Aggregate constrained inventory systems with independent multi-product demand: control practices and theoretical limitations

In practice, inventory managers are often confronted with a need to consider one or more aggregate constraints. These aggregate constraints result from available workspace, workforce, maximum investment or target service level. We consider independent multi-item inventory problems with aggregate constraints and one of the following characteristics: deterministic leadtime demand, newsvendor, basestock policy, rQ policy and sS policy. We analyze some recent relevant references and investigate the considered versions of the problem, the proposed model formulations and the algorithmic approaches. Finally we highlight the limitations from a practical viewpoint for these models and point out some possible direction for future improvements

Rolling schedule approaches for supply chain operations planning

Supply Chain Operations Planning (SCOP) involves the determination of an extensive production plan for a network of manufacturing and distribution entities within and across organizations. The production plan consist of order release decisions that allocate materials and resources in order to transform these materials into (intermediate) products. We use the word item for both materials, intermediate products, and end-products. Furthermore, we consider arbitrary supply chains, i.e. the products produced by the supply chain as a whole and sold to customers consist of multiple items, where each item may in turn consists of multiple items and where each item may be used in multiple items as well. The aim of SCOP is not only to obtain a feasible production plan, but the plan must be determined such that pre-specified customer service levels are met while minimizing cost. To obtain optimal production plans we use a linear programming (LP) model. The reason we use an LP model is twofold. First, LP models can easily be incorporated in existing Advanced Planning Systems (APS). Second, while the multi-echelon inventory concept can only be used for uncapacitated supply chains and some special cases of capacitated supply chains, capacity constraints but also other restrictions can easily added to LP models. In former mathematical programming (MP) models, the needed capacity was allocated at a fixed time offset. This time offset was indicated by fixed or minimum lead times. By the introduction of planned lead times with multi-period capacity allocation, an additional degree of freedom is created, namely the timing of capacity allocation during the planned lead time. When using the LP model in a rolling schedule context, timing the capacity allocation properly can reduce the inventory cost. Although the number of studies on MP models for solving the SCOP or related problems are carried out by various researchers is enormous, only a few of these studies use a rolling schedule. Production plans are only calculated for a fixed time horizon based on the forecast of customers demand. However, since customer demand is uncertain, we emphasize the use of a rolling schedule. This implies that a production plan, based on sales forecasts, is calculated for a time interval (0; T], but only executed for the first period. At time 1, the actual demand of the first period is known, and the inventory status of the consumer products are adjusted according the actual demand. For time interval (1; T + 1], a new production plan is calculated. In this thesis, we studied the proposed LP strategy with planned lead times in a rolling schedule setting whereby we focused on the following topics: ² timing of production within the planned lead time, ² factors influencing the optimal planned lead time, ² early availability of produced items, i.e. availability of items before the end of their planned lead time, and ² balanced material allocation. In the first three studies we explore the possibilities of using planned lead times. In the first study, timing of production, we compare the situation whereby released items are produced as soon as there is available capacity with the situation whereby released items are produced as late as possible