mitigation and to obtain a deeper understanding of the relationship between supply chain characteristics and resilience.     

Resilient and sustainable supply chain design: sustainability analysis under disruption risks

Resilience to disruptions and sustainability are both of paramount importance to supply chains. However, the interactions between the two have not been thoroughly explored in the academic literature. We attempt to contribute to this area by presenting a hybrid methodology for the design of a sustainable supply network that performs resiliently in the face of random disruptions. A stochastic bi-objective optimisation model is developed that utilises a fuzzy c-means clustering method to quantify and assess the sustainability performance of the suppliers. The proposed model determines outsourcing decisions and resilience strategies that minimise the expected total cost and maximise the overall sustainability performance in disruptions. Important managerial insights and practical implications are obtained from the model implementation in a case study of plastic pipe industry.     

Application of gray DEMATEL-ANP in green-strategic sourcing

The study aims to identify and prioritize the criteria and sub-criteria dimensions essentials for green strategic sourcing. Data were taken from the experts working in the automobile industry. By applying the novel Grey-DEMATEL-ANP (GDANP) approach, we identified the technology and environmental management system as the key sub-criteria dimensions to be considered while selecting the supplier. Besides, we also found quality and delivery time as the important factors to be considered while selecting a supplier for green supply chain management (GSCM). To present the useful application of our findings, we conducted an empirical study by taking the automobile industry of an emerging market as the case. The major contribution of the study is the application of variant D-ANP in identifying the important dimensions of supplier selection.     

Budget allocation in coping with supply chain disruption risks

Disruption management, as an important research topic, has attracted scholars’ broad attention in recent years due to the increasing exposure of disruption risks in supply chains. To date, researches in this field often focus on either prevention or mitigation measures and the budget allocation problem is paid relatively little attention. This paper therefore proposes an approach to determine the optimal budget allocation based on prevention measures in combination with mitigation measures. First, considering different disruption situations, the bow-tie is applied to developing the disruption management frameworks that integrate risk prevention and risk mitigation. Second, the corresponding optimization models are formulated to determine the optimal budget allocation plans. In order to validate the proposed approach, we compare the computation results with those obtained from the prevention approach and the mitigation approach. Also, random experiments are conducted to analyse the impacts of randomly generated disruption and response scenarios. Finally, a real-life case is provided to testify the usefulness and merits of our proposed approach. The results show that the proposed approach can help decision-makers reduce more loss caused by the disruption risks.     

A hierarchical model for optimal supplier selection in multiple sourcing contexts

This paper addresses a crucial objective of the strategic purchasing function in supply chains, i.e. optimal supplier selection. We present a hierarchical extension of the data envelopment analysis (DEA), the most widespread method for supplier rating in the literature, for application in a multiple sourcing strategy context. The proposed hierarchical technique is based on three levels. First, a modified DEA approach is used to evaluate the efficiency of each supplier according to some criteria proposed by the buyer. Second, the well known technique for order preference by similarities to ideal solution (TOPSIS) is applied to rank the maximally efficient suppliers given by the previous step. Third and finally, a linear programming problem is stated and solved to find the quantities to order from each maximally efficient supplier in the multiple sourcing context. The presented approach is able to straightforwardly discern between efficient and inefficient partners, avoid the confusion between efficient and effective suppliers and split the supply in a multiple sourcing context. The hierarchical model is applied to the supply of a C class component to show its robustness and effectiveness, while comparing it with the DEA and TOPSIS approaches.     

Dual sourcing in managing operational and disruption risks in contract manufacturing

We study a US OEM that outsources its production to two contract manufacturers, a local manufacturer (e.g. in the US or Mexico) and a foreign manufacturer (e.g. in China). The local manufacturer is relatively reliable, but low margin. The foreign manufacturer offers high margin, but is subject to disruption risks. Both manufacturers experience some level of operational uncertainties, and the operational risks can be positively or negatively correlated. Disruption risks are modelled as a Poisson jump process at a random magnitude, and operational risks are modelled as correlated stochastic diffusion processes. We develop a stochastic dynamic programming formulation to characterise the OEM’s optimal capital allocation decision to contract manufacturers, and provide the necessary and sufficient conditions for each optimal decision. The objective of our study is to investigate how dual sourcing balances the risks and opportunities, when the OEM bears disruption risks and correlated operational risks. We find that the two manufacturers can be substitutes or complements to each other. Risk of disruption renders the unreliable foreign manufacturer less attractive, but has a moderating effect on the allocation to the local manufacturer. We also provide managerial implications of our study.     

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.     

