COORDINATION ALTERNATIVES IN A MANUFACTURER/DEALER INVENTORY SYSTEM UNDER STOCHASTIC DEMAND

This paper introduces a stochastic model of a distribution system where the stocking location is owned by a dealer (or retailer) and the product is supplied by a manufacturer. Inventory is managed by the dealer, and the manufacturer is responsible for delivery of the product through both regular replenishment and expedite shipment modes. The dealer and the manufacturer share the goal of providing a high level of customer service. Demand, moreover, is a function of the service level offered to the market by the dealer. We develop optimal stock control policies for the cases where each decision maker in turn is dominant and acts unilaterally while being constrained by the supply/demand linkages of the system. We also develop an optimum policy for the case where both levels are managed under centralized control (i.e., both levels cooperate). Results indicate that the expected profit for a dominant dealer (or dominant manufacturer) is higher under decentralized control than the optimal solution for either under centralized control. However, the centralized solution is a global-optimal solution and therefore will guarantee longterm stability. Differences between the various solutions are analyzed explicitly to estimate the cost of coordination.     

INVENTORY RATIONING AND SHIPMENT FLEXIBILITY ALTERNATIVES FOR DIRECT MARKET FIRMS

This paper investigates inventory‐rationing policies of interest to firms operating in a direct market channel. We model a single product with two demand classes, where one class requests a lower order fulfillment lead time but pays a higher price. Demand for each class follows a Poisson process. Inventory is fed by a production system with exponentially distributed build times. We study rationing policies in which the firm either blocks or backlogs orders for the lower priority customers when inventory drops below a certain level. We compare the performance of these rationing policies with a pure first‐come, first‐serve policy under various scenarios for customer response to delay: lost sales, backlog, and a combination of lost sales and backlog.     

OPTIMAL AND NEAR OPTIMAL CONTROL OF A TWO-PART-TYPE STOCHASTIC MANUFACTURING SYSTEM WITH DYNAMIC SETUPS

This paper deals with a manufacturing system consisting of a single machine subject to random failures and repairs. The machine can produce two types of parts. When the production is switched from one part type to the other, a random setup time is incurred at a constant cost rate. The objective is to track the demand, while keeping the work-in-process as close as possible to zero for both products. The problem is formulated as an optimal stochastic control problem. The optimal policy is obtained numerically by discretizing the continuous time continuous state opti-mality conditions using a Markov chain approximation technique. The discretized optimality conditions are shown to correspond to an infinite horizon, discrete time, discrete state dynamic programming problem. The optimal setup policy is shown to have two different structures depending on the parameters of the system. A heuristic policy is proposed to approximate the optimal setup policy. Simulation results show that the heuristic policy is a very good approximation for sufficiently reliable systems.     

INVESTMENT IN SETUP COST,LEAD TIME,AND DEMAND PREDICTABILITY IMPROVEMENT IN THE EOQ MODEL

We study an economic order quantity/reorder point (EOQ/ROP) model with stochastic demand and backorders where options of investing in reducing setup cost, lead time, and variance of demand forecast errors are available. The model is quite comprehensive relative to previous models since it simultaneously addresses the strategic decisions associated with these three investment opportunities as well as the tactical decisions of determining both the lot size and the safety stock. We develop a simple search procedure to obtain the optimal values of setup cost, lead time, variance of demand forecast errors, order quantity, and safety stock multiplier. Computational studies are performed to determine the sensitivity of the optimal solution of the model to changes in the model's parameters.     