within the planned lead time. If items are produced as soon as possible, there is more capacity left for future production. Since we work with uncertain customer demand whereby demand may be larger than expected, this capacity might be very useful. A drawback of production as soon as possible are the additional work-in-process cost. The results of simulation studies show that if the utilization rates of resources and/or the variation in demand are high, producing early is better. However this is only the case if the added value between the concerned item and the end item is high. The second study deals with factors influencing the optimal planned lead time. From queuing theory it is already known that the variance in demand and the utilization rate of the resources determine the waiting time. More variation and/or higher utilization rates give longer waiting times. Since lead times consist for a large part of waiting time, these two factors most probably also influence the length of the optimal planned lead time. For a set of representative supply chain structures we showed that this was indeed the case. With longer planned lead times, the flexibility in capacity allocation is higher. Additional flexibility gives lower safety stocks, but longer planned lead times also means more work-in-process. Hence, an important third factor which influence the optimal planned lead time is the holding costs structure. When using planned lead times, early produced items have to wait the remainder of their planned lead time. This seems contradictory, especially if these items are necessary to avoid or reduce backorders. Therefore we adapt the standard LP model in two ways. In the first model, items are made available for succeeding production steps directly after they are produced. And in the second model, produced items are only made available for succeeding production steps if they are needed to avoid or reduce backorders. Experiments showed that the first model does not improve the performance of the standard LP strategy. The advantages of planned lead times longer than one period are nullified by early availability of produced items. The second model indeed improves the performance of the standard LP strategy, but only when the planned lead times are optimal or longer. Comparing the introduced LP strategy with a so-called synchronized base stock policy under the assumption of infinite capacity, it turned out that the LP strategy is outperformed by the base stock policy. In order to obtain a better performance, we Summary 121 added linear allocation rules to the LP model. With these allocation rules shortages of child items are divided among the parent items using a predefined allocation fraction. A second way of balanced allocation of child items is obtained by replacing the linear objective function by a quadratic one. The results of a well-chosen set of experiments showed that although the synchronized base stock policy also outperforms the adjusted LP strategies, the difference in performance is small. Hence, the adjusted LP strategies are good alternatives for large, capacitated supply chain structures which cannot be solved by synchronized base stock policies. Comparing the model with linear allocation rules with the model with quadratic objective function, the preference is given to the latter model. This model does not only give the lowest inventory costs, it also has the shortest computation time. Furthermore, this model can easily be implemented and solved by existing software. Summarizing the main results of this thesis, we conclude that deterministic LP models can be used to solve the SCOP problem with stochastic demand by using the LP model in a rolling schedule concept. By using optimal planned lead times with multiperiod capacity allocation, early production during the planned lead times, and early availability of needed produced items before the end of the planned lead time, we can decrease the inventory costs. The costs can also be reduced by using allocation strategies to allocate shortages among parent items proportionally. Especially the results for the model with quadratic objective function are promising

