Parameters Affecting the Efficacy and Relative Location of a Distribution Center in a Supply Chain with Fixed Replen-ishment Intervals (original) (raw)

Design of supply networks with optimized safety stock levels

International Journal of Engineering, Science and Technology, 2013

In this paper, we address two main issues. First, we determine, through a simulation model, the optimal size and distribution of the safety stocks in a supply network. The "optimal" size of safety stocks results from the minimization of the total logistics cost of the supply network, as a function of the safety stock coefficient (k). In particular, we define the "optimal" size of the safety stocks as the k value which minimizes the total logistics cost of the network. Second, the optimized values of k are used to run the same simulation model under different operating conditions of the network, which are obtained by introducing demand stochasticity, demand seasonality and lead time stochasticity. More precisely, once the optimal size of safety stocks has been set, we carry out further simulations, according to the design of experiments (DOE) procedure, and perform statistical analyses of the resulting outputs, to provide some insights about the design of the supply network under optimal safety stock level. The study is supported by a discrete-event simulation model, developed ad hoc and reproducing 4 different configurations of a fast moving consumer goods (FMCG) network. To run the model, real data related to the FMCG context were used. Results of this study can be useful to supply chain managers, to identify the optimal service level the network should deliver to customers, as well as to understand the behavior of supply networks under optimal safety stock level. Keywords: safety stock; supply network design; simulation model; design of experiments; optimization. DOI: http://dx.doi.org/10.4314/ijest.v5i2.7S

Coordinated Replenishment Strategies in Inventory/Distribution Systems

Management Science, 2007

In this paper, we study the impact of coordinated replenishment and shipment in inventory/distribution systems. We analyze a system with multiple retailers and one outside supplier. Random demand occurs at each retailer, and the supplier replenishes all the retailers. In traditional inventory models, each retailer orders directly from the supplier whenever the need arises. We present a new, centralized ordering policy that orders for all retailers simultaneously. The new policy is equivalent to the introduction of a warehouse with no inventory, which is in charge of the ordering, allocation, and distribution of inventory to the retailers. Under such a policy, orders for some retailers may be postponed or expedited so that they can be batched with other retailers' orders, which results in savings in ordering and shipping costs. In addition to the policy we propose for supplying inventory to the retailers, we also consider three other policies that are based on these well known policies in the literature: (a) Can-Order policy, (b) Echelon Inventory policy, and (c) Fixed Replenishment Interval policy. Furthermore, we create a framework for simultaneously making inventory and transportation decisions by incorporating the transportation costs (or limited truck capacities). We numerically compare the performance of our proposed policy with these policies to identify the settings where each policy would perform well.

Coordinated Replenishment and Shipping Strategies in Inventory/Distribution Systems

2005

In this paper, we study the impact of coordinated replenishment and shipment in inventory/distribution systems. We analyze a system with multiple retailers and one outside supplier. Random demand occurs at each retailer, and the supplier replenishes all the retailers. In traditional inventory models, each retailer orders directly from the supplier whenever the need arises. We present a new, centralized ordering policy that orders for all retailers simultaneously. The new policy is equivalent to the introduction of a warehouse with no inventory, which is in charge of the ordering, allocation, and distribution of inventory to the retailers. Under such a policy, orders for some retailers may be postponed or expedited so that they can be batched with other retailers' orders, which results in savings in ordering and shipping costs. In addition to the policy we propose for supplying inventory to the retailers, we also consider three other policies that are based on these well known policies in the literature: (a) Can-Order policy, (b) Echelon Inventory policy, and (c) Fixed Replenishment Interval policy. Furthermore, we create a framework for simultaneously making inventory and transportation decisions by incorporating the transportation costs (or limited truck capacities). We numerically compare the performance of our proposed policy with these policies to identify the settings where each policy would perform well.

Pooling in multi-location periodic inventory distribution systems

Omega, 1999

Risk pooling through lateral transshipment in inventory distribution systems is an eective means of improving customer service and reducing total system costs. The objective of this paper is to study the performance of an inventory system with one central warehouse and multiple retail outlets, collaborating in the case of imminent shortage by moving inventory between them. The analysis concentrates on the case of three outlets (stocking locations), which captures most of the characteristics and tradeos of multi-location systems with complete pooling. In addition to determining order-up-to quantities for the stocking locations, the decision maker must also specify the details of the transshipment policy when one or two locations face shortages. Simulation with a wide choice of model parameters leads to some very interesting and practically useful conclusions, including the following: (a) the bene®ts of risk pooling through transshipment are substantial and increase with the number of pooled locations; (b) the type of transshipment policy in case of shortages does not aect signi®cantly the system's performance; and (c) it is preferable to form``balanced'' pooling groups, consisting of locations that face similar demand.

