Knowledge that Transforms

Manufacturing 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.

We consider a single-period inventory model in which a risk-averse retailer faces uncertain customer demand and makes a purchasing-order-quantity and a selling-price decision with the objective of maximizing expected utility. This problem is similar to the classic newsvendor problem, except: (a) the distribution of demand is a function of the selling price, which is determined by the retailer; and (b) the objective of the retailer is to maximize his/her expected utility. We consider two different ways in which price affects the distribution of demand. In the first model, we assume that a change in price affects the scale of the distribution. In the second model, a change in price only affects the location of the distribution. We present methodology by which this problem with two decision variables can be simplified by reducing it to a problem in a single variable. We show that in comparison to a risk-neutral retailer, a risk-averse retailer in the first model will charge a higher price and order less; where as, in the second model a risk-averse retailer will charge a lower price. The implications of these findings for supply-chain strategy and channel design are discussed. Our research provides a better understanding of retailers' pricing behavior that could lead to improved price contracts and channel-management policies.

This paper studies a distribution system in which a manufacturer supplies a common product to two independent retailers, who in turn use service as well as retail price to directly compete for end customers. We examine the drivers of each firm's strategy, and the consequences for total sales, market share, and profitability. We show that the relative intensity of competition with respect to each competitive dimension plays a key role, as does the degree of cooperation between the retailers. We discover a number of insights concerning the preferences of each party regarding competition. For instance, there will be circumstances under which both retailers would prefer an increase in competitive intensity. Our analysis generalizes existing knowledge about manufacturer wholesale pricing strategies, and rationalizes behaviors that would not be evident without both price and service competition. Finally, we characterize the structure of wholesale pricing mechanisms that can coordinate the system, and show that the most commonly used formats (those that are linear in the order quantity) can achieve coordination only under very limiting conditions.

For most firms, new product development is the engine for growth and profitability. A firm's new product success depends on its ability to manage the product development process in a way that employs scarce resources to achieve the goal of the firm as well as the specific project's objectives. Simple and measurable performance metrics have been proposed and applied to monitor and compensate the development teams. In this paper, we develop a modeling frame work to analyze the implications of setting managerial priorities for three commonly used new product performance metrics: (1) time-to-market, (2) product performance, and (3) total development cost. We model new product development as a “product performance production” process that requires scarce development resources. Setting a target for development teams for each of these performance metrics can constrain this performance production process and, thereby, affect the other performance metrics. We model the constrained process as a restricted case of a general process that does not have such constraints. We benchmark each constrained process against the optimal, unrestricted process with respect to the level of the resource intensity employed during the development process, the time-to-market, and the performance level of the new product at launch. We show that an overly ambitious time-to-market target leads to an upward bias in resource intensity usage and a downward bias in product performance (i.e., evolutionary product innovation). In addition, our results suggest that the target time-to-market approach may ignore the effect of cannibalization and, thus, can perform suboptimally if a significant degree of cannibalization in the existing product market is expected. Given a target product performance, we show that the coordination between marketing and R&D is easier because the resulting development resource intensity and time-to-market decisions becomes separable. However, an overly ambitious product performance target leads to an upward bias in the development resource intensity and a delayed product launch that misses the window of opportunity. Finally, we show that the target development cost approach can lead a downward bias in product performance and a premature product launch. The above analyses are performed for a monopolistic firm, and they are extended to passive and active competitive environment.

The decentralized supply chain management scheme of Lee and Whang (1999) can be viewed as operationalizing the decentralized management scheme implicit in Clark and Scarf (1960). This paper proposes the use of what are called responsibility tokens (RTs) to further facilitate that operationalization. The proposal assumes that a management information system, presumably electronic, is established to monitor inventories and shipment quantities, and to carry out transfer payments between players. As in Lee and Whang(1999), the incentives of the system are aligned, so if each player is brilliantly self-serving, the system optimal solution will result. While the system administrator need not know how the system should be managed, the most upstream player must know how to manage the system optimally for the system optimal solution to be achieved. RTs endow the system with an attractive self-correcting property: An example illustrates that upstream players are given a mechanism and the incentive to correct for downstream overordering. The downstream players who over-order are penalized, but system performance is not degraded much. Extensions and further research are also discussed.