STOCK LEVELS AND DELIVERY RATES IN VENDORMANAGED INVENTORY PROGRAMS

Using the latest information technology, powerful retailers like Wal‐Mart have taken the lead in forging shorter replenishment‐cycles, automated supply systems with suppliers. With the objective to reduce cost, these retailers are directing suppliers to take full responsibility for managing stocks and deliveries. Suppliers' performance is measured according to how often inventory is shipped to the retailer, and how often customers are unable to purchase the product because it is out of stock. This emerging trend also implies that suppliers are absorbing a large part of the inventory and delivery costs and, therefore, must plan delivery programs including delivery frequency to ensure that the inherent costs are minimized. With the idea to incorporate this shift in focus, this paper looks at the problem facing the supplier who wants quicker replenishment at lower cost. In particular, we present a model that seeks the best trade‐off among inventory investment, delivery rates, and permitting shortages to occur, given some random demand pattern for the product. The process generating demand consists of two components: one is deterministic and the other is random. The random part is assumed to follow a compound Poisson process. Furthermore, we assume that the supplier may fail to meet uniform shipping schedules, and, therefore, uncertainty is present in delivery times. The solution to this transportationinventory problem requires determining jointly delivery rates and stock levels that will minimize transportation, inventory, and shortage costs. Several numerical results are presented to give a feel of the optimal policy's general behavior.     

COMPLEX MULTIECHELON INVENTORY SYSTEM MANAGEMENT USING A DYNAMIC SIMULATION MODEL

A dynamic modeling approach to management of multiechelon, multi-indenture inventory systems with repair is addressed. The structure of the model follows that of the U.S. Air Force Reparable Asset Management System. The model is used as a vehicle to discuss the structure of typical multiechelon systems and to illustrate the advantages of a dynamic modeling approach to such systems.     

QUANTIFYING THE IMPACT OF INVENTORY HOLDING COST AND REACTIVE CAPACITY ON AN APPAREL MANUFACTURER'S PROFITABILITY

This paper was motivated by the operational problems faced by Northco, a school uniform manufacturer in the Northeastern United States. Northco was facing high working capital costs while also incurring high stockout and markdown costs. This paper models the impact of inventory holding cost and reactive capacity on Northco's targeted understocking and overstocking cost and offers a solution methodology for such problems. We quantify the impact of varying inventory carrying costs (and hence, high working capital costs) on stockout costs and the value of additional capacity. Our results illustrate that apparel manufacturers with high working capital costs, and hence high inventory carrying costs, should target higher stockout costs and achieve lower capacity utilization. The results presented have application beyond Northco because high working capital cost is endemic to many supply chains.     

THE EMPIRICAL DETERMINANTS OF INVENTORY LEVELS IN HIGH-VOLUME MANUFACTURING

This study uses survey data on several hundred automotive suppliers in North America to evaluate the determinants of inventory levels in high-volume discrete parts manufacturing. We assess the magnitude of raw materials, work-in-process, and finished goods inventories held at automotive supply plants. Inventories are shown to be jointly determined by technological and managerial factors in a manner roughly consistent with classical inventory theory. Several categories of managerial practices are found to be important. Low inventories are linked to employee problem solving and frequent communication with customers. More unexpectedly, we find the absence of inventory differences between U.S.-owned and Japanese-owned plants in North America. This contrasts with substantial differences in inventory holding between US plants and those in Japan.     

THE VALUE OF SKU RATIONALIZATION IN PRACTICE (THE POOLING EFFECT UNDER SUBOPTIMAL INVENTORY POLICIES AND NONNORMAL DEMAND)

Several approaches to the widely recognized challenge of managing product variety rely on the pooling effect. Pooling can be accomplished through the reduction of the number of products or stock‐keeping units (SKUs), through postponement of differentiation, or in other ways. These approaches are well known and becoming widely applied in practice. However, theoretical analyses of the pooling effect assume an optimal inventory policy before pooling and after pooling, and, in most cases, that demand is normally distributed. In this article, we address the effect of nonoptimal inventory policies and the effect of nonnormally distributed demand on the value of pooling. First, we show that there is always a range of current inventory levels within which pooling is better and beyond which optimizing inventory policy is better. We also find that the value of pooling may be negative when the inventory policy in use is suboptimal. Second, we use extensive Monte Carlo simulation to examine the value of pooling for nonnormal demand distributions. We find that the value of pooling varies relatively little across the distributions we used, but that it varies considerably with the concentration of uncertainty. We also find that the ranges within which pooling is preferred over optimizing inventory policy generally are quite wide but vary considerably across distributions. Together, this indicates that the value of pooling under an optimal inventory policy is robust across distributions, but that its sensitivity to suboptimal policies is not. Third, we use a set of real (and highly erratic) demand data to analyze the benefits of pooling under optimal and suboptimal policies and nonnormal demand with a high number of SKUs. With our specific but highly nonnormal demand data, we find that pooling is beneficial and robust to suboptimal policies. Altogether, this study provides deeper theoretical, numerical, and empirical understanding of the value of pooling.     

