In this master thesis study, new inventory control mechanisms are developed for the repairables in Nedtrain. There is a multi-item, multi echelon system with a continuous review and one for one replenishment policy and there are different demand supply options in each control mechanism. There is an aggregate mean waiting time constraint in each local warehouse and the objective is to minimize the total system cost. The base stock levels in each warehouse are determined with an approximation method. Then different demand supply options are compared with each other.

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Lowering the degree of component commonality may yield a higher type-II service level for a periodic review assemble-to-order system that aims to maximize reward. This is achieved via separating inventories of all the shared components for different products. We investigate the optimal bill-of-materials structure for two-product assemble-to-order systems with arbitrary number of components. The inventory of a shared component can be separated or common between different products. We show that an optimal bill-of-materials can be characterized between the following two extremal configurations: either two products share all common components, or they do not share any common component. / Thesis / Doctor of Philosophy (PhD)

In this paper, we minimize the holding cost of the safety stock in the supply chain subject to linear constraints on the service times between the nodes of the network. In the problem, the objective function is concave as we assume the demand to be bounded by a concave function. The optimal solutions of the problem belong to the set of extreme points of the polyhedron, specified by the constraints of the problem. We first characterize the extreme points for the two-layer networks and then provide bounds to use in a branch and bound algorithm. / Singapore-MIT Alliance (SMA)

In this thesis we analyze the procurement process of buyers and supply decisions of manufacturers. Companies are looking for ways to decrease their procurement costs, which account for a large percentage of the supply chain costs. We study the effects of demand aggregation and collaborative procurement on buyers' profitability. First, we make a high-level analysis and consider a market with multiple buyers and suppliers where multi-unit transactions for multiple items take place. The procurement costs are effected by economies of scale in the suppliers' production costs and by economies of scope in transportation. We design buyer strategies that model different collaboration levels and assess the role of collaboration under varying market conditions. Next, we analyze the procurement process at a lower level and identify benefits of inter-firm collaboration among buyers who are potential competitors in the end market. We adopt a game-theoretic approach to explore the economics of the basic mechanism underlying collaborative procurement, and determine the conditions that makes it beneficial for the participants. Besides low procurement costs, important considerations in supplier selection are responsiveness and the reliability of the suppliers in meeting demand. Hence, manufacturers face the pressure for quoting short and reliable lead times. We cover several aspects of the manufacturer's problem, such as quoting reliable due-dates based on current workload in the system, maximizing profit considering the lateness cost incurred due to late deliveries, and deciding on the level of inventory to increase responsiveness. We employ a model where demand arrival and manufacturing processes are stochastic, and obtain insights on the optimal due-date quotation policy and on the optimal inventory level.

In this thesis, two-echelon systems with exogenous and endogenous lead times are studied for the orders placed by the retailer(s) from the supplier. The retailer(s) employ periodic-review base-stock policy, namely (R,S) policy. For the case the demand during review period is i.i.d. and the probability distribution is Normal for each review period, a new method is proposed for exogenous lead time case under stationary policy. The results of the proposed method is then compared with the results of the existing methods in the literature and it is concluded that the proposed method provides service levels sufficiently close to target levels whereas the existing methods do not necessarily provide target levels. We use the simulation to study the endogenous replenishment lead time case. The proposed method is modified when the retailer employs stationary policy and it is seen that the proposed method gives no-stockout probabilities close to target levels. Moreover, the impacts of using adaptive policy on the performance of the retailer are studied for endogenous replenishment lead time case. It is concluded that updating of the order-up-to-level deteriorates the performance of the retailer. Finally, it is questioned whether it is beneficial for a retailer to use adaptive policy in a supply chain with two retailers. Simulation results show that the deterioration in the performance of the retailer handling stationary policy is larger compared to the other retailer handling adaptive policy and the deteriorations get larger in the case of an increase in update frequency or in utilization of the supplier.