We consider a stochastic, capacitated production-inventory model in which the customer provides information about the expected timing of future orders to the supplier. We allow for randomness in customer order arrivals as well as the quantity demanded, but work under the assumption that the customer is making every effort to follow the schedule provided. We term this as a target reverting policy. This gives rise to an interesting nonstationary inventory control model at the supplier. After characterizing the optimal policy, we develop solution procedures to compute the optimal parameters. An extensive computational study provides insights into the behavior of this model at optimality. Further, comparing the cost of the optimal policy to the cost of simple policies that either ignore the customer's information or the capacity constraint, we are able to provide insights as to when these simplifications could be costly.

The bottleneck in a production-inventory network is commonly taken to be the facility that most limits flow through the network and thus the most highly utilized facility. A further connotation of “bottleneck,” however, is the facility that most constrains system-wide performance or the facility at which additional resources would have the greatest impact. Adopting this broader sense of the term, we look for fill-rate bottlenecks: facilities in a production-inventory network that most constrain the system-wide fill rate (the proportion of demands filled within a fixed delivery leadtime) or facilities at which either additional production capacity or additional inventory would have the greatest impact on the fill rate. We consider systems in which various components are produced through a series of stages holding intermediate inventories and are then assembled into finished goods to meet external demands. With each station in the network we associate precise measures of the station's propensity to constrain the fill rate. We call a station with a minimal measure a fill-rate bottleneck and justify this label both theoretically and numerically. Examples show that even the least utilized facility can be a fill-rate bottleneck. Unlike utilization, our bottleneck criteria capture information about process variability.

The publication of Volume 1, Number 1 of M&SOM marks the end of a process that began more than three years ago: a process whose goal was to create the premier journal in operations management. I will leave it to you, the reader of 1:1, to decide whether or not we have been successful “right out of the blocks” or if more issues must be published before we can rightfully claim to have succeeded. But we will suceed. I am committed to it. So are the distinguished group of senior editors and the esteemed members of M&SOM's Editorial Review Board, whose names are, and will remain, prominently featured in every issue. And most important, so are the authors who have submitted their manuscripts to us.

The relentless pursuit of increased product quality via continuous improvement is an important long-term strategy for achieving competitive advantage. However, manufacturers must still achieve high product quality in the short run. Hence, short-run quality improvement strategies are necessary, and, if possible, should complement (rather than substitute for) a longer run continuous improvement strategy with suppliers. We propose a novel short-run quality improvement strategy with suppliers which is based on a combinatorial optimization model for determining the optimal matching of raw material batches (from suppliers) with process variable parameters (of the manufacturer) by exploiting their interaction effects. Although the model can be difficult to solve, we develop sufficient conditions for a special case where the attainment of an optimal solution is straightforward. We demonstrate the use of the proposed approach in an actual pharmaceutical process. Using data from the pharmaceutical case, we develop several simulation scenarios where matching is shown (predicted) to greatly improve process yield. We also discuss the use of matching to illuminate various strategies for long-term continuous improvement of supplier and manufacturer processes in general.

We study the problem of allocating stocked fibers to made-to-order cables with the goals of satisfying due dates and reducing the costs of scrap, setup, and fiber circulation. These goals are achieved by generating remnant fibers either long enough to satisfy future orders or short enough to scrap with little waste. They are also achieved by manufacturing concatenations, in which multiple cable orders are satisfied by the production of a single cable that is afterwards cut into the constituent cables ordered. We use a function that values fibers according to length, and which can be viewed as an approximation to the optimal value function of an underlying dynamic programming problem. The daily policy that arises under this approximation is an integer program with a simple linear objective function that uses changes in fiber value to take into account the multi-period consequences of decisions. We describe our successful implementation of this integer program in the factory, summarizing our computational experience as well as realized operational improvements.