We investigate how a supply chain involving a risk‐neutral supplier and a downside‐risk‐averse retailer can be coordinated with a supply contract. We show that the standard buy‐back or revenue‐sharing contracts may not coordinate such a channel. Using a definition of coordination of supply chains proposed earlier by the authors, we design a risk‐sharing contract that offers the desired downside protection to the retailer, provides respective reservation profits to the agents, and accomplishes channel coordination.
This paper studies the impact of learning on a multi‐staged investment scenario. In contrast to other models in the real options literature in which learning is viewed as a passive consequence of the delay period, this paper quantifies information acquisition by merging statistical decision theory with the real options framework. In this context, real option attributes are discussed from a Bayesian perspective, thresholds are identified for improved decision‐making, and information's impact on downstream decision‐making is discussed. Using real data provided by a firm in the aerospace maintenance, repair, and overhaul industry, the methodology is used to guide a multi‐phased irreversible investment decision involving process design and capacity planning.
A model is introduced to analyze the manufacturing‐marketing interface for a firm in a high‐tech industry that produces a series of high‐volume products with short product life cycles on a single facility. The one‐time strategic decision regarding the firm's investment in changeover flexibility establishes the link between market opportunities and manufacturing capabilities. Specifically, the optimal changeover flexibility decision is determined in the context of the firm's market entry strategy for successive product generations, the changeover cost between generations, and the production efficiency of the facility. Moreover, the dynamic pricing policy for each product generation is obtained as a function of the firm's market entry strategy and manufacturing efficiency. Our findings provide insights linking internal manufacturing capabilities with external market forces for the high‐tech and high‐volume manufacturer of products with short life cycles. We show the impact of manufacturing efficiency and a firm's ability to benefit from volume‐based learning on the dynamic pricing policy for each product generation. The results demonstrate the benefits realized by a firm that works with its manufacturing equipment suppliers to develop more efficient and flexible technology. In addition, we explore how opportunities afforded by pioneer advantage enable a firm operating a less efficient facility to realize long term competitive advantage by deploying an earlier market entry strategy.
When the development cycle for a product is longer than the development cycle for a core technology that is embedded in it, designers may need to modify the product̂s design to avail of upgrades in this core technology. We model optimal product positioning with regard to technology choice in this setting, using a stochastic dynamic programming framework. Under fairly general assumptions, we find that there are three possible optimal actions: to abandon the project, to maintain the current technology, or to reposition so as to use the best technology currently available. We characterize the optimal positioning sequence in different design environments, discussing throughout the practical implications of our model. Previous research and conventional wisdom suggest early finalization of product specifications if design flexibility is decreasing over time. In contrast, we find that in some design environments, repositioning late in the development cycle can be optimal.
We consider a service system with two types of customers. In such an environment, the servers can either be specialists (or dedicated) who serve a specific customer type, or generalists (or flexible) who serve either type of customers. Cross‐trained workers are more flexible and help reduce system delay, but also contribute to increased service costs and reduced service efficiency. Our objective is to provide insights into the choice of an optimal workforce mix of flexible and dedicated servers. We assume Poisson arrivals and exponential service times, and use matrix‐analytic methods to investigate the impact of various system parameters such as the number of servers, server utilization, and server efficiency on the choice of server mix. We develop guidelines for managers that would help them to decide whether they should be either at one of the extremes, i.e., total flexibility or total specialization, or some combination. If it is the latter, we offer an analytical tool to optimize the server mix.
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.