Global supply chains of high value low volume products

Imperial Users onl

Planning of outsourced operations in pharmaceutical supply chains

In this dissertation, we focus on the planning and control of supply chains where part of the supply chain is outsourced to a contract manufacturer(s). Supply Chain Management deals with the integration of business processes from end-customers through original suppliers that provide products, services and information that add value for customers (Cooper et al., 1997). In a narrow sense, a supply chain can be ‘owned’ by one large company with several sites, often located in different countries. Planning and coordinating the materials and information flows within such a worldwide operating company can be a challenging task. However, the decision making is easier than in case more companies are involved in a supply chain, since the sites are part of one organization with one board and it is likely that the decision makers have full access to information needed for the supply chain planning. Outsourcing is an ‘act of moving some of a firm’s internal activities and decision responsibilities to outside providers’ (Chase et al., 2004) and it has been studied extensively in the literature.Outsourcing is developing in many industries, but in this dissertation, we focus on outsourcing in the pharmaceutical industry, where outsourced supply chain structures are rapidly developing. Recent studies show that the global pharmaceutical outsourcing market has doubled from 2001 to 2007 and it is expected to further increase in the upcoming years. In the pharmaceutical industry, the outsourcing relationship is typically long-term and customers often require high service levels. Due to high setup costs, production is conducted in fixed large batch sizes and campaign sizes. The cumulative lead time within the supply chain is more than one year, whereas the customer lead time is about two months. In this industry, production activities are outsourced for three main reasons. First, intellectual property legislation requires outsourcing the production activities to a contract manufacturer that owns the patent for specific technologies that are needed to perform the production activities. Second, expensive technologies or tight (internal) capacity restrictions also result in outsourcing. Third, to limit the supply uncertainty, companies outsource to have an external source producing the same product next to an internal source. This dissertation deals with the planning and control of outsourced supply chains, which are supply chains where part of the supply chain is outsourced to a contract manufacturer. Most supply chain operations planning models from the literature assume that the supply chain is planned at some level of aggregation and that further coordination is conducted at a more detailed level by lower planning levels. These concepts implicitly assume that the lower planning level and the operations are conducted within the same company with full information availability and full control over the operations, which is not case when part of the supply chain is outsourced. Hence, the objective of this dissertation is to obtain insights into the implications of outsourcing on the supply chain planning models. First, we review the literature on outsourcing research and we find that little is known on the operational planning decisions in an outsourced supply chain and on the implications of outsourcing on the operations planning. The literature on outsourcing at the operational level uses outsourcing purely as a secondary source to control performances such as the delivery reliability. Consequently, we discuss two case studies that we conducted into outsourced supply chains to understand the implications of outsourcing on the supply chain operations planning function, where the contract manufacturer is the only source of supply. The main implications of the planning and control of outsourced supply chains can be summarized in three categories: limited information transparency, limited control over the detailed planning and priorities at the contract manufacturer, and contractual obligations. Below, we discuss these in more detail. In order to decide on the release of materials and resources in a supply chain, it is required that the decision maker is able to frequently monitor the status of the supply chain. In an outsourced supply chain, the outsourcer does not have access to all relevant information of the entire supply chain, especially not to the available capacity in each period, also because the contract manufacturer serves a number of different (and sometimes even competing) outsourcers on the same production line. Moreover, the contract manufacturer plans and controls its part of the supply chain based on rules and priorities that are unknown to the outsourcer. This results in facing an uncertain capacity allocation by the outsourcer. Another implication is that the contract manufacturer requires by contract to reserve capacity slots prior to ordering. These reservations are subject to an acceptation decision, which means that part of the reservation quantity can be rejected. The accepted reservation quantity bounds the order quantity that follows later on. Therefore, another main insight from the case study is that in an outsourcing relationship, the order process consists of different (hierarchically connected) decisions in time. In the ordering process, the uncertain capacity allocation of the contract manufacturer should be incorporated. Hence, the order release mechanism requires a richer and more developed communication and ordering pattern than commonly assumed in practice. In a subsequent study, we build on this insight and we design three different order release mechanisms to investigate to what extent a more complicated order release function improves (or deteriorates) the performance of the supply chain operations planning models. The order release mechanisms differ in the number of decision levels and they incorporate the probabilistic behaviour of the contract manufacturer. Based on a simulation study, we show that a more advanced order release strategy