In an e-retailing setting, the efficient utilization of inventory, storage space, and labor is paramount to achieving high levels of customer service and company profits. To optimize the storage space and labor, a retailer will split the warehouse into two storage regions with different densities. One region is for picking customer orders and the other to hold reserve stock. As a consequence, the inventory system for the warehouse is a multi-item two-stage, serial system. We investigate the problem when demand is stochastic and the objective is to minimize the total expected average cost under some space constraints. We generate an approximate formulation and solution procedure for a periodic review, nested ordering policy, and provide managerial insights on the trade-offs. In addition, we extend the formulation to account for shipping delays and advanced order information. / Singapore-MIT Alliance (SMA)

Two important areas of decision-making in distribution system design involve facility location and inventory policy determination. Facility location analyzes questions such as how many facilities should be opened, where they should be located, and which customers should be assigned to which DCs. Inventory policy determination involves more tactical decisions such as the order quantities and frequencies at each level or echelon in the network. It is believed that these two decisions can influence each other significantly. Including a multi-echelon inventory policy decision in a location analysis allows a user to capitalize on the strengths that each DC has to offer (e.g., lower labor rates, land costs, etc.). Likewise, when the locations of two facilities are known, a multi-echelon inventory policy can be designed better to incorporate the exact lead times and fixed costs between the facilities at each level of the system. Despite this, the two problems are typically solved independently. This research addresses these problems together and investigates different heuristic methods for solving a combined inventory-location model. We begin by presenting the background and formulation for each problem. These formulations are then combined to show how the two problems can be mathematically formulated together. Rather than solve the problem exactly, two heuristic methods using different philosophies are tested. We apply these heuristic methods to the combined inventory-location problem to determine how much we can improve distribution network design solutions and what type of heuristic methodology is most effective in gaining these improvements. Our results show that the combined inventory-location model is capable of improving on the solutions obtained by a location model with a fixed inventory policy. The improvement based on the data sets tested in this research was approximately $60,000. However, in cases where the inventory costs are a larger portion of the total cost, the improvement made by the inventory-location model increased to over $1,000,000. We also found that our second heuristic method tested provided statistically significant improved results over our first heuristic method. Moreover, the second heuristic method typically ran 67% faster. The improved results, although small in a relative sense (the average improvement was 0.18%), would still represent a large absolute improvement in supply chain costs. As much as $174,000 was saved in the data sets tested for this research. / Master of Science

We report on an industrial project in which we developed an inventory model to provide decision support for the design and deployment of the field service support system for a remanufacturable product. The product was a dialysis unit for home use. Each unit that was installed in a home would eventually be removed due to failure, or the need for preventative maintenance, or the termination of the service. Upon removal, each unit was shipped to a central depot for re-manufacturing so that it could be returned to service. We develop a model to determine the inventory requirements at each regional depot, and then describe how to use the model to determine the inventory requirements in the two-echelon system consisting of the central facility and the regional depots. / Singapore-MIT Alliance (SMA)

Many real-world supply chains can be characterised as large and complex multi-echelon systems since they consist of several stages incorporating assembly and distribution processes. A challenge facing such systems is the efficient management of inventory when demand is uncertain, operating costs and customer service requirements are high. This requires specifying the inventory levels at different stages that minimise the total cost and meet target customer service levels. In order to address this problem, researchers proposed the Stochastic-Service Model and the Guaranteed-Service Model (GSM) approaches. These two approaches differ in terms of assumptions with regard to how to address demand variations and service times. This thesis develops several contributions to the GSM based multi-echelon inventory optimisation problem. First of all, we conduct a comprehensive literature review which gives a synthesis of the various GSM work developed so far. Then, we study the impact of some specific assumptions of the GSM such as bounded demand, guaranteed-service times and common review periods. Our numerical analysis shows that the bounded demand assumption may cause a deviation on customer service levels while the guaranteed-service times and common review periods assumptions may result in an increase on the total cost. In real-world supply chains the impact of these assumptions might be significant. Based on the findings presented while investigating the impact of the common review periods assumption, we develop an extension of the GSM that enables to simultaneously optimise the review periods (reorder intervals) and safety stock levels (order-up-to levels) in general acyclic multi-echelon systems. We formulate this problem as a nonlinear integer programming model. Then, we propose a sequential optimisation procedure that enables to obtain near optimal solutions with reasonable computational time. Finally, we focus on the issue of customer service level deviation in the GSM and propose two approaches in order to mitigate this deviation. The numerical study shows that the first approach outperforms the second one in terms of computational time while the second approach provides more accurate solutions in terms of cost. We also present some related issues in decentralised supply chain settings.