PRICE‐DEPENDENT INVENTORY MODELS WITH DISCOUNT OFFERS AT RANDOM TIMES*

We consider an inventory model with a supplier offering discounts to a reseller at random epochs. The offer is accepted when the inventory position is lower than a threshold level. We compare three different pricing policies in which demand is induced by the resellers price variation. Policy 1 is the EOQ policy without discount offers. Policy 2 is a uniform price, stock‐independent policy. Policy 3 is a stock level‐dependent, discriminated price policy. Assuming constant demand rates, expressions are obtained for the optimal order quantities, prices, and profits. The numerical experiments show that if it is better to accept a suppliers discount, then it benefits the reseller to transfer the discount to downstream customers.     

POWER APPROXIMATIONS FOR A TWO-ECHELON INVENTORY SYSTEM USING SERVICE LEVELS

We consider a two-echelon inventory system with one warehouse and several stores. The warehouse as well as the stores are controlled by periodic review (s, S) inventory policies. We study the interrelationship between the safety stocks at the warehouse and the stores. Stockouts at the warehouse will result in supply delays to the stores and cause the lead time to be stochastic. The stores may react by increasing their safety stock. However, there is a trade-off between the safety stock at the warehouse and the safety stock at the stores. We use a service level at the warehouse to quantify the effect of warehouse stockouts on the lead time to the stores. The service level at the warehouse is considered a decision variable to find the best compromise between the various safety stocks by minimizing the overall costs. Using power approximations for the (s, S) policies, we provide an iterative procedure for adjusting the lead time distribution to the stores; this can result in substantial savings, but it doesn't guarantee the overall optimality. Numerical studies are provided to test the accuracy of approximations. The effects of the different system parameters on the inventory policy give general guidelines for use of the policies.     

A COMPARISON OF PRODUCTION SCHEDULING POLICIES ON COSTS,SERVICE LEVEL,AND SCHEDULE CHANGES

We consider a single product, single level, stochastic master production scheduling (Mps ) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the Mps control system: the method used in determining the amount of stock planned for production in each time period. Typically, Mps control systems utilize a single buffer stock. Here, two Mps dual-buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (dea ) methodology to aid in the evaluation of the simulation results, where Dea serves to increase the scope of the experimental design. Results indicate that the dual-buffer control systems outperform existing policies.     

OPTIMALITY OF STATE-DEPENDENT (s,S) POLICIES IN INVENTORY MODELS WITH MARKOV-MODULATED DEMAND AND LOST SALES

Markov-modulated processes have been used in stochastic inventory models with setup costs for modeling demand under the influence of uncertain environmental factors, such as fluctuating economic and market conditions. The analyses of these models have been carried out in the literature only under the assumption that unsatisfied demand is fully backlogged. The lost sales situation occurs in many retail establishments such as department stores and supermarkets. We use the analysis of the Markovian demand model with backlogging to analyze the lost sales case; in particular, we establish the optimality of an (s, S)-type policy under fairly general conditions.     

PRODUCTION FLOW CONTROL FOR A MANUFACTURING SYSTEM WITH FLEXIBLE ROUTINGS

We study the dynamic routing problem for a flexible manufacturing system consisting of two unreliable machines and a finite buffer. One product-type is produced which requires two operations in sequence. The demand rate is assumed to be constant. Each machine is capable of performing both operations. The objective is to trace the demand while keeping the work-in-process low and the cycle-time short. An optimal control formulation is established for the dynamic routing problem. A production flow control algorithm is developed based on a combination of mathematical modeling and heuristics. The control policy is simulated and a comparison with the numerical optimal solution shows that it performs well for the instances under consideration.     