that captures the characteristics of outsourcing performs significantly better than a simple order release strategy that is commonly used in practice. We also discuss the conditions for a successful implementation of the more advanced order release strategy. In another study, we study the case where the contract manufacturer is a second source next to an internal manufacturing source for the same product and where the outsourcer faces inaccurate demand forecasts. The two sources are constraining the supply quantities in different ways. Its own manufacturing source is more rigid, cheaper and tightly capacitated, whereas the contract manufacturer is more flexible but more expensive. In that study, we compare the performance of two different allocation strategies by a simulation study in which we solve the model in a rolling horizon setting. The results show that the rigid allocation strategy (the cheaper source supplies each period a constant quantity) performs substantially better than the dynamic allocation strategy (each period the allocation quantities are dynamic) if the parameters are chosen properly. In another study, we study the outsourcer’s problem of deciding on the optimal reservation quantity under capacity uncertainty, i.e., without knowing what part of the reservation will be accepted. In that study, we develop a stochastic dynamic programming model for the problem and we characterize the optimal reservation and order policies. We conduct a numerical study where we also consider the case where the capacity allocation is dependent on the demand distribution. For that case, we show the structure of the optimal policies based on the numerical study. Further, the numerical results reveal several interesting managerial insights, such as that the optimal reservation policy is little sensitive to the uncertainty of the capacity allocation from the contract manufacturer. In that case, the optimal reservation quantities hardly increase, but the optimal policy suggests increasing the utilization of the allocated capacity. We also study the outsourced supply chain from the contract manufacturer’s perspective. In that study, we consider the case where the contract manufacturer serves a number of outsourcers with different levels of uncertainty. The contract manufacturer faces the question of how to allocate the contractual capacity flexibility in an optimal way. More precisely, we focus on the contract manufacturer’s decision to make the acceptation decision under uncertainty. The more the contract manufacturer accepts from an outsourcer, the more risk is taken by the contract manufacturer, as the outsourcer might not fully utilize the accepted reservation quantity. However, we assume that the outsourcer is willing to pay an additional amount to compensate the contract manufacturer for that risk. We develop a mixed-integer programming model, which optimizes the allocation of capacity flexibility by maximizing the expected profit. Offering more flexibility to the more risky outsourcer generates higher revenue, but also increases the penalty costs. The allocated capacity flexibilities are input (parameters) to the lower decision level, where the operational planning decisions are made and demands are observed. The simulation results reveal interesting managerial insights, such that the more uncertain outsourcer gets at least the same capacity flexibility allocated as the less uncertain outsourcer. Moreover, we have seen that when the acceptation decision is made, priority is given to the less uncertain outsourcer, because that information is the most valuable. However, we see the opposite effect when orders are placed, namely that priority is given to the more uncertain outsourcer, i.e., the most paying outsourcer, as no uncertainty is involved anymore. These insights are helpful for managers of contract manufacturers when having contract negotiations with the outsourcers. We believe that the results and insights that we obtained in the various research studies of this dissertation can contribute to solving the broader real-life problems related to the planning and control of outsourced supply chains. We also discuss potential managerial implications of our findings explicitly addressing the management decisions that may be affected by using the insights from our studies. Considering the operational implications of outsourcing when taking the strategic outsourcing decision will lead to a different and a better estimate of the transaction costs and probably to a different strategic outsourcing decision. Based on our research, we think that the transaction cost estimate will be higher if the outsourcer and the contract manufacturer do not agree on operational issues, such as the multi-level order release mechanism. From a tactical point of view, the outsourcer may include the options of postponement and cancellation in the contract, even if the contract manufacturer would charge little extra for these options. The results show that the benefits of including these options are substantial. Moreover, we showed that controlling a contract manufacturer operationally in the same way as an internal manufacturing source leads to a nervous ordering behaviour with a lot of changes and a lot of panicky communication between the outsourcer and the contract manufacturer. Combining the insights from different studies, one can also conclude that including little reservation cost is beneficial to both parties; it leads to a win-win situation. The outsourcer with a high level of demand uncertainty secures sufficient capacity allocation from the contract manufacturer and avoids more expensive penalty costs. For the outsourcer with less demand uncertainty, it is wise to set the contract such that the reservation costs are subtracted from the total paid amount. Moreover, this outsourcer may gain competitive advantage if his competitors operate in the same market by securing sufficient capacity allocation (by paying little reservation costs). For the contract manufacturer, including reservation cost is also beneficial, as it leads to a better match between the outsourcer’s reservation and ordering behaviour

