Knowledge that Transforms

We address the problem of determining the optimal retailer order quantities from a manufacturer who makes new products in conjunction with ordering remanufactured products from a remanufacturer using used and unsold products from the previous product generation. Specifically, we determine the optimal order quantity by the retailer for four systems of decision‐making: (a) the three firms make their decisions in a coordinated fashion, (b) the retailer acts independently while the manufacturer and remanufacturer coordinate their decisions, (c) the remanufacturer acts independently while the retailer and manufacturer coordinate their decisions, and (d) all three firms act independently. We model the four options described above as centralized or decentralized decision‐making systems with the manufacturer being the Stackelberg leader and provide insights into the optimal order quantities. Coordination mechanisms are then provided which enable the different players to achieve jointly the equivalent profits in a coordinated channel.

This paper proposes simple methods for staffing a single‐class call center with uncertain arrival rate and uncertain staffing due to employee absenteeism. The arrival rate and the proportion of servers present are treated as random variables. The basic model is a multi‐server queue with customer abandonment, allowing non‐exponential service‐time and time‐to‐abandon distributions. The goal is to maximize the expected net return, given throughput benefit and server, customer‐abandonment and customer‐waiting costs, but attention is also given to the standard deviation of the return. The approach is to approximate the performance and the net return, conditional on the random model‐parameter vector, and then uncondition to get the desired results. Two recently‐developed approximations are used for the conditional performance measures: first, a deterministic fluid approximation and, second, a numerical algorithm based on a purely Markovian birth‐and‐death model, having state‐dependent death rates.

We study the effect of financial risk on the economic evaluation of a project with capacity decisions. Capacity decisions have an important effect on the project̂s value through the up‐front investment, the associated operating cost, and constraints on output. However, increased scale also affects the financial risk of the project through its effect on the operating leverage of the investment. Although it has long been recognized in the finance literature that operating leverage affects project risk, this result has not been incorporated in the operations management literature when evaluating projects. We study the decision problem of a firm that must choose project scale. Future cash flow uncertainty is introduced by uncertain future market prices. The firm's capacity decision affects the firm's potential sales, its expected price for output, and its costs. We study the firm's profit maximizing scale decision using the CAPM model for risk adjustment. Our results include that project risk, as measured by the required rate of return, is related to the inverse of the expected profit per unit sold. We also show that project risk is related to the scale choice. In contrast, in traditional discounted cash flow analysis (DCF), a fixed prescribed rate is used to evaluate the project and choose its scale. When a fixed rate is used with DCF, a manager will ignore the effect of scale on risk and choose suboptimal capacity that reduces project value. S/he will also misestimate project value. Use of DCF for choosing scale is studied for two special cases. It is shown that if the manager is directed to use a prescribed discount rate that induces the optimal scale decision, then the manager will greatly undervalue the project. In contrast, if the discount rate is set to the risk of the optimally‐scaled project, the manager will undersize the project by a small amount, and slightly undervalue the project with the economic impact of the error being small. These results underline the importance of understanding the source of financial risk in projects where risk is endogenous to the project design.

This paper describes a structured methodology for decomposing the conceptual design problem in order to facilitate the design process and result in improved conceptual designs that better satisfy the original customer requirements. The axiomatic decomposition for conceptual design method combines Alexander's network partitioning formulation of the design problem with Suh's Independence Axiom. The axiomatic decomposition method uses a cross‐domain approach in a House of Quality context to estimate the interactions among the functional requirements that are derived from a qualitative assessment of customer requirements. These interactions are used in several objective functions that serve as criteria for decomposing the design network. A new network partitioning algorithm is effective in creating partitions that maximize the within‐partition interactions and minimize the between‐partition interactions with appropriate weightings. The viability, usability, and value of the axiomatic decomposition method were examined through analytic comparisons and qualitative assessments of its application. The new method was examined using students in engineering design capstone courses and it was found to be useable and did produce better product designs that met the customer requirements. The student‐based assessment revealed that the process would be more effective with individuals having design experience. In a subsequent assessment with practicing industrial designers, it was found that the new method did facilitate the development of better designs. An important observation was the need for limits on partition size (maximum of four functional requirements.) Another issue identified for future research was the need for a means to identify the appropriate starting partition for initiating the design.

