Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers

© 2014 IEEE. In this paper, a supply disruption management model is introduced in a three-tier supply chain with multiple suppliers and retailers, where the system may face sudden disruption in its raw material supply. At first, we formulated a mathematical model for ideal conditions and then reformulated it to revise the supply, production and delivery plan after the occurrence of a disruption, for a future period, to recover from the disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We have also proposed an efficient heuristic to solve the model and the results have been compared, with another established solution approach, for a good number of randomly generated test problems. The comparison showed the consistent performance of our developed heuristic. This paper also presents some numerical examples to explain the usefulness of the proposed approach

On the use of phase-type distributions for inventory management with supply disruptions

Cataloged from PDF version of article.Maintaining the continuity of operations becomes increasingly important for systems that are subject to disruptions due to various reasons. In this paper, we study an inventory system operating under a (q, r) policy, where the supply can become inaccessible for random durations. The availability of the supply is modeled by assuming a single supplier that goes through ON and OFF periods of stochastic duration, both of which are modeled by phase-type distributions (PTD). We provide two alternative representations of the state transition probabilities of the system, one with integral and the other employing Kolmogorov differential equations. We then use an efficient formulation for the analytical model that gives the optimal policy parameters and the long-run average cost. An extensive numerical study is conducted, which shows that OFF time characteristics have a bigger impact on optimal policy parameters. The ON time characteristics are also important for critical goods if disasters can happen. Copyright (C) 2011 John Wiley & Sons, Ltd

A quantitative model for disruption mitigation in a supply chain

© 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches

Developing lean and responsive supply chains : a robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet

Supply Chain Coordination Contracts under Double Sided Disruptions Simultaneously

Supply chain coordination models are developed in a two-echelon supply chain with double sided disruptions. In a supply chain system, the supplier may suffer from the product cost disruption and the retailer suffers from the demand disruption simultaneously. The purpose of this study is to design proper supply chain contracts, under which the supply chain with double sided disruption can be coordinated. Firstly, the centralized decision-making models are applied to find the optimal price and quantity under three cases as the baseline. The different cases are divided by the different relationship between the product cost disruption and the demand disruption. Secondly, two different types of contracts are introduced to coordinate the whole supply chain. One is all-unit wholesale quantity discount policy (AQDP) contract, and the other one is capacitated linear pricing policy (CLPP) contract. And it is found out that the gap between the demand disruption and the product cost disruption is the key factor to influence the supply chain coordination. Some numerical examples and sensitivity analysis are given to illustrate the models. The AQDP contracts are listed out under different cases to show how to use it under double sided disruptions

Joint inventory-location problem under the risk of probabilistic facility disruptions

This paper studies a reliable joint inventory-location problem that optimizes facility locations, customer allocations, and inventory management decisions when facilities are subject to disruption risks (e.g., due to natural or man-made hazards). When a facility fails, its customers may be reassigned to other operational facilities in order to avoid the high penalty costs associated with losing service. We propose an integer programming model that minimizes the sum of facility construction costs, expected inventory holding costs and expected customer costs under normal and failure scenarios. We develop a Lagrangian relaxation solution framework for this problem, including a polynomial-time exact algorithm for the relaxed nonlinear subproblems. Numerical experiment results show that this proposed model is capable of providing a near-optimum solution within a short computation time. Managerial insights on the optimal facility deployment, inventory control strategies, and the corresponding cost constitutions are drawn. Highlights ► A reliable inventory-location model is proposed for optimal facility location and inventory management under facility disruptions. ► The model allows customer re-assignments and minimizes the expected total system cost across all facility disruption scenarios. ► A customized Lagrangian relaxation approach is developed for the mixed-integer nonlinear formulation. ► Numerical experiments are conducted to demonstrate the model performance and draw managerial insights

Towards pre-emptive resilience in military supply chains: a compromise decision support model-based approach

The complex and dynamic nature of military supply chains (MSC) requires constant vigilance to sense potential vulnerabilities. Several studies have employed decision support models for the optimization of their operations. These models are often limited to a best single-point solution unsuitable for complex MSC constellations. In this article, the authors present a novel approach based on decision support models to explore a range of satisficing solutions against disruptions in MSCs using a compromise Decision Support Problem (cDSP) construct and Decision Support in the Design of Engineered Systems (DSIDES). Two cases were evaluated: (1) a baseline scenario with no disruption and (2) with disruption to achieve target values of three goals: (1) minimizing lead time, (2) maximizing demand fulfilment and (3) maximizing vehicle utilization. The results obtained in Case 1 identified a more stable solution space with minimal deviations from the target value, while in Case 2 the solution space was unstable with deviations from the target values

Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand

A systems thinking approach for modelling supply chain risk propagation

Supply Chain Risk Management (SCRM) is rapidly becoming a most sought after research area due to the influence of recent supply chain disruptions on global economy. The thesis begins with a systematic literature review of the developments within the broad domain of SCRM over the past decade. Thematic and descriptive analysis supported with modern knowledge management techniques brings forward seven distinctive research gaps for future research in SCRM. Overlapping research findings from an industry perspective, coupled with SCRM research gaps from the systematic literature review has helped to define the research problem for this study. The thesis focuses on a holistic and systematic approach to modelling risks within supply chain and logistics networks. The systems thinking approach followed conceptualises the phenomenon of risk propagation utilising several recent case studies, workshop findings and focus studies. Risk propagation is multidimensional and propagates beyond goods, finance and information resource. It cascades into technology, human resource and socio-ecological dimensions. Three risk propagation zones are identified that build the fundamentals for modelling risk behaviour in terms of cost and delay. The development of a structured framework for SCRM, a holistic supply chain risk model and a quantitative research design for risk assessment are the major contributions of this research. The developed risk assessment platform has the ability to capture the fracture points and cascading impact within a supply chain and logistics network. A reputed aerospace and defence organisation in UK was used to test the experimental modelling set up for its viability and for bridging the gap between theory and practice. The combined statistical and simulation modelling approach provides a new perspective to assessing the complex behavioural performance of risks during multiple interactions within network

Um modelo conjunto de localização e operação de estoque em redes dinâmicas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2010Neste trabalho é proposto um modelo de otimização para o problema de localização de instalações, alocação de demanda e operação de estoque em redes dinâmicas onde parte dos arcos desta rede está sujeita a interrupções que podem ocorrer segundo probabilidades conhecidas, respeitando um processo Markoviano. O modelo é concebido com o objetivo de integrar, numa mesma abordagem, decisões estratégicas (onde localizar) e decisões operacionais (como operar) visando proporcionar a minimização dos custos do sistema, ao mesmo tempo em que se estabelece nível de serviço para atendimento à demanda. Para resolver este modelo é desenvolvida uma estratégia que utiliza programação dinâmica estocástica, simulação, o modelo clássico de lote econômico de compra e uma adaptação do algoritmo heurístico de Teitz & Bart. Tal estratégia é implementada em um programa de computador e testes computacionais são realizados com sucesso em um estudo de caso elaborado a partir de dados hipotéticos. Os resultados obtidos e as análises realizadas demonstram a factibilidade do modelo e a aplicabilidade da estratégia de solução