Backup Supplier Research Articles

Healthcare Supply Chain Simulation with Disruption Considerations: A Case Study from Northern Italy

Healthcare supply chains (HSCs) have often been considered different from the usual supply chains (SCs) due to their high level of complexity, the presence of high-valuable medical materials and, finally, the fact that they deal with human lives. Furthermore, HSCs are not excluded from the increasing competitivity that is typical of the modern environment: International Healthcare systems are under increasing pressure to reduce waste and eliminate unnecessary costs while improving the quality and consistency of the care they provide to patient. The aim of this work is to analyse the impact of severe disruptions on SC performance in a Healthcare Supply Chain. More specifically, the objective of this study is to develop an approach to firstly analyse the effects of disruptions in terms of financial and operational performances, and successively to test different proactive and reactive mitigation strategies with the aim to maintain the highest service level, closed to 100%, in order to avoid shortages in hospitals’ wards. Disruptions can have a crucial impact on HSCs, because human lives can possibly be at the stake. Considering that the occurrence of disastrous events has been increasing in the last years, it is becoming inevitable to consider this risk in designing and planning HSCs that are more flexible and resilient. This work demonstrates that for long-lasting disruption, activating a backup supplier represents the most efficient mitigation strategy; on the other hand, for small-scale and short-duration disruptions, lateral transshipment represents a good way to mitigate the negative impacts of disruptions against a little increase in costs.

Resilient supplier selection in complex product and its subsystems’ supply chain under uncertainty and risk disruption: A case study for satellite components

Recently, the manufactures of complex product and its subsystems have faced a series of disruptions and troublesome behaviors in supplying goods and items. Likewise, suppliers in this area are more likely to be affected by external risks, in turn eventuating in disturbances. Selecting resilient and expedient suppliers dramatically decreases the delay time and costs and contributes to the competitiveness and development of the companies and organizations in this field. In this regard, this paper aims at proposing a bi-objective robust mathematical model to provide resilience supplier selection and order allocation for complex products and its subsystems in response to uncertainty and disruption risks. In the proposed model, a robust optimization approach is deployed, providing stable decisions for the proposed problem. Also, different resilience strategies including restoring supply from occurred disruptions, fortification of the suppliers, using backup suppliers, and utilizing the extra production capacity for suppliers have been devised to tolerate disruptions. Meanwhile, the augmented e-constraint method is used, ensuring the optimal strong Pareto solutions and preventing the weak ones for the proposed bi-objective model. The evaluation of the effectiveness and desirability of the developed model is explored by discussing a real case study, via which helpful managerial insights are gained.

Mitigating supply disruption with a backup supplier under uncertain demand: competition vs. cooperation

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

Building resilience and managing post-disruption supply chain recovery: Lessons from the information and communication technology industry

In recent years, major local and global disruptions have had significant adverse effects on corporate performance, particularly for businesses with long, global supply chains. Managing disruption and its effects has therefore become a key focus for firms. While the process of disruption management has attracted considerable research attention, much of it has been directed at the pre-disruption stage. This study investigates the post-disruption stage, and its management. The unpredictability of disruption magnitude and nature suggests that the post-disruption management process may be as important, if not more so, than pre-determined pre-disruption strategies. An effective post-disruption management process would directly affect actual ability to recover from sudden and serious disruptions. This study analyzes six companies, variously positioned upstream and downstream in the supply chain of the information and communications technology (ICT) industry in Taiwan. Specific factors and strategies relating to the post-disruption management process were collected by conducting in-depth interviews with the managers and executives of the firms. The information was categorized into distinct disruption management process stages: discovery, recovery, and supply chain redesign. The study findings are interesting and at times, new and counter-intuitive, including the surprising positive effects of clustering in disruptions, and the role of back-up supplier and material verification in disruption recovery. The study emerges with an integrated framework that can be utilized to establish an effective post-disruption management process. The framework is used to develop research propositions for future research.

Inventory rationing policy and backup supply with two classes of customers.

In this paper, we consider joint decisions on inventory rationing and replenishment from a regular supplier or a backup supplier with two classes of customers. When the on-hand inventory falls below a certain critical level, an inventory rationing policy is implemented to reject lower priority customers. We investigated the potential use of a backup supplier as a response to inventory rationing policy. First, we establish an inventory level transition equilibrium equation for the steady-state inventory level probability distributions and derive analytical steady-state performance indicators. Next, the inventory cost function and the nonlinear optimization model are characterized. We use real-coded genetic algorithm (RCGA) to search for the optimal joint decisions. Finally, the proposed algorithm and the joint decisions are investigated with different inventory service polices through numerical experiments. The results show that the inventory rationing policy (IRP) is superior to the traditional priority service policy (CPSP) and the common first-come-first service policy (FCFSP). The backup supply can reduce inventory costs with high arrival intensity.