Optimizing supply chain is the most successful key for manufacturing systems to be competitive. Supply chain (SC) has gotten intensive research works at all levels: strategic, tactical, and operational levels. These levels, in some researches, have integrated with each other or integrated with other planning issues such as inventory. Optimizing inventory location and level of safety stock at all supply chain partners is essential in high competitive markets to manage uncertain demand and service level. Many works have been developed to optimize the location of safety stock along supply chain, which is important for fast response to fluctuation in demand. However, most of these studies focus on the design stage of a supply chain. Because demand at different horizon times may vary according to different reasons such as the entry of different competitors on market or seasonal demand, safety stock should be optimized accordingly. At the planning (tactical) level, safety stock can be controlled according to each planning horizon to satisfy customer demand at lower cost instead of being fixed by a decision taken at the strategic level. On the other hand, most studies that consider safety stock optimization are tied to a specific system structure such as serial, assembly, or distribution structure. This research focuses on formulating two different models. First, a multi- echelon safety stock optimization (MESSO) model for general supply chain topology is formulated. Then, it is converted into a robust form (RMESSO) which considers all possible fluctuation in demand and gives a solution that is valid under any circumstances. Second, the safety stock optimization model is integrated with tactical supply chain planning (SCP) for manufacturing systems. The integrated model is a multi-objective mixed integer non-linear programming (MINLP) model. This model aims to minimize the total cost and total time. A case study for each model is provided and the numerical results are analyzed.