Optimal Global Supply Chain and Warehouse Planning under Uncertainty

A manufacturing company\u27s inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation. The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer\u27s geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw materials and parts from the local supplier and control the inventory levels in the warehouse. In contrast, the lead time for the orders placed with an overseas supplier is usually long because sea-freight is often used as a primary mode of transportation. Therefore, the orders for the raw materials and parts (henceforth, we collectively refer to raw material and part by part) procured from overseas suppliers are usually placed using forecasted order quantities. In Chapter 2, we study the procurement process to reduce the overall expected cost and determine the optimal order quantities as well as the mode of transportation for procurement under forecast and inventory uncertainty. We formulate a two-stage stochastic integer programming model and solve it using the progressive hedging algorithm, a scenario-based decomposition method. Generally, the orders are placed with overseas suppliers using weekly or monthly forecasted demands, and the ordered part is delivered using sea-containers since sea-freight is the primary mode of transportation. However, the end manufacturing warehouse is usually designed to hold around one to two days of parts. To replenish the inventory levels, the manufacturer considered in this research unloads the sea-container that contains the part that needs to be restocked entirely. This may cause over-utilization of the manufacturer\u27s warehouse if an entire week\u27s supply of part is consolidated into a single sea-container. This problem is further aggravated if the manufacturer procures hundreds of different parts from overseas suppliers and stores them in its warehouse. In Chapter 3, we study the time-series forecasting models that help predict the manufacturing company\u27s daily demand quantities for parts with different characteristics. The manufacturer can use these forecasted daily demand quantities to consolidate the sea-containers instead of the weekly forecasted demand. In most cases, there is some discrepancy between the predicted and actual demands for parts, due to which the manufacturer can either have excess inventory or shortages. While excess inventory leads to higher inventory holding costs and warehouse utilization, shortages can result in substantially undesirable consequences, such as the total shutdown of production lines. Therefore, to avoid shortages, the manufacturer maintains predetermined safety stock levels of parts with the suppliers to fulfill the demands arising from shortages. We formulate a chance-constraint optimization model and solve it using the sample approximation approach to determine the daily safety stock levels at the supplier warehouse under forecast error uncertainty. Once the orders are placed with the local and overseas suppliers, they are consolidated into trailers (for local suppliers) and sea-containers (for overseas suppliers). The consolidated trailers and sea-containers are then delivered to the manufacturing plant, where they are stored in the yard until they are called upon for unloading. A detention penalty is incurred on a daily basis for holding a trailer or sea-container. Consolidating orders from different suppliers helps maximize trailer and sea-container space utilization and reduce transportation costs. Therefore, every sea-container and trailer potentially holds a mixture of parts. When a manufacturer needs to replenish the stocks of a given part, the entire sea-container or trailer that contains the required part is unloaded. Thus, some parts that are not imminently needed for production are also unloaded and stored inside the manufacturing warehouse along with the required parts. In Chapter 4, we study a multi-objective optimization model to determine the sea-containers and trailers to be unloaded on a given day to replenish stock levels such that the detention penalties and the manufacturing warehouse utilization are minimized. Once a sea-container or trailer is selected to replenish the warehouse inventory levels, its contents (i.e., pallets of parts) must be unloaded by the forklift operator and then processed by workers to update the stock levels and break down the pallets if needed. Finally, the unloaded and processed part is stored in the warehouse bins or shelves. In Chapter 5, we study the problem of determining the optimal team formation such that the total expected time required to unload, process, and store all the parts contained in the sea-containers and trailers selected for unloading on a given day is minimized

Optimal Policy for Production Systems with Two Flexible Resources and TwoProducts

Manufacturing companies are facing increasing volatility in demand. As a result, there has been an emerging need for a flexible multi-period manufacturing system that uses multiple resources to produce multiple products with stochastic demands. To manage such multi-product, multi-resource systems, manufacturers need to make two decisions simultaneously: setting a production quantity for each product and allocating the limited resources dynamically among the products. Unfortunately, although the flexibility design and investment have been extensively studied, the literature has been muted on how to make production and allocation decisions optimally from an operational perspective. This article attempts to fill this literature gap by investigating a multi-period system using multiple flexible resources to produce two products. We identify the structural property of the cost functions, namely ρ-differential monotone. Based on this property, the optimal production and allocation policy can be characterized by switching curves, which divide the state space into eight or nine sub-regions based on the segmentation of decision rules. We analyze different cases in terms of production costs and resource utilization ratios, and show how they affect the optimal production and allocation decisions. Finally, we compare three heuristic policies to the optimal one to display the advantage of resource flexibility and the effectiveness of a heuristic policy. Supplementary materials are available for this article. Go to the publisher’s online edition of IISE Transaction, datasets, additional tables, detailed proofs, etc

