Cheryl Gaimon

Managing Knowledge-Based Resource Capabilities Under Uncertainty

A firms ability to manage its knowledge-based resource capabilities has become increasingly important as a result of performance threats triggered by technology change and intense competition. At the manufacturing plant level, we focus on three repositories of knowledge that drive performance. First, the physical production or information systems represent knowledge embedded in the plants technical systems. Second, the plants workforce has knowledge, including diverse scientific information and skills, to effectively operate the technical systems. Third, the firms managerial systems embody knowledge in the form of goals, reward systems, and control and coordination systems. Taken together, we consider the technical systems, workforce knowledge, and the managerial systems as the plants knowledge-based resource capability.Two normative models are introduced offering insight on how plant performance is impacted by investments in workforce knowledge (training) or the technical systems (process change). The models explicitly recognize that the outcome of investments in knowledge-based change is uncertain due to factors including technical problems, worker resistance, and limited financial resources. Also, we recognize that workforce knowledge may be deployed to mitigate the outcome uncertainty encountered with process change.Investments in knowledge-based change cannot be fully understood in isolation of the managerial systems. In one model, the plant manager is motivated by an incentive system that rewards the realization of a threshold goal, whereas in the other model the incentive system emphasizes the realization of meeting a particular target goal. We also investigate the impact of the managers view of uncertainty (her willingness to absorb risk), which is influenced by the managerial systems. Results show that different characterizations of the managerial systems have a profound effect on managerial behavior and plant-level performance.

Simultaneous and dynamic price, production, inventory and capacity decisions

Abstract This paper concerns a profit maximizing firm that derives the optimal price for its output, level of output, level of inventory, and composition of productive capacity over time. First, it is assumed that capacity may be acquired to either increase or replace the firms existing productive capacity. Second, it is assumed that the acquisition of new capacity causes a reduction in the firms production cost. Lastly, it is assumed that demand, which is expressed as a function of time and price, must be met either from current production or from inventory. Clearly, the level of production cannot exceed the productive capacity at any time. The optimal control model formulated requires the solution of a two-point boundary value problem so that a discrete approximation numerical procedure is presented. Numerical results indicate that due to anticipated technological improvement, the acquisition of new capacity is postponed and occurs in greater magnitude later in the planning horizon. In addition, as a result of upgrading productive capacity through the acquisition of new technology, numerical results show that a firm lowers its per unit production cost and thereby reduces the optimal price charged for output which increases demand. Furthermore, since the level of capacity is increased due to the purchase of new technology, less reliance on inventory is necessary to meet peak demand. Lastly, it is shown that a larger firm optimally acquires a greater level of new capacity over the planning horizon to reduce its operating costs, and thereby further its competitive advantage.

Dynamic Game Results of the Acquisition of New Technology

Open and closed-loop Nash strategies are derived and analyzed for a differential game in which two competing firms choose prices and productive capacity where new technology reduces a firms unit operating cost. Using an open-loop strategy, a firm makes an irreversible commitment to a future course of action. For example, a contract with a labor union may force a firm to commit to maintaining its entire workforce regardless of its competitors future realized behavior. In contrast, using a closed-loop strategy, a firms decisions evolve over time, continuously responding to the competitors behavior. The dynamic Nash strategies obtained for the closed-loop model exhibit a more restricted acquisition of new technology and a greater reduction of existing capacity relative to the open-loop solutions. In addition, the dynamic Nash price to be charged for output is higher in the closed as opposed to open-loop competitive environment. Numerical solutions are presented to demonstrate that if both firms apply the closed-loop approach, each will earn higher profits than if one or both firms choose an open-loop strategy.

Revenue Driven Resource Allocation: Funding Authority, Incentives, and New Product Development Portfolio Management

The first step in transforming strategy from a hopeful statement about the future into an operational reality is to allocate resources to innovation and new product development (NPD) programs in a portfolio. Resource allocation and NPD portfolio decisions often span multiple levels of the organizations hierarchy, leading to questions about how much authority to bestow on managers and how to structure incentives for NPD. In this study, we explore how funding authority and incentives affect a managers allocation of resources between existing product improvement (relatively incremental projects) and new product development (more radical projects). Funding may be either fixed or variable depending on the extent to which the manager has the authority to use revenue derived from existing product sales to fund NPD efforts. We find that the use of variable funding drives higher effort toward improving existing products and developing new products. However, variable funding has a subtle side effect: it induces the manager to focus on existing product improvement to a greater degree than new product development, and the relative balance in the NPD portfolio shifts toward incremental innovation. In addition, we highlight a substitution effect between explicit incentives (compensation parameters) and implicit incentives (career concerns). Explicit incentives are reduced as career concerns become more salient.

Flexibility and the choice of manufacturing facilities under short product life cycles

Abstract Rapid changes in technology and intense competition have led to shorter product life cycles in both high-technology industries and industries not commonly regarded as high-technology. As a result of short product life cycles, firms are faced with the challenge of planning for facilities whose useful lives are much longer than the life cycle of any individual product it manufactures. This paper develops a model to analyze the critical trade-offs associated with determining the best level of flexibility for a facility that manufactures a series of high-volume products, sequentially over each products life cycle. Our analysis considers the benefits associated with more flexible facilities including reduced changeover costs and earlier market entry, versus the higher initial investment and higher production costs typically encountered.

