Lateral Transshipment Research Articles

Spare parts inventory routing problem with transshipment and substitutions under stochastic demands

We study a two-level spare parts supply chain in which a manufacturer supplies a central warehouse (CW) with original equipment manufacturer (OEM) and replacement or pattern parts (PP). The CW, distantly located from the manufacturer, distributes both OEM parts and PP to a given number of depots facing stochastic demands. The demand for spare parts is intermittent, exhibiting an infrequent rate and extreme dispersal over time periods. Along with lateral transshipment, PP can be used as substitutes for the OEM parts to sidestep shortage at depots. Assuming that emergency shipments are significantly longer and more expensive, we aim at underlining the relative effectiveness of such a new spare parts inventory management policy. A mixed-integer linear programming model is proposed to solve the inventory routing problem with transshipment and substitution under stochastic demands. The objective is to minimise costs of holding inventory, transportation which includes regular shipment and transshipment, substitution and lost sales. To solve the problem, Sample Average Approximation method is used. Based on empirical goodness-of-fit tests, three demand patterns are studied: Poisson distribution, stuttering Poisson distribution and negative binomial distribution. The model is tested on well-known benchmark instances generated for multi-product multi-vehicle IRP. Computational experiments highlight the benefits of promoting transshipment and substitution on the overall supply chain performance. Results also suggest insights, which are of interest to professionals who are willing to develop new decision support models for the most efficient management of such items.

Stochastic inventory-routing problem with lateral transshipment for perishable product

PurposeThis study aims to design a supply chain network in an uncertain environment while exists two options for distribution of the perishable product and production lot-sizing is concerned.Design/methodology/approachOwing to the complexity of the mathematical model, a solution approach based on a Lagrangian relaxation (LR) heuristic is developed which provides good-quality upper and lower bounds.FindingsThe model output is discussed through various examples. The introduction of some enhancements and using some heuristics results in better outputs in the solution procedure.Practical implicationsThis paper covers the modeling of some real-world problems in which demand is uncertain and managers face making some concurrent decisions related to supply chain management, transportation and logistics and inventory control issues. Furthermore, considering the perishability of product in modeling makes the problem more practically significant as these days there are many supply chains handling dairy and other fresh products.Originality/valueConsidering uncertainty, production, transshipment and perishable product in the inventory-routing problem makes a new variant that has not yet been studied. The proposed novel solution is based on the LR approach that is enhanced by some heuristics and some valid inequalities that make it different from the current version of the LR used by other studies.

Two-Echelon Inventory Model With Service Consideration and Lateral Transshipment

This paper considers a two-echelon inventory system with service consideration and lateral transshipment. So far, researchers have not extensively considered the use of lateral transshipment for such systems. Demand arrivals at both echelons follow the Poisson process. We introduce a continuous review base stock policy for the system in steady state, which determined the expected level for on-hand inventory, expected lateral transshipment level and expected backorder level. We showed that the model satisfied convexity with respect to base stock level. Computational experiments showed that the model with lateral transshipment performed better that the model without lateral transshipment.

Redeploying excess inventories with lateral and reverse transshipments

Service parts management has the potential to generate high profits for companies that deliver superior service parts services in the after-sale market. However, a big challenge in managing service parts operations is to meet the high expectations of service levels and to reduce excess inventories caused by fluctuating demand and a complex service parts logistics network structure. By expanding the conventional inventory management that passively focuses on the forward and lateral flows of service parts deployment, we propose a crucial but overlooked practice of inventory redeployment as an integral part of the operations that allow the proactive management of lateral and reverse flows of service parts. We formulate the service parts inventory problem with the application of an excess inventory redeployment strategy in a multi-echelon service network as a multi-period integer programming model. This optimisation model is evaluated using a case study of an international company’s service parts operations and demonstrates a higher cost-saving potential. Our novel, integrated approach confers the advantage of redeploying excess inventories in a closed-loop service parts logistics network with a higher cost-saving potential that could not have been achieved in a conventional approach.