Evaluation of sales and operations planning in a process industry

Cette thèse porte sur la planification des ventes et des opérations (S±&OP) dans une chaîne d'approvisionnements axée sur la demande. L'objectif de la S±&OP, dans un tel contexte, est de tirer profit de l'alignement de la demande des clients avec la capacité de la chaîne d'approvisionnement par la coordination de la planification des ventes, de la production, de la distribution et de l'approvisionnement. Un tel processus de planification exige une collaboration multifonctionnelle profonde ainsi que l'intégration de la planification. Le but étant d'anticiper l'impact des décisions de vente sur les performances de la chaîne logistique , alors que l'influence de la dynamique des marchés est prise en compte pour les décisions concernant la production, la distribution et l'approvisionnement. La recherche a été menée dans un environnement logistique manufacturier multi-site et multi-produit, avec un approvisionnement et des ventes régis par des contrats ou le marché. Cette thèse examine deux approches de S±&OP et fournit un support à la décision pour l'implantation de ces méthodes dans une chaîne logistique multi-site de fabrication sur commande. Dans cette thèse, une planification traditionnelle des ventes et de la production basée sur la S±feOP et une planification S±fcOP plus avancée de la chaîne logistique sont tout d'abord caractérisées. Dans le système de chaîne logistique manufacturière multi-site, nous définissons la S±&OP traditionnelle comme un système dans lequel la planification des ventes et de la production est effectuée conjointement et centralement, tandis que la planification de la distribution et de l'approvisionnement est effectuée séparément et localement à chaque emplacement. D'autre part, la S±fcOP avancée de la chaîne logistique consiste en la planification des ventes, de la production, de la distribution et de l'approvisionnement d'une chaîne d'approvisionnement effectuée conjointement et centralement. Basés sur cette classification, des modèles de programmation en nombres entiers et des modèles de simulation sur un horizon roulant sont développés, représentant, respectivement, les approches de S±&OP traditionnelle et avancée, et également, une planification découplée traditionnelle, dans laquelle la planification des ventes est effectuée centralement et la planification de la production, la distribution et l'approvisionnement est effectuée séparément et localement par les unités d'affaires. La validation des modèles et l'évaluation pré-implantation sont effectuées à l'aide d'un cas industriel réel utilisant les données d'une compagnie de panneaux de lamelles orientées. Les résultats obtenus démontrent que les deux méthodes de S±feOP (traditionnelle et avancée) offrent une performance significativement supérieure à celle de la planification découplée, avec des bénéfices prévus supérieurs de 3,5% et 4,5%, respectivement. Les résultats sont très sensibles aux conditions de marché. Lorsque les prix du marché descendent ou que la demande augmente, de plus grands bénéfices peuvent être réalisés. Dans le cadre de cette recherche, les décisions de vente impliquent des ventes régies par des contrats et le marché. Les décisions de contrat non optimales affectent non seulement les revenus, mais également la performance manufacturière et logistique et les décisions de contrats d'approvisionnement en matière première. Le grand défi est de concevoir et d'offrir les bonnes politiques de contrat aux bons clients de sorte que la satisfaction des clients soit garantie et que l'attribution de la capacité de la compagnie soit optimisée. Également, il faut choisir les bons contrats des bons fournisseurs, de sorte que les approvisionnements en matière première soient garantis et que les objectifs financiers de la compagnie soient atteints. Dans cette thèse, un modèle coordonné d'aide à la décision pour les contrats e développé afin de fournir une aide à l'intégration de la conception de contrats, de l'attribution de capacité et des décisions de contrats d'approvisionnement pour une chaîne logistique multi-site à trois niveaux. En utilisant la programmation stochastique à deux étapes avec recours, les incertitudes liées à l'environnement et au système sont anticipées et des décisions robustes peuvent être obtenues. Les résultats informatiques montrent que l'approche de modélisation proposée fournit des solutions de contrats plus réalistes et plus robustes, avec une performance prévue supérieure d'environ 12% aux solutions fournies par un modèle déterministe