The aim of this thesis is to develop a tractable method for approximating the steady-state behavior of continuous-review base-stock controlled assembly systems with Poisson demand arrivals and manufacturing and assembly facilities modeled as Jackson networks. One class of systems studied is to produce a single type of finished product assembling a number of components and another class is to produce two types of finished products allowing component commonality. The performance measures evaluated are the expected backorders, fill rate and the stockout probability for finished product(s). A partially aggregated but exact model is approximated assuming that the state-dependent transition rates arising as a result of the partial aggregation are constant. This approximation leads to the derivation of a closed-form steady-state probability distribution, which is of product-form. Adequacy of the proposed model in approximating the steady-state performance measures is tested against simulation experiments over a large range of parameters and the approximation turns out to be quite accurate with absolute errors of 10% at most for fill rate and stockout probability, and of less than 1.37 (&amp / #8776 / 2) requests for expected backorders. A greedy heuristic which is proposed to be employed using approximate steady-state probabilities is devised to optimize base-stock levels while aiming at an overall service level for finished product(s).

This study is on the service models for dynamic scheduling of multi-class make-to-stock systems. An exponential single-server facility processes different types of items one by one and demand arrivals for different item types occur according to independent Poisson processes. Inventories of the items are managed by base-stock policies and backordering is allowed. The objective is to minimize base-stock investments or average inventory holding costs subject to a constraint on the aggregate fill rate, which is a weighted average of the fill rates of the item types. The base-stock controlled policy that maximizes aggregate fill rate is numerically investigated, for both symmetric and asymmetric systems, and is shown to be optimal for minimizing base-stock investments under an aggregate fill rate constraint. Alternative policies are generated by heuristics in order to approximate the policy that maximizes aggregate fill rate and performances of these policies are compared to those of two well-known Longest Queue and First Come First Served policies. Also, optimal policy for the service model to minimize average inventory holding cost subject to an aggregate fill rate constraint is investigated without restricting the attention to only base-stock controlled dynamic scheduling policies. Based on the equivalence relations between this service model and the corresponding cost model, it is observed that the base-stock controlled policy that maximizes aggregate fill rate is almost the same as the solution to the service model and cost model under consideration, especially when backorder penalties are large in the cost model as compared to cost parameters for inventory holding or equivalently when the target fill rate is large in the service model.

We consider a single-part, two-echelon supply chain problem for spare parts. The network consists of a single manufacturing plant, a set of service centers (SCs) and a set of customers. Both echelons keep spare parts using the base-stock replenishment policy. The plant behaves as an M/M/1 queueing system and has limited production and storage capacity. Demand faced by each SC follows an independent Poisson process. The problem is to determine optimal location-allocation and optimal base-stock levels at both echelons while satisfying the target service levels and customer preferences of SCs. We develop a mixed integer non-linear programming model and use cutting-plane method to optimize the inventory-location decisions. We present an exact solution procedure for the inventory stocking problem and demonstrate the limitations of using traditional inventory models like METRIC-like and Approximate in case of high utilization rates. We show the effectiveness of our proposed cutting-plane algorithm and provide important managerial insights for spare parts management.

The implicit assumption in conventional inventory models is that the stored products maintain the same utility forever, i.e., they can be stored for an infinite period of time without losing their value or characteristics. However, generally speaking, almost all products experience some sort of deterioration over time. Some products have very small deterioration rates, and henceforth the effect of such deterioration can be neglected. Some products may be subject to significant rates of deterioration. Fruits, vegetables, drugs, alcohol and radioactive materials are examples that can experience significant deterioration during storage. Therefore the effect of deterioration must be explicitly taken into account in developing inventory models for such products. In most existing deteriorating inventory models, time is treated as a continuous variable, which is not exactly the case in practice. In real-life problems time factor is always measured on a discrete scale only, i.e. in terms of complete units of days, weeks, etc. In this research, we present several discrete-in-time inventory models and identify optimal ordering policies for a single deteriorating product by minimizing the expected overall costs over the planning horizon. The various conditions have been considered, e.g. periodic review, time-varying deterioration rate, waiting-time-dependent partial backlogging, time-dependent demand, stochastic demand etc. The objective of our research is two-fold: (a) To obtain optimal order quantity and useful insights for the inventory control of a single deteriorating product over a discrete time horizon with deterministic demand, variable deterioration rates and waiting-time-dependent partial backlogging ratios; (b) To identify optimal ordering policy for a single deteriorating product over a finite horizon with stochastic demand and partial backlogging. The explicit ordering policy will be developed for some special cases. Through computational experiments and sensitivity analysis, a thorough and insightful understanding of deteriorating inventory management will be achieved.