A Comparative Study of Meta-heuristic Algorithms in Supply Chain Networks

Today, with the development of Information Technology (IT) and economic globalization, the suppliers’ selection has been emphasized in supply chain systems. Accordingly, artificial intelligence-based methods have attracted much attention. Hence, in this research, the selection of appropriate suppliers with respect to the multi-resource supply policy, and the implementation of lateral transshipment have been studied, and meta-heuristic algorithms have been employed to solve the problem. In the proposed method, the supply chain network is improved by minimizing the inventory shortages through utilizing lateral transshipment between different factories. In order to efficiently solve the problem, a hybrid meta-heuristic algorithm based on population-based genetic algorithm (GA) and single-solution simulated annealing (SA), named GASA, is propose, in order to simultaneously gain with the advantages of both algorithms, i.e., global search ability of GA and local search ability of SA. In order to compare the results of the proposed GASA, it is compared with GA and SA, to find the best solution. Given the parameters optimization and conducted analyses and comparisons of primary and hybrid algorithms performance, the hybrid GASA algorithm has been identified as the most efficient algorithm to solve the problem,compared to the other algorithms, emphasizing cost reduction and shortage volume.

Material Risk Pooling: Strategies for Precast Fabrication

Materials used in precast fabrication are different from those cast in the construction site. When fabricators lack production materials, they have to await the materials until the supplier transports them to the precast fabricator. The primary objective of the study is to enhance on-time delivery with relatively low inventory by leading risk-pooling concept with lateral transshipment strategy. To achieve the goal, this research develops lateral transshipment strategies by considering the characteristics and requirements of precast production. The computer simulation technique is then used to analyze the material risk pooling strategies. The proposed method can be used to analyze optimum transshipment policy for shipping the required materials from manufacturing plants that have surplus to others starving for materials. Risks of late delivery can thus be reduced.

Inventory management in blood supply chain considering fuzzy supply/demand uncertainties and lateral transshipment

Supply and demand uncertainties combined with very short lifetime of blood platelets has led to significant wastage of the total blood collected from the donors. Conversely, great shortage of platelets may be obtained due to the limited number of donors and emergency demands. Therefore, it is of utmost importance to develop appropriate inventory management model to simultaneously minimize both shortage and wastage along the blood supply chain. To achieve this purpose, this paper presents an Inventory Management model for Age-differentiated platelets under supply/demand Uncertainties (IMAU) for Blood Supply Chains with Lateral Transshipment (BSCLT), resulting a new model named IMAU-BSCLT. The proposed model is solved using whale optimization algorithm considering the costs of ordering from blood centers and lateral transshipment, transportation, inventory holding, shortage, and wastage. In order to validate the proposed methodology, a case study of blood supply chain is used to show the usability of the proposed model and claim its benefits over existing models. Simulation results demonstrate that lateral transshipment between different demand nodes has a major impact on load balancing leads to simultaneously reduce both shortage and wastage costs. According to the obtained results, shortage rate (total shortage per total demands) and wastage rate (total wastage per total supply) of the proposed method are 3.4 % and 4.8 %, respectively.

An Innovative Business Model for a Multi-echelon Supply Chain Inventory Management Pattern

Nowadays, companies are experimenting novel organizational solutions to efficiently operate in uncertain and highly dynamic scenarios. As a potential solution, this paper proposes a new business model for a multi-echelon Supply Chain inventory management pattern. Specifically, an inventory model with proactive lateral transshipments was developed and subsequently tested carrying out 288 experiments with the aim of assessing transshipments impact on the performance of a two-echelon Supply Chain. The final goal was to investigate the potential reduction of the overall cost of the enterprise and, conversely, whether this approach could promote significant improvements in the level of service, achievable through a more efficient management of resources. The analyses and simulations indicate the use of large batches and/or low-cost products did not demand the necessity of transshipment events. These preliminary findings could be potentially validated and tested in the future considering more complex networks or multiple products.