Supplier selection based on supply chain ecosystem,performance and risk criteria

A supply chain ecosystem consists of the elements of the supply chain and the entities that influence the goods, information and financial flows through the supply chain. These influences come through government regulations, human, financial and natural resources, logistics infrastructure and management, etc., and thus affect the supply chain performance. Similarly, all the ecosystem elements also contribute to the risk. The aim of this paper is to identify both performances-based and risk-based decision criteria, which are important and critical to the supply chain. A two step approach using fuzzy AHP and fuzzy technique for order of preference by similarity to ideal solution has been proposed for multi-criteria decision-making and illustrated using a numerical example. The first step does the selection without considering risks and then in the next step suppliers are ranked according to their risk profiles. Later, the two ranks are consolidated into one. In subsequent section, the method is also extended for multi-tier supplier selection. In short, we are presenting a method for the design of a resilient supply chain, in this paper.     

Allocation flexibility for agribusiness supply chains under market demand disruption

This paper develops a multi-commodity multi-period optimisation model to analyse market demand disruption risk in agribusiness supply chains. It investigates the role of allocation flexibility and the effectiveness of multiple risk management strategies for achieving allocation flexibility. A robust optimisation formulation is used to obtain risk-averse solutions for an objective combining expected profit and risk. Numerical results are presented for a real-life case study of Zespri’s kiwifruit supply chain. The results show that allocation flexibility is effective for mitigating market demand disruption risk. Three proposed risk management strategies, namely diversified demand market, backup demand market and flexible rerouting, improve both expected profit and risk measures. While diversified demand market and backup demand market strategies are equally important for all decision-makers, flexible rerouting is especially significant for less risk-averse decision-makers.     

Order allocation in purchasing management: a review of state-of-the-art studies from a supply chain perspective

Supplier selection (SS) and order allocation (OA) are strategic decisions that have a substantial effect on a company’s performance. However, order allocation is often neglected, since it results from supplier selection and is considered supplementary: little attention has been paid to its specific nature and complexity. Consequently, the authors conducted a systematic literature review specifically regarding order allocation methods. The research aimed to evaluate how often and when the issue has been dignified with an individual focus, independently of the supplier selection problem. This study conducted a comprehensive examination of the order allocation models and solutions, criteria for order quantity allocation, features of suppliers, items, planning periods, and demand most commonly considered in the literature. Finally, it aimed to discover whether supply chain configurations and trends have been considered in efforts to find a solution to the problem. The scientific contribution of this study is threefold: (i) to expand the review of scientific literature regarding order allocation models, (ii) to identify research gaps and highlight research opportunities, and (iii) to suggest a research agenda for the development of order allocation models according to the requirements of current trends in supply chain management.     

A two-way approach to supply chain partner selection

All organisations face partner selection. It is the first step in establishing partnership. Over recent decades, a one-way selection approach has been dominant in the literature. It is assumed that a buyer searches for one or more suppliers from a set of suppliers. Once the best supplier is found and selected, a partnership is formed. However, in reality, not only is the selection not a one-way process as the relationship itself is bilateral, but the existence of other buyers and suppliers also needs to be taken into account. In this study, a two-way partner selection approach is proposed, where not only the buyers evaluate suppliers, but also the suppliers have the opportunity to evaluate buyers. Considering a marketplace where there are several buyers and suppliers, an integrative model is proposed in the form of an assignment model to optimally match all the buyers and suppliers. A generalised model is also formulated to take the suppliers’ capacity and buyers’ demand into account. The proposed models are illustrated using some numerical examples. Finally, conclusions and recommendations for future research are provided.     

Mitigating global supply chain risks through corporate social responsibility

This paper presents a decision model that captures supply-side disruption risks, social risks, and demand-side uncertainty within an integrated global supply chain and corporate social responsibility (CSR) modelling and analysis framework. The global supply chain decision-makers must decide on the level of investment in CSR activities and the choice of trading partners (manufacturer or retailer) given their CSR consciousness and perceived riskiness in order to maximise profit and minimise their overall risk. The model incorporates individual attitudes towards disruption risks among the manufacturers and the retailers, with the demands for the product associated with the retailers being random. The model allows one to investigate the effects of heterogeneous CSR activities in a global supply chain and to compute the resultant equilibrium pattern of product outputs, transactions, product prices, and levels of social responsibility activities. The results show that CSR activities can potentially be used to mitigate global supply chain risk.     

Impact of risk aversion and backup supplier on sourcing decisions of a firm

This study focuses on a newsvendor problem with multiple suppliers, considering the risk-neutral and risk-averse objectives in a mean-risk optimisation model. The firm first decides order quantities from the primary (unreliable) suppliers and reserve capacity from the secondary (reliable) backup supplier. After the state of its primary suppliers and customer demand is revealed, the firm purchases from the available suppliers and uses the backup supplier subject to the reserved capacity. For the special case of normal distribution and risk-neutral objective, optimality properties were developed. A comprehensive numerical study examines the sensitivity of the sourcing strategies of firms to risk, shortage cost, demand uncertainty, salvage value, and capacity reserve options; various managerial insights are offered based on this.