Knowledge that Transforms

We review the manuscripts accepted for publication by the Manufacturing Operations Department of Production and Operations Management ( POM) over 13 years (1992–2004). The manuscripts managed by this department deal with topics including scheduling, manufacturing systems management, inventory control and capacity management, maintenance management, and teaching and applications. In the process of this review, we highlight the significant contribution of POM to the field of operations management and illustrate how this body of work has served to further the mission of the journal and department. We then offer comments regarding characterizations of these manuscripts and a few ideas on how to expand this body of work in the future to further the mission of the journal.

Diffusion theory has typically focused on how communication, internal or external to a social system, leads to adoptions and diffusion of an innovation. We develop a diffusion and substitution model based on a somewhat different perspective. In some cases, progressive improvements in product attributes and/or continual cost reduction seem to be a key driver of the diffusion process. For example, after introduction of the 5.25‐inch disk drive, its capacity continually increased, and accordingly, so did customer willingness‐to‐pay. Our model is based on a linear reservation price framework, in which a product is described by its depth (defined as the difference between a product̂s maximum reservation price and its production cost), and its breadth (related to the slope of its reservation price curve), indicating how broadly it appeals across various customer segments. Because of changes in product depths and breadths over time, customers who previously preferred the old product may later prefer the new product, thus creating the diffusion process. While the Bass model describes diffusion as a function of the coefficients of innovation and imitation, in our model, it is described by the coefficients of depth and breadth (the rates of change in relative depth and breadth), along with an S‐coefficient that we associate with the technology S‐curve. We fit our model to data from the disk‐drive and the microprocessor industries.

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.

Several firms are interested in manufacturing and selling new products based on a new process technology. Before manufacturing can begin, either these Original Equipment Manufacturers (OEMs), or a Contract Manufacturer (CM) needs to adopt the process technology, i. e., make a capacity investment in it. Due to market uncertainty, the timing of capacity investment is crucial. In such a setting, we investigate how the timing of process adoption, an important determinant of time‐to‐market, is impacted by the make/buy decision. We first characterize the optimal time for process adoption and show that this delay depends on competitive intensity, cost structure and the rate of forecast improvement. Due to differing cost structures, incentives and risks, an OEM and a CM may invest in a new process technology at different times. We show that while there are conditions where outsourced manufacturing can be advantageous for the OEM from a time‐to‐market perspective, there are also cases where the OEM would be disadvantaged. In these cases, the OEM can accelerate process adoption by risk sharing through joint investment. Finally, the right choice of CM is extremely important for an OEM that faces a short time window for product introduction: An efficient CM not only provides low costs but also rapid access to new process technologies, and therefore higher revenues.

Uncertainty in new product development (NPD) planning embraces market, creative, technological, and process dimensions to a much greater extent than in non‐NPD project planning. Yet, NPD management is becoming increasingly decentralized, both within the firm and across the supply chain. Hence, planning for NPD uncertainty often results in path‐dependent scenarios cutting across the strategic, tactical, and operational levels of planning. To coordinate this resulting complexity, we propose a stochastic hierarchical product development planning framework with multiple recourses, i. e., corrective actions, to maximize performance across a firm's entire NPD program. We also argue the necessity for a fourth planning level, the infrastructural, that reestablishes norms for market projections, technological forecasts, scheduling, and requirements as latent uncertainty in the environment is continually revealed. An illustration from the automotive industry is presented to demonstrate a deployment of our framework. We additionally discuss the applicability of this framework for managing NPD capabilities over time.

In this paper, we study the performance of a sourcing mechanism gaining popularity in industrial procurement environments; a tournament. Under a tournament, a buyer initially procures her parts from two suppliers with possibly different quality levels, for T time periods, i.e., she parallel sources. During this time, the buyer is able to observe noisy signals about the suppliers' quality. At time T, she selects the supplier with the highest observed performance and awards it the remainder of her business. We characterize the optimal duration of the tournament as a function of various market parameters, including information and investment costs. Furthermore, we demonstrate that a tournament can be more profitable for the buyer than selecting the highest quality supplier at time T = 0 and sole sourcing entirely.

This paper describes an experimentation methodology to measure how demand varies with price and the results of its application at a toy retailer. The same product is assigned different price‐points in different store panels and the resulting sales are used to estimate a demand curve. We use a variant of the k‐median problem to form store panels that control for differences between stores and produce results that are representative of the entire chain. We use the estimated demand curve to find a price that maximizes profit. Our experiment yielded the unexpected result that demand increases with price in some cases. We present likely reasons for this finding from our discussions with retail managers. Our methodology can be used to analyze the effect of several marketing and promotional levers employed in a retail store besides pricing.

Business processes have become more simultaneous and collaborative in the recent past. In simultaneous processes, multiple parties must adapt to one another in real time as decisions evolve. For example, New Product Development (NPD) requires collaboration in the context of Concurrent Engineering, and Supply Chain Management (SCM) in the context of collaborative planning. In both cases, parties must modify decisions based on preliminary information, information that is not fully precise or stable, about what the other parties are doing. This article develops a generalized model of real‐time decision making based on preliminary information, which applies both to NPD and SCM. The model offers insights into when to commit to a course of action, and we derive seven principles that help in dealing with preliminary information.

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.

We present an integrated framework for measuring product development performance. The framework consists of a three stage model for exploring the relationships between metrics used by design, manufacturing, marketing functions, and overall commercial success. Using a cross‐sectional survey of 383 product development professionals working on 38 product development projects in the high‐tech electronic assembled goods manufacturing sector, we provide empirical evidence of the proposed framework. The findings indicate that in the high‐tech manufacturing sector (1) commercial success of new product development projects is primarily determined by market share, (2) gain in market share is primarily driven by lower unit cost and not by technical performance, and (3) reduction in unit cost is primarily driven by the increased speed of new product development and not by the R&D budget. The study failed to identify any significant association between R&D budget and technical performance, and development speed and technical performance.