Is it fake? Using potentially low quality suppliers as back-up when genuine suppliers are unavailable

Supply scarcity of authentic products may force businesses to turn to alternative suppliers potentially carrying counterfeit goods. Detecting these products prior to sale has proven challenging, so when they are procured they may end up in the consumer market. When protection methods, such as full inspection, are operationally or financially unfeasible, inventory management becomes critical. In this paper, we explore the use of alternative back-up suppliers, who might be carrying counterfeit goods in their inventories, when the main trusted source is unavailable. In particular, we prove the optimality of state-dependent order-up-to level replenishment policies under different liability schemes for an intermediary with access to (i) one genuine supplier subject to random disruption and (ii) an alternative supplier offering the same good but of dubious quality. When the intermediary is fully liable for products resold to third parties at the end of the planning horizon, we show that the problem is equivalent to a multi-period inventory control problem with random demand and procurement cost. Optimal cost for the full liability case serves as an upper bound for the case where the intermediary is not held liable for the salvaged goods (limited liability). We perform sensitivity analyses of the optimal order quantities and costs to identify critical problem parameters and shed light on the incentives of supply chain members to combat counterfeit.

Outsourcing contracts and ordering decisions of a supply chain under multi-dimensional uncertainties

We consider a supply chain in which a manufacturer with random yield sells the product through a retailer, where the overflowed order of the retailer may be fulfilled by the manufacturer’s outsourcing product. The inherent uncertainties include demand uncertainty, yield uncertainty, and outsourcing information asymmetry. Two contingent outsourcing contracts are studied based on whether the delivery lead-time quotation is adopted or not. We study how the two outsourcing contracts affect supply chain decision-makings and profits by introducing a backup supplier with private cost information. By employing backward induction technique, we obtain a closed-form analytical solution for backup supplier selection and ordering decisions. The manufacturer can outsource the retailer’s overflowed order to a backup supplier when the actual yield is low. In the presence of multi-dimensional uncertainties, there exists robustness for both outsourcing contracts. Depending on both the yield and the backup supplier’s cost type, the manufacturer makes one of three choices to outsource production: choosing both types of the backup supplier, choosing only the low-cost backup supplier, or choosing neither. The outsourcing quantity may be a part or all of the retailer’s overflowed order. Unlike the contract without constraint on lead-time, the quotation of the delivery lead-time endows the manufacturer with the ability of revealing the true cost information, thus it can improve the manufacturer’s outsourcing profit. However, the quotation of delivery lead-time may make the whole supply chain less efficient.

Game theoretic analysis of a closed-loop supply chain with backup supplier under dual channel recycling

In a recent article, Jafari et al. (2017) considered dual-channel recycling in a closed-loop supply chain (CLSC) consisting one collector, one recycler and one manufacturer. In this paper, their model is extended with a backup supplier considering uncertainty of collection of used products. The shortfall quantity of collection is met up by the backup supplier with the estimated fresh raw materials. Under various power structures or interactions of the supply chain entities, different game theoretic models are developed. It is observed from the numerical study that, depending on the fractional part of the manufacturer’s requirements of recyclable wastes supplied by the collector, the performance of the supply chain increases compared to that of Jafari et al.’s (2017) model in the absence of the recycler. However, in the presence of the recycler, the whole supply chain’s profit surpasses Jafari et al.’s (2017) profit for any amount of used product collection.

Dual Sourcing Strategy for High-Tech Manufacturer Under Supply Risk and Capacity Constraint

High-tech manufacturers frequently suffer from supply risk and capacity constraint. This paper considers a manufacturer who procures a type of high-tech component from two suppliers with asymmetric costs, capacities, and reliabilities, where the low-cost supplier has low reliability. One supplier is selected as a primary supplier and another as a backup supplier. The manufacturer places regular order to the primary supplier and determines backup order quantity from the backup supplier after random yield realization of the primary supplier. By developing a dynamic game-theoretical model, we obtain the optimal wholesale prices for the primary supplier and backup supplier, the optimal order quantity and supplier arrangement decisions for the manufacturer. We show that the manufacturer selects the low-cost supplier as a primary supplier and the high-cost supplier as a backup supplier (namely, LH policy) when the reliabilities of the two suppliers are close. When these reliabilities are not so close, the manufacturer chooses the high-cost supplier as a primary supplier and the low-cost supplier as a backup supplier (namely, HL policy). In addition, the manufacturer in the industry with scale diseconomies is more likely to select HL policy than that in the industry with scale economies. Compared with centralized decision making scheme, decentralized scheme induces the manufacturer more likely to select HL policy in the industry without scale economies, whereas in the industry with scale economies, decentralized scheme induces the manufacturer more likely to choose either HL policy when the capacity advantage of the low-cost supplier is weak, or LH policy when this advantage is significant.