Inventory management of manufacturers with yield uncertainty and lateral transshipment

Inventory management of manufacturers with yield uncertainty and lateral transshipment

Multi-period stochastic lateral transshipment problem for rental products

In this study, we employed a multi-period stochastic programming model to examine the lateral transshipment problem of rental products and verified the effectiveness of this model under several ass...

Multi-period stochastic lateral transshipment problem for rental products

Multi-period stochastic lateral transshipment problem for rental products

Safety Stock Allocation in an Online Retailing Network: A Stochastic Optimization Approach

Online retailers utilize lateral transshipment to meet the immediate shortage of demand at one location from surplus at another location within their retailing network. One option to reduce such potentially expensive transshipment is to hold additional safety stock at each location. We investigate the allocation of safety stock in a network of fulfillment centers with the possibility of lateral transshipment. Our problem setting is motivated by online retailers who operate multi-node retailing networks of fulfillment centers under a periodic review and order-up-to policy. We formulate the safety stock allocation problem as a stochastic optimization model with uncertain demand, and optimally solve the model for a network with up to six fulfillment centers. The optimization model quickly becomes intractable, with an increase in problem size. Therefore, for larger networks, we propose a novel hub-and-spoke approach where the fulfillment centers are first clustered and then connected as a Minimal Spanning Tree, followed by a system-wide safety stock allocation via stochastic optimization. The hub-and-spoke approach performs quite favorably in our extensive computational experiments, and demonstrates the potential to save millions of dollars in outbound shipping costs for an online retailer. Our study provides online retailers with an easily implementable and efficient safety stock allocation mechanism across a complex and geographically distributed network of fulfillment centers.

A stochastic prepositioning model for distribution of disaster supplies considering lateral transshipment

This paper focuses on addressing uncertainties in disasters when considering lateral transshipment opportunities for pre-positioning relief supplies. To deal with uncertain demands the problem is formulated as a two-stage stochastic programming model, which decides simultaneously on the locations of relief facilities and the allocations of relief supplies to demand nodes. Meanwhile, different damage levels caused by disasters are considered and reflected by a survival rate of usable stocked relief items. Multiple types of supplies with various priorities, values and spaces are explored. A real-world case study based on the Gulf Coast region of the United States is conducted to illustrate the application of the developed model. By comparison with the direct shipment solution, the lateral transshipment solution is demonstrated to be more cost-effective and flexible. The sensitivity analysis of out-of-stock penalty cost and maximum travel distance provides managerial insights for relief agencies.

A multi-cut L-shaped method for resilient and responsive supply chain network design

We present a stochastic optimisation model that can be used to design a resilient supply chain operating under random disruptions. The model aims to determine sourcing and network design decisions that minimise the expected total cost while ensuring that the minimum customer service level is achieved. The proposed model incorporates several resilience strategies including multiple sourcing, multiple transport routes, considering backup suppliers, adding extra production capacities, as well as lateral transshipment and direct shipment. A multi-cut L-shaped solution approach is developed to solve the proposed model. Data from a real case problem in the paint industry is utilised to test the model and solution approach. Important managerial insights are obtained from the case study. Our analyses focus on (1) exploring the relationship between supply chain cost and customer service level, (2) examining the impacts of different types of disruptions on the total cost, (3) evaluating the utility of resilience strategies, (4) investigating the benefits of the proposed solution approach to solve problems of different sizes and (5) benchmarking the performance of the proposed stochastic programming approach.