We consider the problem of managing demand risk in tactical supply chain planning for a particular global consumer electronics company. The company follows a deterministic replenishment‐and‐planning process despite considerable demand uncertainty. As a possible way to formally address uncertainty, we provide two risk measures, “demand‐at‐risk” (DaR) and “inventory‐at‐risk” (IaR) and two linear programming models to help manage demand uncertainty. The first model is deterministic and can be used to allocate the replenishment schedule from the plants among the customers as per the existing process. The other model is stochastic and can be used to determine the “ideal” replenishment request from the plants under demand uncertainty. The gap between the output of the two models as regards requested replenishment and the values of the risk measures can be used by the company to reallocate capacity among different products and to thus manage demand/inventory risk.

Manufacturing capability has often been viewed to be a major obstacle in achieving higher levels of customization. Companies follow various strategies ranging from equipment selection to order process management to cope with the challenges of increased customization. We examined how the customization process affects product performance and conformance in the context of a design‐to‐order (DTO) manufacturer of industrial components. Our competing risk hazard function model incorporates two thresholds, which we define as mismatch and manufacturing thresholds. Product performance was adversely affected when the degree of customization exceeded the mismatch threshold. Likewise, product conformance eroded when the degree of customization exceeded the manufacturing threshold. Relative sizes of the two thresholds have management implications for the subsequent investments to improve customization capabilities. Our research developed a rigorous framework to address two key questions relevant to the implementation of product customization: (1) what degrees of customization to offer, and (2) how to customize the product design process.

Much of the empirical research in the past two decades has suggested that quality management (QM) is context dependent. This research develops an empirical QM model in a technology‐based sector—electronics manufacturing. Based on quantitative and qualitative investigations of 225 electronics firms in Hong Kong and the Pearl River Delta (PRD) region of China, a path analytic model is developed. The empirical model shows that a typical quality management system (QMS) in the electronics industry is composed of four major modules, namely leadership, cultural elements, operational support systems, and process management. These modules create a series of chain effects on organizational performance, rather than acting as parallel elements with an equal impact. By quantifying their effects on organizational performance and comparing the model to others in the literature, we identify those QM constructs that are context dependent. In electronics manufacturing, process management and customer focus are more important than other elements (e.g., cultural factors) for garnering business results. This study contributes to contingency theory and research by identifying the key constructs and their relationships in a competitive, volatile, and technology‐based industry with complex supply networks.

As much as 50% of effective capacity can be lost to setups in printed circuit board assembly. Shigeo Shingo showed that radical reductions in setup times are possible in metal fabrication using an approach he called “Single Minute Exchange of Dies” (SMED). We applied SMED to setups of high speed circuit board assembly tools. Its key concepts were valid in this very different industry, but while SMED typically emphasizes process simplification, we had to add modern information technology tools including wireless terminals, barcodes, and a relational database. These tools shield operators from the inherent complexity of managing thousands of unique parts and feeders. The economic value of setup reduction is rarely calculated. We estimate a reduction of key setup times by more than 80%, and direct benefits of $1.8 million per year. Total cost of the changes was approximately $350,000.

An electronic marketplace typically provides industrial suppliers an alternative option for selling their capacity in addition to the traditional open market. However, suppliers face different sets of costs and risks in open market and in electronic market. Consequently, suppliers participating in an electronic market are likely to offer their capacity at a different price compared with traditional open market. We analyze this problem and derive the price‐capacity function for the supplier. We also derive a basis for allocating buyer's requirements among multiple suppliers so as to minimize his cost. Our model shows that suppliers with large capacities would quote a lower price in the electronic market. It also predicts that the unit bid price increases with bid quantity in the electronic market. Based on the price‐capacity curve, we model a scenario where the buyer announces, a priori, the number of suppliers to be selected for award of a contract that will minimize its costs.

We present a tool to diagnose the behavior of planners in complex production processes and to establish improvement potential for the delivery performance by changing the planning behavior. Scientific literature on production control offers valuable knowledge, but the complexity of real‐life processes makes it impossible to directly apply this knowledge in real‐life. The presented tool identifies possible deficiencies in the current way of managing the business processes, by matching the scientific knowledge on order planning with data reflecting the real‐life processes via logistic regression. A case study at a maintenance organization illustrates the diagnosis tool.