Policies Adoption for Supply Disruption Mitigation Based on Customer Segmentation

Supply chain disruptions resulted from various disasters may cause awesome loss; therefore, corresponding operational policies should be designed to mitigate the negative effect. In industrial goods supply chain, customers are segmented and have different contributions and significances to manufacturers, which have to be considered when adopting mitigation policies. From the customer heterogeneity perspective, the models of a three-stage supply chain with a single supplier and two suppliers are established, and sensitivity analysis is carried out to investigate scenario parameters' impact on optimal mitigation policies. The numerical experiments find that safety stocks are suitable for low disruption probability situation. Meanwhile, strategic reserves with lower holding cost are attractive and efficient to handle a fairly highly disruptive situation or high key customer loss. The results also indicate backup supplier should be adopted if there is a low disruption probability or the backup supplier's price is not significantly higher. This paper extends the supply disruption research and leads some managerial implications and insights that might be useful for disruption mitigation in industrial goods supply chain.

A resilient supply portfolio considering political and disruption risks

The need of accounting for resilience in global supply chains has been growing from practical and academic points of view. However, there is still the need for developing quantitative decision support models on this issue. In the present work, we propose a novel multi-objective mixed possibilistic, two-stage scenario-based stochastic programming model to handle supplier selection and order allocation problem in a global supply chain under operational and disruption risks. The model minimises cost and political risk, while, maximising resilience of the supply portfolio. Various risk mitigation approaches including: contracting with backup suppliers, fortification of suppliers and procurement of emergency inventory, are considered in the model. In addition, the proposed model determines recovery plans. Reservation level driven Tchebycheff procedure is incorporated in the solution procedure to find Pareto-optimal solutions. The validation of the model via computational experiments demonstrates the applicability of the proposed model and solution method in building a resilient supply portfolio under consideration of operational and disruption risks.

Procurement Strategy with Backup Sourcing under Stochastic Supply Risk

Supply risk can have a negative impact on a manufacturer’s performance. Backup sourcing is one of the most commonly used strategies to mitigate the adverse consequences of supply risks. In this paper, we study a procurement strategy with backup sourcing when a manufacturer faces stochastic supply risk and demand. The optimal decisions of the players involved are investigated theoretically using the game theoretic framework, and the impacts of the key parameters, such as wholesale prices and the risk probability, are assessed numerically under supply information symmetry and asymmetry. The results illustrate that the reservation price provided by the backup supplier varies greatly under different information‐sharing conditions. Reservation quantity is negatively correlated with the reservation price, which is affected by the risk probability, wholesale prices, and marginal cost of the backup supplier. We also show that given the same wholesale prices, the potential supply risk has a substantial impact on the manufacturer’s expected profit, and the performance of the manufacturer under asymmetrical supply information is not always better than that under symmetrical information. In addition, we also discuss the impact of wholesale prices on reservation price and participants’ profits based on numerical examples. The research enriches the understanding of a procurement strategy under stochastic supply risk, and the conclusions have certain management significance.

A resilient supply portfolio considering political and disruption risks

The need of accounting for resilience in global supply chains has been growing from practical and academic points of view. However, there is still the need for developing quantitative decision support models on this issue. In the present work, we propose a novel multi-objective mixed possibilistic, two-stage scenario-based stochastic programming model to handle supplier selection and order allocation problem in a global supply chain under operational and disruption risks. The model minimises cost and political risk, while, maximising resilience of the supply portfolio. Various risk mitigation approaches including: contracting with backup suppliers, fortification of suppliers and procurement of emergency inventory, are considered in the model. In addition, the proposed model determines recovery plans. Reservation level driven Tchebycheff procedure is incorporated in the solution procedure to find Pareto-optimal solutions. The validation of the model via computational experiments demonstrates the applicability of the proposed model and solution method in building a resilient supply portfolio under consideration of operational and disruption risks.