Routing flexibility and production scheduling in a flexible manufacturing system

Abstract A multiproduct, multiperiod, multistage network model is presented for the planning of order release and production scheduling in a flexible manufacturing system environment under the existence of alternate routings. It is assumed that premanufacturing decisions such as machine grouping and tool loading have been made, so that setup costs and setup times are neglible and can be included in the processing times. The decision process addressed by the model is the disaggregation of weekly production requirments to daily production requirments, the determination of production batch sizes for each operation of each part type, and the daily assignment of each batch to machine groups given the flexibility of alternate routings. The model also provides the interface and linkage between an MRP component planning system and the shop scheduling system. The model is solved using a price-directive decomposition technique with column generation. Experimentation is performed with the model for varying problem sizes to determine the impact of shop flexibility on total cost, inventory levels, existence of bottlenecks, shop utilization, and the number of setups and split lot production. The results indicate important cost-benefit trade-off implications for system design and acquisition. For example, if in fact setup costs and times are nonnegligible, then it is shown that increasing the routing flexibility of a system without a parallel decrease in setup costs and times is unlikely to reap significant benefits.

A framework for process change

Forces such as technology change and increased competition provide opportunities and challenges that drive a firm to continuously evaluate and modify its resource capabilities. As a consequence, a firms process change strategy is of paramount importance for sustained manufacturing success. However, fundamental elements of process change strategy are not well understood. Long term performance benefits associated with potential process change alternatives are often unclear. Moreover, uncertainty exists regarding the actual benefits that may be attained from various types of process change. Critical issues impacting the proper implementation of process change are frequently underestimated or largely ignored. Therefore, despite the improved performance sought, process change often leads to lower productivity, excessive equipment downtime, and deterioration in quality. As the authors review the relevant empirical and normative literature, a framework emerges that characterizes the salient features of a firms process change strategy. The underlying dynamics of process change are explored and strategies are discussed to reduce the short-term disruption and enhance the long-term gain. In particular, the authors demonstrate the importance of creating and applying knowledge to improve the outcome of process change. They describe managerial actions that can be taken to reduce various sources of uncertainty associated with process change. Moreover, they identify key contributions as well as limitations of the existing normative literature on process change. Insights from the empirical literature are given that both support elements of the existing normative models and provide direction for future normative research. Thus, the authors seek to aid practicing managers and researchers alike to better understand the full scope and implications of process change.

Flexibility and Pricing Decisions for High-Volume Products with Short Life Cycles

We examine the competitive implications of a firms ability to change over its facility for the manufacture of successive generations of high-volume products with short life cycles. This ability is known as changeover flexibility. The model introduced extends the existing literature in several directions. First, the model offers explicit treatment of the critical relationships between market entry time, changeover flexibility, product life cycles, and profit. Second, the model explicitly considers the effect of early market entry on the accumulation of manufacturing experience (learning), which reduces the unit production cost. Third, the products optimal selling price is determined and its relation to the firms changeover flexibility is examined. Last, facility flexibility is permitted to vary over a continuum. Therefore, we are able to capture decision making concerning the optimal degree of changeover flexibility. Both analytic and numerical results are reported, demonstrating the link between the operations and marketing domains in the context of a firms optimal entrance and exit strategies. Among the key findings are (1) a firm more capable of reducing operating costs through learning over short life cycles optimally invests in more changeover flexibility, charges higher prices, and obtains greater profit; and (2) as the cost of flexible technologies decrease, a firm optimally increases its investment in changeover flexibility, enters markets earlier, and charges higher average prices over the products life cycle.

Dynamic Resource Capabilities: Managing Workforce Knowledge with a Technology Upgrade

Although managers invest in new technology to improve performance, often the benefits sought are elusive while the costs incurred far exceed expectations. The literature offers insights to help explain this phenomenon. Evidence suggests that, while carefully considering the purchase cost, managers often underestimate the costs and planning necessary for proper implementation. Consequently, short-term problems arise and long-term benefits are not realized. A model is introduced that integrates workforce knowledge management with the technology upgrade decision. The manager upgrades technology or pursues general training of the workforce in response to depreciation in the ability of each resource to drive net income over time. Depreciation occurs because of changes in consumer preferences and competition. Although adding to technology capability, an upgrade makes a portion of workforce knowledge obsolete. The manager invests in preparatory training prior to the upgrade to reduce obsolescence. Whereas general training is pursued to respond to depreciation by enhancing the ability of the workforce to improve existing products or create new products, preparatory training is technology-specific and focused on preparing for a technology upgrade. We find that the rates of preparatory and general training follow entirely different paths over time. Conditions are given where a manager uses one training strategy as a substitute for or complement to the other. We show that training strategies are not only impacted by learning phenomena such as the rate of forgetting, but also by the rates of technology depreciation and advancement. We show how workforce learning phenomena impact the technology upgrade decision.

An impulsive control approach to deriving the optimal dynamic mix of manual and automatic output

Abstract An optimization model is described in which the level and composition of productive capacity are obtained over time. Specifically, the levels of output achieved manually and from automatic equipment are optimally derived. The objective is to minimize costs incurred due to deviating from a goal level of output, operating the manual and automatic equipment, and changing the level of output obtained manually and from automation. Since all costs are expressed as functions of time, factors such as technological innovation and increasing wage rates (operating cost of manual output) are considered. Automation is acquired at discrete times in the planning period so that the model is formulated and solve using optimal impulsive control theory.