Supply chain system design integrated with risk pooling q

This paper considers the location, production–distribution and inventory system design model for supply chain for determining facility locations and their capacity. Risk pooling effect, for both safety stock and running inventory (RI), have been incorporated in the system to minimize the supply chain cost along with determining facility location and capacity. In order to study the benefit of risk pooling for safety stock and RI two cases have been considered, first when retailers act independently and second when DCs-retailers work jointly. The model is formulated as mixed integer nonlinear problem and divided into two stages. The first stage determines the optimal locations for plants and flow relation between plants-DCs and DCs-retailers. At this stage the problem has been linearized using piece-wise linear function. Second stage enumerates the required capacity of opened plants and DCs. The first stage problem is further divided in two sub-problems using Lagrangean relaxation. First sub-problem determines the flow relation between plants and DCs whereas; second sub-problem determines the DCs-retailers flow. Solution of the sub-problems provides the lower bound for the main problem. Computational results reveal that main problem is within the 8.25% of the lower bound and significant amount of cost reduction can be achieved for safety stock and RI costs when DC-Retailer acts jointly. Crown

Policies for inventory/distribution systems: The effect of centralization vs. decentralization

International Journal of Production Economics, 2003

This paper concerns with a multi-echelon inventory/distribution system considering one-warehouse and N-retailers. The retailers are replenished from the warehouse. We assume that the demand rate at each retailer is known. The problem consists of determining the optimal reorder policy which minimizes the overall cost, that is, the sum of the holding and replenishment costs. Shortages are not allowed and lead times are negligible. We study two situations: when the retailers make decisions independently and when the retailers are branches of the same firm. Solution methods to determine near-optimal policies in both cases are provided. Computational results on several randomly generated problems are reported.

Managing supply chain inventories: A multiple retailer, one warehouse, multiple supplier model

Inventories exist throughout the supply chain in various forms for various reasons. Since carrying these inventories can cost anywhere from 20 to 40% of their value a year, managing them in a scientific manner to maintain minimal levels makes economic sense. This paper presents a near-optimal (s, Q)-type inventory policy for a production/distribution network with multiple suppliers replenishing a central warehouse, which in turn distributes to a large number of retailers. The model is a synthesis of three components: (i) the inventory analysis at the retailers, (ii) the demand process at the warehouse, and (iii) the inventory analysis at the warehouse. The key contribution of the model is the seamless integration of the three components to analyze simple supply chains. The decisions in the model were made through a comprehensive distribution-based cost framework that includes the inventory, transportation, and transit components of the supply chain.

Positioning inventory in distribution systems

International Journal of Production Economics, 1996

This article reports on a carefully controlled simulation experiment to determine whether the inventories in a distribution system should be positioned at a warehouse or a retail store closer to the customer. For companies that must fill customer demand from inventory, the results indicate that locating the inventory close to the customer is the best choice. The results also suggest that many of the anomalies in the previous research on this problem might be explained by the lack of control of the frequency of shipments.

Optimizing Strategic Safety Stock Placement in Supply Chains

Manufacturing & Service Operations Management, 2000

M anufacturing managers face increasing pressure to reduce inventories across the supply chain. However, in complex supply chains, it is not always obvious where to hold safety stock to minimize inventory costs and provide a high level of service to the final customer. In this paper we develop a framework for modeling strategic safety stock in a supply chain that is subject to demand or forecast uncertainty. Key assumptions are that we can model the supply chain as a network, that each stage in the supply chain operates with a periodic-review base-stock policy, that demand is bounded, and that there is a guaranteed service time between every stage and its customers. We develop an optimization algorithm for the placement of strategic safety stock for supply chains that can be modeled as spanning trees. Our assumptions allow us to capture the stochastic nature of the problem and formulate it as a deterministic optimization. As a partial validation of the model, we describe its successful application by product flow teams at Eastman Kodak. We discuss how these flow teams have used the model to reduce finished goods inventory, target cycle time reduction efforts, and determine component inventories. We conclude with a list of needs to enhance the utility of the model.

Integrated Production-Inventory-Distribution System Design with Risk Pooling: Model Formulation and Heuristic Solution

Transportation Science, 2007

ecelebi@uwaterloo.ca, elhedhli@uwaterloo.ca, emjewkes@uwaterloo.ca} I n this paper, we consider a multiproduct two-echelon production-inventory-distribution system design model that captures risk-pooling effects by consolidating the safety-stock inventory of the retailers at distribution centers (DCs). We propose a model that determines plant and DC locations, shipment levels from plants to the DCs, safety-stock levels at DCs, and the assignment of retailers to DCs by minimizing the sum of fixed facility location costs, transportation costs, and safety-stock costs. The model is formulated as a nonlinear mixed-integer programming problem and linearized using piecewise-linear functions. The formulation is strengthened using redundant constraints. Lagrangean relaxation is applied to decompose the problem by echelon. A lower bound is provided by the Lagrangean relaxation, while a heuristic is proposed that uses the solution of the subproblems to construct an overall feasible solution. Computational results reveal that the Lagrangean relaxation provides a sharp lower bound and a heuristic solution that is within 5% of the optimal solution.