Retailers’ Order Strategies in Transshipments in Disruption Risks of Supply Chains

This article investigates the order problem in a two-stage supply chain consisting of retailers, primary suppliers and backup suppliers. From retailers’ perspectives, the optimal offering strategies in unidirectional transshipments are analyzed on the basis of disruption risks in supply chains. With a focus on retailers’ profits, it develops the decision model of retailers’ orders in the dual sourcing mode and in the mode of capacity options, to maximize the retailers’ expected profits. The simulation shows that retailers’ optimal order decisions are not correlated with the joint disruption probability in the two order modes without service constraints; retailers’ optimal decisions are influenced by the joint disruption probability in the two order modes with service constraints, and the impact of the joint disruption probability of the primary suppliers’ optimal order from retailers differs in the two modes. Retailers’ order decisions in respect of backup suppliers are more sensitive to the changing volumes of transshipments. The contribution of this article is the impact of main parameters on retailers’ decisions, providing some guidance for enterprises to make order decisions.

Contingent Strategies for Mitigating Supply Disruptions With Backup Supplier and Information Acquirement

This article describes pervasive supply disruptions and their impacts on supply chain performance. The retailer sources from two suppliers, one supplier is subject to disruptions and the other supplier is perfectly reliable, but more expensive. According to availability of the disruption information, the authors present four information acquirement models. The research conclusions are as follows: The effect of backup supplier is reduced with the increases of reliability coefficient; The value of information is more sensitive to reliability coefficient but less sensitive to potential market capacity; The earlier the information is available, the higher the profit of vendor is; The value of information is lower when reliability coefficient is close to 0 or 1, while information value is significant when reliability coefficient is 0.5; predetermined amount of backup supplier has nothing to do with the cost of strategic supplier, and predetermined amount of strategic supplier is also no connection with backup supplier's predetermined cost and execution cost.

A robust model for a humanitarian relief network with backup covering under disruptions: A real world application

In this paper a reliable and robust model for the humanitarian relief supply chain has been presented to deal with the risk of facility disruptions after an earthquake. Assignment of backup suppliers for affected population centers is considered in order to improve the reliability of the model. Employment of public local centers and defining a two-level structure for rescue centers was considered to raise the accessibility to population centers. A four-step methodology was suggested to define the input parameters of the model which were affected by the uncertainty associated with the magnitude of an earthquake. To investigate this uncertainty, the potential epicenters of probable earthquakes and their magnitudes were assumed as uncertain parameters and estimated with a set of scenarios. A scenario-based robust optimization approach was used to sweep over the uncertain parameters. In the proposed methodology, a practical three-factor function was described to determine the impact of an earthquake on rescue centers and demand points. A solution method was proposed based on Lagrangian relaxation method, whose computational efficiency was also examined. A case study in Tehran was used to show the performance of the model in addition to determine whether the proposed methodology was efficient and practical. The computational results revealed the efficiency of the proposed model and sensitivity analyses indicated that the proposed model could be useful in conditions where budget constraints exist.

Using backup supply with responsive pricing to mitigate disruption risk for a risk-averse firm

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

Multiobjective optimisation model for the selection of critical suppliers integrating sustainability criteria

Most companies are seeking their supplier base around the world. However, sourcing from a global supply base exposes buying companies to a notable set of risks and naturally increases transportation distance with the associated environmental consequences. In this study, we propose a multiobjective order allocation model for selecting primary and backup suppliers in a global supply chain setting. Our model explicitly minimises product costs, transportation costs and the cost of exceeding CO2 allowances within total procurement cost, while also minimising lead-time, sustainability risks and greenhouse gas (GHG) emissions. We present a case study where our model is applied to a global manufacturer of consumer goods and the current supply scenario is compared against an optimal scenario given by the proposed model. Even though total procurement cost is given the highest priority, the optimal scenario (which assigns orders to primary suppliers locally located) represents important advantages in relation to all the criteria evaluated.

Strategic cooperation with a backup supplier for the mitigation of supply disruptions

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

Sustainable supplier selection and order allocation under operational and disruption risks

This study proposes a novel bi-objective two-stage mixed possibilistic-stochastic programming model to address sustainable supplier selection and order allocation problem under operational and disruption risks. A hybrid SWOT-QFD systematic framework is suggested for choosing the most influential sustainability criteria in accordance with the manufacturer's strategies. Furthermore, different proactive strategies such as contracting with backup suppliers, and considering excess capacity for supply nodes are used to enhance the resilience level of the selected supply base. A mixed sustainability-resilience objective function is also introduced to select a resiliently sustainable supply base. A number of numerical examples are tested to validate the formulated model and its solution method. A case study is also provided to demonstrate the applicability of the proposed framework in practice. Finally, a sensitivity analysis is carried out to show the merits of the proposed aggregate sustainability-resilience objective function.