De nombreuses chaînes logistiques peuvent être caractérisées comme de larges systèmes multi-échelons, car ils se composent souvent de plusieurs étages qui intègrent des activités d'assemblage et de distribution. L’un des enjeux majeurs associé au management de ces systèmes multi-échelons est la gestion efficace de stocks surtout dans des environnements où la demande est incertaine, les coûts de stocks sont importants et les exigences en termes de niveau de service client sont élevées. Cela nécessite en particulier de spécifier les niveaux de stocks aux différents étages afin de minimiser le coût total du système global et de satisfaire les niveaux cibles de service client. Pour faire face à ce problème, deux approches existent dans la littérature; il s’agit du Modèle de Service Stochastique (SSM) et le Modèle de Service Garanti (GSM). Ces deux approches diffèrent en termes d'hypothèses utilisées concernant la façon de gérer les variations de la demande et les temps de service. Cette thèse amène plusieurs contributions au problème d'optimisation de stocks multi-échelons basé sur le GSM. Tout d'abord, nous menons une revue de la littérature internationale qui donne une synthèse des différents travaux réalisés à ce jour. Ensuite, nous étudions l'impact de certaines hypothèses spécifiques du GSM comme la demande bornée, les temps de service garanti et les périodes d’approvisionnement communes. Notre analyse numérique montre que l'hypothèse de demande bornée peut causer une déviation sur les niveaux de service client tandis que les hypothèses de temps de service garanti et de périodes d’approvisionnement communes peuvent entraîner une augmentation du coût total. En pratique, l’impact de ces hypothèses peut être important. En se basant sur les résultats présentés lors de l'analyse de l’hypothèse des périodes d'approvisionnement communes, nous développons une extension du GSM qui permet d'optimiser simultanément les périodes d’approvisionnement (les intervalles de réapprovisionnement) et les niveaux de stocks de sécurité (les niveaux de recomplétement) dans les systèmes multi-échelons acycliques généraux. Nous formulons ce problème comme un modèle de programmation non-linaire en nombres entiers. Ensuite, nous proposons une procédure d'optimisation séquentielle qui permet d'obtenir des solutions proches de l’optimal avec un temps de calcul raisonnable. Enfin, nous nous concentrons sur le problème de déviation de niveau de service client dans le GSM et nous proposons deux approches afin d'atténuer cette déviation. L'étude numérique montre que la première approche est plus performante que la deuxième en termes de temps de calcul tandis que la deuxième approche offre des meilleures solutions en termes de coût. Nous présentons également des problèmes similaires dans les chaînes logistiques décentralisées. / Many real-world supply chains can be characterised as large and complex multi-echelon systems since they consist of several stages incorporating assembly and distribution processes. A challenge facing such systems is the efficient management of inventory when demand is uncertain, operating costs and customer service requirements are high. This requires specifying the inventory levels at different stages that minimise the total cost and meet target customer service levels. In order to address this problem, researchers proposed the Stochastic-Service Model and the Guaranteed-Service Model (GSM) approaches. These two approaches differ in terms of assumptions with regard to how to address demand variations and service times. This thesis develops several contributions to the GSM based multi-echelon inventory optimisation problem. First of all, we conduct a comprehensive literature review which gives a synthesis of the various GSM work developed so far. Then, we study the impact of some specific assumptions of the GSM such as bounded demand, guaranteed-service times and common review periods. Our numerical analysis shows that the bounded demand assumption may cause a deviation on customer service levels while the guaranteed-service times and common review periods assumptions may result in an increase on the total cost. In real-world supply chains the impact of these assumptions might be significant. Based on the findings presented while investigating the impact of the common review periods assumption, we develop an extension of the GSM that enables to simultaneously optimise the review periods (reorder intervals) and safety stock levels (order-up-to levels) in general acyclic multi-echelon systems. We formulate this problem as a nonlinear integer programming model. Then, we propose a sequential optimisation procedure that enables to obtain near optimal solutions with reasonable computational time. Finally, we focus on the issue of customer service level deviation in the GSM and propose two approaches in order to mitigate this deviation. The numerical study shows that the first approach outperforms the second one in terms of computational time while the second approach provides more accurate solutions in terms of cost. We also present some related issues in decentralised supply chain settings.

Optimizing supply chain is the most successful key for manufacturing systems to be competitive. Supply chain (SC) has gotten intensive research works at all levels: strategic, tactical, and operational levels. These levels, in some researches, have integrated with each other or integrated with other planning issues such as inventory. Optimizing inventory location and level of safety stock at all supply chain partners is essential in high competitive markets to manage uncertain demand and service level. Many works have been developed to optimize the location of safety stock along supply chain, which is important for fast response to fluctuation in demand. However, most of these studies focus on the design stage of a supply chain. Because demand at different horizon times may vary according to different reasons such as the entry of different competitors on market or seasonal demand, safety stock should be optimized accordingly. At the planning (tactical) level, safety stock can be controlled according to each planning horizon to satisfy customer demand at lower cost instead of being fixed by a decision taken at the strategic level. On the other hand, most studies that consider safety stock optimization are tied to a specific system structure such as serial, assembly, or distribution structure. This research focuses on formulating two different models. First, a multi- echelon safety stock optimization (MESSO) model for general supply chain topology is formulated. Then, it is converted into a robust form (RMESSO) which considers all possible fluctuation in demand and gives a solution that is valid under any circumstances. Second, the safety stock optimization model is integrated with tactical supply chain planning (SCP) for manufacturing systems. The integrated model is a multi-objective mixed integer non-linear programming (MINLP) model. This model aims to minimize the total cost and total time. A case study for each model is provided and the numerical results are analyzed.

