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Strategies used in the management of the supply chain dealing with change in the demand variability could have significant impact on the logistics cost. Demand variability would directly impact the selection of supply chain strategic solution and the calculated unit cost for the logistics system. The relationship between logistics strategies of inventory policy, transportation lot size, demand mean, and demand variance are examined to evaluate the impact on the performance of the supply chain behavior. Discrete-event simulation was developed to run eight scenarios with four factors at two levels. The results show that increasing the average demand decreases unit cost. Any increase in the demand variation results in an increase in unit cost with positive interaction effects with all other factors.

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... The interest rate is assumed 12% APR when calculating the carrying cost. The equations presented by Lee and Farahmand (2008) are as follows: ...

A logistics network management system controlling the entire supply chain was designed to reduce the total cost and to achieve an efficient system. The interactions between inventory and transportation strategies in the logistics network are presented in this paper. Demand volumes and shipping sizes were simulated as part of a new conceptual model by using a discrete event simulation to minimize the total cost in the supply chain. The experiments indicate that the Full Truckload scenario leads to cost-efficiency and the larger demand size results in smaller cost per unit based on economies of scale. Considering the interaction effects, the demand size has a greater impact on the cost reduction than the shipping size.

(This article originally appeared in Management Science, April 1997, Volume 43, Number 4, pp. 546–558, published by The Institute of Management Sciences.)
Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of “orders” tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and distortion tends to increase as one moves upstream—a phenomenon termed “bullwhip effect.” This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.

We introduce a distribution center (DC) location model that incorporates working inventory and safety stock inventory costs at the distribution centers. In addition, the model incorporates transport costs from the suppliers to the DCs that explicitly reflect economies of scale through the use of a fixed cost term. The model is formulated as a non-linear integer-programming problem. Model properties are outlined. A Lagrangian relaxation solution algorithm is proposed. By exploiting the structure of the problem we can find a low-order polynomial algorithm for the non-linear integer programming problem that must be solved in solving the Lagrangian relaxation subproblems. A number of heuristics are outlined for finding good feasible solutions. In addition, we describe two variable forcing rules that prove to be very effective at forcing candidate sites into and out of the solution. The algorithms are tested on problems with 88 and 150 retailers. Computation times are consistently below one minute and compare favorably with those of an earlier proposed set partitioning approach for this model (Shen, 2000; Shen, Coullard and Daskin, 2000). Finally, we discuss the sensitivity of the results to changes in key parameters including the fixed cost of placing orders. Significant reductions in these costs might be expected from e-commerce technologies. The model suggests that as these costs decrease it is optimal to locate additional facilities.

Ramasesh (1990) recast the traditional EOQ model into JIT purchasing concepts. In this scheme, be suggested that the ultimate form of JIT purchasing should be adopted in order to minimize the total cost through the implementation of frequent deliveries in small lots based on a buyer-vendor's long-term agreement. This paper shows that obtaining the optimal number of shipments at the infinite number of shipments is impractical. It is explained that, in fact, working with the contract quantity rather than the shipping quantity can make the outcome of the contract more feasible. This is because (1) no assumption is needed for the number of shipments; and (2) working with a large but finite quantity, i.e., the optimal contract quantity, is obviously better for setting up a long-term contract with the vendor than a smaller quantity, i.e., the optimal shipping quantity.

In 1990, Ramasesh recast the purchasing EOQ model and suggested that the ultimate form should be a JIT contract purchasing model in order to minimize cost. This study examines the model proposed by Ramasesh with the aim of clarifying the weakness of such model. Decision rules are also suggested to assist management in determining the suitability of switching to a JIT contract model in lieu of the more traditional EOQ model.

This paper studies supply chain demand variability in a model with one supplier and N retailers that face stochastic demand. Retailers implement scheduled ordering policies: Orders occur at fixed intervals and are equal to some multiple of a fixed batch size. A method is presented that exactly evaluates costs. Previous research demonstrates that the supplier's demand variance declines as the retailers' order intervals are balanced, i.e., the same number of retailers order each period. This research shows that the supplier's demand variance will (generally) decline as the retailers' order interval is lengthened or as their batch size is increased. Lower supplier demand variance can certainly lead to lower inventory at the supplier. This paper finds that reducing supplier demand variance with scheduled ordering policies can also lower total supply chain costs.

This article provides a model for the implementation of JIT concepts in purchasing systems that have not yet advanced to the ultimate level of JIT purchasing (in which the lot size is one and material from the suppliers is received directly into the buyer's production lines). The fixed costs associated with the adoption of a JIT concept should be treated as an investment and justified based on the savings generated using any of the well-known techniques of investment analysis. To maximize the savings, optimize the operating decisions so that the total cost of the inventory system is minimized. For this, recast the traditional EOQ model so that the costs of the small-lot shipments (which are a key feature of JIT purchasing) are explicitly included. Some easily defensible assumptions have been made to keep the mathematics simple and render the model easier to implement. Guidelines and formulas have been provided for determining the order quantity and the optimal number of shipments. An illustrative numerical example is provided to illustrate the use of the model in implementing the JIT purchasing concepts.

Examines the interdependence between facility location, transportation and inventory issues in a distribution network design problem. Management of inventories, determination of transportation policy, and location of plants and distribution centers are normally carried out by different groups of people in an organization. These activities interact, however, when the transportation is used to replace inventory, an increase in the number of warehouses increases total system inventory or location of warehouses would dictate the type of transportation mode choice or carrier that needs to be used. The proposed model, FLITNET, is expected to provide a more complete representation of the trade-offs that exist among the location, transportation and inventory cost components, and lead to an optimal solution.

In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. We study the value of sharing these data in a model with one supplier, N identical retailers, and stationary stochastic consumer demand. There are inventory holding costs and back-order penalty costs. We compare a traditional information policy that does not use shared information with a full information policy that does exploit shared information. In a numerical study we find that supply chain costs are 2.2% lower on average with the full information policy than with the traditional information policy, and the maximum difference is 12.1%. We also develop a simulation-based lower bound over all feasible policies. The cost difference between the traditional information policy and the lower bound is an upper bound on the value of information sharing: In the same study, that difference is 3.4% on average, and no more than 13.8%. We contrast the value of information sharing with two other benefits of information technology, faster and cheaper order processing, which lead to shorter lead times and smaller batch sizes, respectively. In our sample, cutting lead times nearly in half reduces costs by 21% on average, and cutting batches in half reduces costs by 22% on average. For the settings we study, we conclude that implementing information technology to accelerate and smooth the physical flow of goods through a supply chain is significantly more valuable than using information technology to expand the flow of information.

An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.