A REVIEW OF THE KANBAN PRODUCTION CONTROL RESEARCH LITERATURE

Station interdependence, blocking caused by finite buffer capacities, and periodic material handling make modeling and analysis of kanban-controlled lines challenging. Also, one must consider flows of material as well as flows of kanbans. The many models given in the literature contribute to the confusion and debate that often characterize kanban research. The only element common to all kanban systems appears to be finite buffer capacities. I describe blocking by total queue size, blocking by part type, and the single-card and twocard systems. I review the kanban literature and organize it by type of system and decision area. First, I discuss elements of system design, including setting kanban numbers, performance measures, material-handling frequencies, and container sizes. Then I cover the production control topics of sequencing and batch-sizing. I conclude with a comparison of kanban and conventional methods of production control and with suggestions for future research.     

JOB RELEASE CONTROL USING A CYCLIC SCHEDULE*

In just‐in‐time inventory management in any manufacturing setting, the general idea has been to release jobs as late as possible (to reduce inventory costs) while still having them arrive at bottleneck machines in time to maintain the desired throughput (by not starving a bottleneck machine). When a cyclic schedule is employed, the throughput is determined by a cyclic sequence of operations known as the cyclic critical path. These operations are not, in general, all performed on a single bottleneck machine. We present an algorithm for releasing jobs that treats this cyclic critical path as the bottleneck. Although this algorithm has the somewhat complex task of not delaying any of these operations on the cyclic critical path, it is greatly simplified by being able to take advantage of the fixed sequence of the cyclic schedule. The result is that the algorithm is relatively simple to implement. Although it uses a simulation‐based analysis, this analysis can all be done and the necessary results stored in advance of its use. We test the algorithm in a job shop environment with stochastic operation times. This algorithm is shown to be effective at reducing inventory while avoiding decreases in throughput.     

A NEW PARADIGM FOR TEACHING INTRODUCTORY PRODUCTION/OPERATIONS MANAGEMENT

This paper postulates that the perception among students that operations management is a tedious, irrelevant subject is a symptom of the lack of a conceptual framework that effectively communicates the importance and relevance of the operations function in a firm. The first half of this paper discusses traditional frameworks, those most frequently found in introductory production and operations management (pom) textbooks, and several alternative approaches to teaching an introductory POM course. The discussion questions whether any of these existing frameworks is sufficient to meet the challenges faced while teaching POM in today's environment and identifies what characteristics an effective framework should possess. These characteristics include defining the scope and bounds of the field, capturing its integrative and system aspects, providing a visible depiction of the framework to aid comprehension, and promoting higher-level thinking (i.e., analysis, synthesis, and criticism) to deepen understanding. This discussion concludes that no current framework meets all of these criteria, therefore there remains a need for a more effective approach for introducing POM. The remainder of the paper describes one approach to conceptualizing the field that satisfies these criteria.     

ROBUSTNESS OF VARIOUS PRODUCTION CONTROL POLICIES IN SEMICONDUCTOR MANUFACTURING

This paper compares several different production control policies in terms of their robustness to random disturbances such as machine failures, demand fluctuations, and system parameter changes. Simulation models based on VLSI wafer fabrication facilities are utilized to test the performance of the policies. Three different criteria, namely, the average total WIP, the average backlog, and a cost function combining these measures, are used to evaluate performance. Among the policies tested, the Two‐Boundary Control policy outperforms the others.     

SUPPLY CONTRACTS WITH PERIODIC,STATIONARY COMMITMENT

The purpose of this paper is to model and analyze supply contracts with periodical commitment, in which the order quantities are fixed and stationary, with limited flexibility to change the order quantity at a cost to the buyer. A solution methodology is provided for the general, stochastic problem, and consideration is given to specific demand distributions. The deterministic model is also investigated, formulating the problem as a mixed‐integer linear program and as a network flow problem. Computational analyses are conducted, and the extension of the basic problem to the multiple‐product, multiple‐constraint problem is discussed.