No description available.

The planning of an Aftermarket Supply Chain is a very complex task. This is due to an unpredictable demand which is driven by the need for maintenance and repair. This drive translates to a high variety of lead times, a large number of stock-keeping units (SKUs) and the capacity to deliver spare parts during its full lifecycle. With all these complexities in place, optimizing and parametrizing the planning process is a difficult and time-consuming task. Moreover, the current optimization tool focuses only on one node (each warehouse individually) of the whole Supply Chain, without considering the information such as inventory levels of the other nodes. Hence, the Supply Chain is not completely connected, making it difficult to get a better understanding of the system performance to identify cost draining areas. This leads to capital being tied up in the upper stream of the Supply Chain and later adding unnecessary costs like high inventory costs, rush freight costs, return or scrapping cost. In this study, Discrete Event Simulation (DES) is explored as an additional optimization tool that could analyse and improve the performance of the whole Supply Chain. To do that, the functioning of a node is modelled by replicating the logics behind the flow of material, which includes analysing some manual workflows which are currently present. In Addition, all the information needed from the orders, order lines and parts are mapped. The later part of the study aims to connect all the nodes to form a whole overview of the Supply Chain and further perform optimizations globally.  As an outcome, Multi-Echelon Inventory Optimization has been performed on the whole Supply Chain after connecting all the nodes and thus getting an overview. Furthermore, the impact of different parameters has been studied on the whole model to understand the sensitivity of parameters such as variations in lead time and demand. Finally, different what-if scenarios such as COVID and problems with delay in suppliers were studied, which could help understand the impact of unforeseen situations. / Planeringen av en eftermarknadskedja är en mycket komplex uppgift. Detta beror på en oförutsägbar efterfrågan som drivs av behovet av underhåll och reparation. Enheten översätter till många olika ledtider, ett stort antal lagerhållningsenheter (SKU) och kapacitet att leverera reservdelar under hela dess livscykel. Med alla dessa komplexiteter på plats är optimering och parametrering av planeringsprocessen en svår och tidskrävande uppgift. Dessutom fokuserar det nuvarande optimeringsverktyget bara på en nod (varje lager separat) i hela leveranskedjan utan att beakta informationen som lagernivåerna för de andra noderna. Därför är försörjningskedjan inte helt ansluten, vilket gör det svårt att få en bättre förståelse för systemets prestanda för att identifiera kostnadsavtappningsområden. Detta leder till att kapital binds i den övre strömmen i försörjningskedjan och senare lägger till onödiga kostnader som höga lagerkostnader, snabba fraktkostnader, retur- eller skrotningskostnader. I denna studie undersöks Discrete Event Simulation (DES) som ett ytterligare optimeringsverktyg som kan analysera och förbättra prestanda för hela försörjningskedjan. För att göra det modelleras en nods funktion genom att replikera logiken bakom materialflödet, vilket inkluderar analys av några manuella arbetsflöden som för närvarande finns. Dessutom kartläggs all information som behövs från beställningar, orderrader och delar. Den senare delen av studien syftar till att ansluta alla noder för att bilda en hel översikt över försörjningskedjan och ytterligare utföra optimeringar globalt. Som ett resultat har Multi-Echelon Lageroptimering utförts i hela försörjningskedjan efter att alla noder har anslutits och därmed fått en översikt. Dessutom har effekterna av olika parametrar studerats på hela modellen för att förstå känsligheten hos parametrar som variationer i ledtid och efterfrågan. Slutligen studerades olika tänkbara scenarier som COVID och problem med förseningar hos leverantörer, vilket kan hjälpa till att förstå effekterna av oförutsedda situationer.