Application of Lateral Transshipment in Cost Reduction of Decentralized Systems

COVID-19 has exposed the global supply chains to great vulnerability. In such extreme circumstances, product availability becomes a primary concern. This paper studies a basic inventory management strategy—lateral transshipment—under decentralized systems, which may play an important role in dealing with stockouts during unexpected crises. Lateral transshipments not only react quickly to stockout, but are also environmentally friendly due to the significant reduction of production and transportation pollution. This paper studies optimal lateral transshipment and replenishment decisions under a decentralized setting. We construct a multi-stage stochastic model that captures demand uncertainty and customer switching behavior. We demonstrate that, similar to the centralized setting, the optimal transshipment decision follows a double-threshold structure. The optimal replenishment quantities are determined under two pricing mechanisms—individual mechanism (IP) and negotiated mechanism (NP). Numerical examples are provided to demonstrate the impact of lateral transshipment on supply chain cost reduction.

An augmented sweep clustering method for inventory-routing problem with simultaneous pickup and delivery considering lateral transshipment between repair centers

An augmented sweep clustering method for inventory-routing problem with simultaneous pickup and delivery considering lateral transshipment between repair centers

Operational level planning of a multi-item two-echelon spare parts inventory system with reactive and proactive interventions

In this paper, we investigate operational spare parts planning in a multi-item two-echelon distribution system, taking into account real-time supply information in the system. We consider a broad range of operational interventions, either reactive (to solve a shortage) or proactive (to avoid a shortage). These interventions particularly include lateral transshipments between warehouses (local warehouses), emergency shipments from the depot (central warehouse), and doing nothing and waiting for pipeline inventory. We propose an integrated approach to determine the optimal timing and size of each intervention type to minimize the total downtime and shipment costs associated with interventions. Data from a leading original equipment manufacturer of high-tech systems is used to test the performance of our approach. We find that our integrated approach reduces total downtime considerably with a very limited increase in total shipment costs. Proactive emergency shipments contribute most to downtime reduction. The benefit of our approach is higher for high demand parts. Allowing complete pooling between warehouses increases downtime savings and usage of proactive emergency shipments even further. Our approach is efficient enough to solve practical size problems. We also propose a heuristic based on a greedy algorithm, which is well known in the literature. We find that the gap between the heuristic and the optimal solution is relatively large.

Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks

The effects of disruption in both supply chain and its infrastructures like power network have not been addressed in previous works, simultaneously. Besides, disruption risks have environmental effects besides their economic effects. For this purpose, this paper studies a resilient-green closed loop supply chain design problem considering the perishable nature of products and disruption risks in both supply chain and power networks. To cope with disruptions five different risk mitigation strategies are employed. We have developed an integrated bi-objective mixed-integer linear programming model to minimize the expected total network cost and the expected total amount of carbon emissions of both networks. The results demonstrate that applying strategy of integrating interdependent networks along with four other resilient strategies, especially “intermediate facility” and “lateral transshipment” strategies, will improve the performance of integrated network significantly. Also, with increasing the lifetime of products, the performance of the proposed model will be improved.

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.

Lateral transshipment with partial request and random switching

A large body of inventory management research has been devoted to lateral transshipment. Most of the existent models assume that the unmet local demand will automatically request transshipment, and that the unmet local demand does not seek inventory at other locations within the same echelon. In contrast, we investigate a two-store retailer’s inventory replenishment and transshipment decisions when those two assumptions do not hold. Specifically, we use a fixed request rate to model partial demand for transshipment at the shortage store and a random switch rate to model the arrival of the unmet demand at the surplus store. We characterize the optimal transshipment and inventory replenishment policies. We find that it is not always in the best interest of the retailer to satisfy as much as possible the transshipment demand. In light of the switched demand flowing to the surplus store, the retailer may benefit from saving the leftover inventory at the surplus store for the switched demand. The optimal transshipment policy follows a double-threshold structure when the prospect of the switched demand is not large enough; and a transshipment quantity of zero becomes optimal otherwise. Through an extensive numerical analysis, we examine the impact of the request rate and the switch rate, together with other parameters. We also evaluate a few simple-to-use transshipment heuristics, including one that we devise based on the structure of the optimal transshipment policy. The consistent, near-optimal performance of the devised heuristic is a confirmation of the importance of our theoretical work on the optimal policy.