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Hospitals procure high volumes of medical supplies through large distributors in order to leverage economies of scale. However, when shortages hit, hospitals incur high penalty costs by purchasing from secondary markets. In this paper, the authors counter the hospital's typical purchasing strategy that a collaborative relationship with a large, Tier I medical supply distributor is beneficial under all conditions. The paper finds that during shortages the more beneficial strategy is for the hospital to add a medium-sized, Tier II distributor who offers a transactional relationship and is willing to provide a “preferred allocation” in return for a pre-committed annual purchase contract. The paper aims to discuss these issues.

The authors assume availability of order volume to be a stochastic process and formulate the problem as a two-stage stochastic programming model, with optimal allocation in the second stage. The authors analyze the first-stage objective function using full-factorial numerical experimentation and perform a complete search for optimal volume mix. In addition, the model accounts for purchasing relationship, shortage cost, and varying price discount schedules.

Under no shortage situation, hospitals purchase its entire order volume from Tier I distributor. However, during shortages, for any increase in preferred allocation from the Tier II distributor, hospitals purchase high volumes from the Tier II distributor except when preferred allocation and availability is high. The paper finds that the average cost savings for the use of preferred allocation is 16.14 percent.

Existing purchasing literature focusses on the benefit of using single/multiple homogenous distributors under all conditions. In this paper, the authors examine the benefit of using non-homogenous distributors under conditions of shortage when one of them is willing to provide preferred allocation under varying price discount schedules.

Details an empirical study of the measurement of stockout costs in a distribution system and examines the incidence of stockouts in the supply of spare parts to the motor trade. Comprehensive studies were conducted in three major car workshops in Sweden and cost data collected from a variety of sources, including structured observation, quasi‐experiments, interviews and secondary data from internal information systems. On the basis of this research, a typology of stockout cost situations has been constructed and central concepts and measurement models developed. Results reveal that stockout costs are measurable in the system under review; information about stockout costs can have a significant impact on managerial decision making.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

This study aims to design a reliable multi-level, multi-product and multi-period location-inventory-routing three-echelon supply chain network, which considers disruption risks and uncertainty in the inventory system.

A robust optimization approach is used to deal with the effects of uncertainty, and a mixed-integer nonlinear programming multi-objective model is proposed. The first objective function seeks to minimize inventory costs, such as ordering costs, holding costs and carrying costs. It also helps to choose one of the two modes of bearing the expenses of shortage or using the excess capacity to produce at the expense of each. The second objective function seeks to minimize the risk of disruption in distribution centers and suppliers, thereby increasing supply chain reliability. As the proposed model is an non-deterministic polynomial-time-hard model, the Lagrangian relaxation algorithm is used to solve it.

The proposed model is applied to a real supply chain in the aftermarket automotive service industry. The results of the model and the current status of the company under study are compared, and suggestions are made to improve the supply chain performance. Using the proposed model, companies are expected to manage the risk of supply chain disruptions and pay the lowest possible costs in the event of a shortage. They can also use reverse logistics to minimize environmental damage and use recycled goods.

In this paper, the problem definition is based on a real case; it is about the deficiencies in the after-sale services in the automobile industry. It considers the disruption risk at the first level of the supply chain, selects the supplier considering the parameters of price and disruption risk and examines surplus capacity over distributors’ nominal capacity.

Antibiotics shortages have become an increasingly common problem in Europe because of several reasons, including the offshoring of the production of active pharmaceutical ingredients for many of these products to low production cost countries, such as China and India. The problem has deteriorated because of the Covid-19 crisis that has put most global value chains (GVCs) under great stress. This situation has boosted extensive discussions among academics, practitioners and policymakers on possible changes to the configuration of GVCs. This paper aims to focus specifically on antibiotics supply chains from the perspective of a small country (Sweden), and analyse the pros and cons of backshoring and nearshoring alternatives, as a means to reduce drug shortages.

This work adopts a systemic perspective to capture the implications of reshoring for the different stakeholders involved in the antibiotics field. The present meso-analysis, focusing at the industry level, is based on multiple sources of primary data collected between 2014 and 2021, including participation in policy-related projects and interviews with over 100 representatives of key stakeholders in the antibiotics field.

This paper shows how reshoring can address the problems of drug shortages and reduce availability risk in antibiotics’ GVCs. However, the authors show that no simple and best solution exists because both alternatives of reshoring, i.e. backshoring and nearshoring, entail pros and cons for different stakeholders. The authors conclude with implications for policymakers and managers.

The analysis of pros and cons of both backshoring and nearshoring for various stakeholders offers relevant implications for research on operations and supply management, international business and economics/political science.

This paper looks at reshoring as a policy-driven decision and provides an innovative systemic perspective to analyse the implications for different stakeholders of two reshoring options concerning the antibiotics supply chain.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

For many years, facilities location problems have attracted a great deal of attention in the literature. As a result, there is now a variety of methods for solving these problems. However, due to the recent interest, little research is found relating to the issues concerning international facilities location problems. Furthermore, in spite of the extensive modelling work done on facilities location, little modelling research exists on location problems. Provides a capacitated multi‐period, 0‐1 mixed integer programming formulation for the international facilities location problem and discusses its applications to an actual company case. This application is carried out to demonstrate not only how the model can be applied in practice but also to show its potential benefits when compared to other methods.

This paper deals with the combined management and design of a sustainable pharmaceutical supply chain network with considering recycling.

This paper first utilizes the analytical hierarchy process to select and rank green manufacturers. Second, the authors proposed a multi-objective nonlinear mathematical model to design a sustainable pharmaceutical supply chain network. The proposed model has been linearized and solved using the LP-metric method using GAMS software.

A real case study has been conducted in Iran. The results show that environmental and social issues can be improved while minimizing total costs.

Given the criticality and importance of drugs in human health and the importance of recycling in today's world, proper management and design of a sustainable drug supply chain are necessary. This study pays special attention to environmental issues by utilizing multi-criteria decision approaches and customer satisfaction.

Inventory management is generally recognised to be of sufficient importance to warrant the appointment of a person to carry specific responsibility for it; possibly along with other areas of the distribution function. Preferably, inventory should be seen as one component of distribution, interacting as it does with all the elements contained therein. This monograph has not been written for the analyst but rather for the person responsible for taking inventory decisions. The aim is to present a general description of the main problem areas and to describe procedures by which the problems can be resolved. Given a broad appreciation a useful dialogue may then be set up with the analyst and more effective use made of the large quantity of data which is invariably available in the distribution field.

The purpose of this paper is to analyze the effects of supply chain coordination on inventory management while the retailer inventory cycle consists of a shortage period and the backorder rate linearly decreases as a function of shortage duration. It is intended to consider how on‐hand inventory and shortage durations are altered when the decisions are centralized.

Mathematical modelling of inventory costs for the retailer and the vendor is used to formulate objective functions. The vendor sets his inventory period as an integer multiple (n) of the retailer inventory cycle in which the integer multiple is a decision variable. Solution spaces of models are analyzed to determine two other decision variables including, on‐hand inventory duration and shortage length.

The integrated model consists of a unique pseudo convex area when (n) is fixed and as a result, there is a unique minimum point. Based on numerical examples and sensitivity analysis, in most situations coordinated inventory management reduces total costs of the supply chain and cost reduction rate increase at larger production rates.

This paper is a combination between production‐inventory models in two‐stage supply chains and partial backordering, which has appeared in single inventory models. To the best of the authors' knowledge, no mathematical model has yet been proposed. Moreover, the benefits of synchronization are analyzed through numerical examples.