Des stratégies d'approvisionnement pérennes sont nécessaires pour les gestionnaires de la chaîne d'approvisionnement afin de faire face aux incertitudes d’approvisionnement et de demande. La diminution des niveaux de service et l'augmentation simultanée des coûts de stockage sont les impacts les plus importants de ces incertitudes. Les perturbations peuvent être causées par des discontinuités de l’approvisionnement, de l'instabilité politique, des catastrophes naturelles et des grèves des employés. Elles pourraient avoir un effet important sur la performance de la chaîne d'approvisionnement. Pour faire face à de telles perturbations, les modèles d'optimisation des stocks doivent être adaptés pour couvrir une structure de réseau multi-échelons et envisager des stratégies d'approvisionnement alternatives telles que le transport latéral (lateral transshipment) et plusieurs sources d’approvisionnement. Dans ce travail, une approche de modélisation basée sur des scénarios est proposée pour résoudre un problème d'optimisation multi-échelons des stocks. En prenant en compte la demande stochastique et les incertitudes sur les capacités de production, le modèle minimise le coût opérationnel total (coûts de stockage, de transport et de retard) tout en optimisant la gestion des stocks et les flux des marchandises. Afin de faire face aux incertitudes, plusieurs échantillons de scénarios sont générés par Monte Carlo et les exemples correspondants d'approximation (SAA) des programmes sont résolus pour obtenir une politique de réponse adéquate au système d'inventaire en cas de perturbations. De nombreuses expériences numériques sont menées et les résultats permettent d'acquérir des connaissances sur l'impact des perturbations sur le coût total du réseau et le niveau de service. / Supply Chain Management (SCM) is an important part of most companies and applying the appropriate strategy is essential for managers in competitive industries and markets. In this context, Inventory Management plays a crucial role. Different inventory systems are widely used in practice. However, it is fundamentally difficult to optimize, especially in multi-echelon networks. A key challenge in managing inventory is dealing with uncertainties in supply and demand. The simultaneous decrease of customer service and increase of inventory-related costs are the most significant effects of such uncertainties. To deal with this pattern, supply chain managers need to establish more effective and more flexible sourcing and distribution strategies. In this thesis, a “framework to optimize inventory decisions in multi-echelon distribution networks under supply and demand uncertainty” is proposed. In the first part of the research work, multi-echelon distribution systems, subject to demand uncertainty, are studied. Such distribution systems are one of the most challenging inventory network topologies to analyze. The optimal inventory and sourcing policies for these systems are not yet unknown. We consider a basic type of distribution network with a single family product through a periodic review setting. Based on this property, a two-stage mixed integer programming approach is proposed to find the optimal inventory-related decisions considering the non-stationary demand pattern. The model, which is based on a Distribution Requirements Planning (DRP) approach, minimizes the expected total cost composed of the fixed allocation, inventory holding, procurement, transportation, and back-ordering costs. Alternative inventory optimization models, including the lateral transshipment strategy and multiple sourcing, are thus built, and the corresponding stochastic programs are solved using the sample average approximation method. Several problem instances are generated to validate the applicability of the model and to evaluate the benefit of lateral transshipments and multiple sourcing in reducing the expected total costs of the distribution network. An empirical investigation is also conducted to validate the numerical findings by using the case of a major French retailer’s distribution network. The second part of the research work is focused on the structure of the optimal inventory policy which is investigated under supply disruptions. A two-stage stochastic model is proposed to solve a capacitated multi-echelon inventory optimization problem considering a stochastic demand as well as uncertain throughput capacity and possible inventory losses, due to disruptions. The model minimizes the total cost, composed of fixed allocation cost, inventory holding, transportation and backordering costs by optimizing inventory policy and flow decisions. The inventory is controlled according to a reorder point order-up-to-level (s, S) policy. In order to deal with the uncertainties, several scenario samples are generated by Monte Carlo method. Corresponding sample average approximations programs are solved to obtain the adequate response policy to the inventory system under disruptions. In addition, extensive numerical experiments are conducted. The results enable insights to be gained into the impact of disruptions on the network total cost and service level. In both parts of the research, insights are offered which could be valuable for practitioners. Further research possibilities are also provided.