Chester Chambers

Quality-Based Competition, Profitability, and Variable Costs

We consider the impact of variable production costs on competitive behavior in a duopoly where manufacturers compete on quality and price in a two-stage game. In the pricing stage, we make no assumptions regarding these costs---other than that they are positive and increasing in quality---and no assumptions about whether or not the market is covered. In the quality stage, we investigate a broad family of variable cost functions and show how the shape of these functions impacts equilibrium product positions, profits, and market coverage. We find that seemingly slight changes to the cost functions curvature can produce dramatically different equilibrium outcomes, including the degree of quality differentiation, which competitor is more profitable (the one offering higher or lower quality), and the nature of the market itself (covered or uncovered). Our model helps to predict and explain the diversity of outcomes we see in practice---something the previous literature has been unable to do.

Technological advancement, learning, and the adoption of new technology

Abstract This research focuses attention upon three related issues: the persistence of technological progress over time, multiple adoption decisions over a long horizon, and the impact of production based learning on those decisions. We develop a simple economic model for a single product producing firm incorporating continuous technological progress, linear product demand, linear switching costs, and experience based cost reductions. A dynamic programming framework is used to evaluate cases where either one or an unlimited number of adoptions are allowed over an infinite horizon. Both structural and numerical results are presented. Fundamental results serve to explain several counter-intuitive dynamics. In the single adoption case, faster rates of technological progress, as well as growing markets, or a steeply sloping demand curve, serve to delay adoption while an increased ability to learn may accelerate it. When multiple adoptions are allowed, the adoption of any particular technology presents a “window of opportunity” in which future investment will be warranted. If, for whatever reason, this window has passed, then maintaining the older technology becomes more attractive than adoption. Thus, seemingly outdated technologies may remain embedded in some settings.

Competition, learning and investment in new technology

Efforts to describe the evolution of production costs must simultaneously include the impact of investment in new technologies as well as experience-based learning. In this paper we formulate a dynamic model of technological investments, which includes the impacts of learning curve effects, investment levels in new technologies with uncertain timing, and competitive responses. Our results highlight the interaction of investment and output rate decisions in monopolistic and duopolistic situations, and illuminate the impact of the planning horizon length on such decisions. The model results elaborate upon how the attractiveness of new technologies is related to the firms ability to learn using its existing technology, how competition can increase or decrease the market size for new technologies, and how investment levels are driven in opposite directions by considering longer horizons on one hand and the competitive response on the other.

Patient punctuality and clinic performance: observations from an academic-based private practice pain centre: a prospective quality improvement study

Objectives The aim of this study was to examine the effects of an intervention to alter patient unpunctuality. The major hypothesis was that the intervention will change the distribution of patient unpunctuality by decreasing patient tardiness and increasing patient earliness. Design Prospective Quality Improvement. Setting Specialty Pain Clinic in suburban Baltimore, Maryland, USA. Participants The patient population ranged in age from 18 to 93 years. All patients presenting to the clinic during the study period were included in the study. The average monthly volume was 86.2 (SD=13) patients. A total of 1500 patient visits were included in this study. Interventions We tracked appointment times and patient arrival times at an ambulatory pain clinic. An intervention was made in which patients were informed that tardy patients would not be seen and would be rescheduled. This policy was enforced over a 12-month period. Primary and secondary outcome measures The distribution of patient unpunctuality was developed preintervention and at 12 months after implementation. Distribution parameters were used as inputs to a discrete event simulation to determine effects of the change in patient unpunctuality on clinic delay. Results Data regarding patient unpunctuality were gathered by direct observation before and after implementation of the intervention. The mean unpunctuality changed from −20.5 min (110 observations, SD=1.7) preintervention to −23.2 (169, 1.2) at 1 month after the intervention, −23.8 min (69, 1.8) at 6 months and −25.0 min (71, 1.2) after 1 year. The unpunctuality 12 months after initiation of the intervention was significantly different from that prior to the intervention (p<0.05). Conclusions Physicians and staff are able to alter patient arrival patterns to reduce patient unpunctuality. Reducing tardiness improves some measures of clinic performance, but may not always improve waiting times. Accommodating early arriving patients does serve to improve clinic performance.

The Use of Flexible Manufacturing Capacity in Pharmaceutical Product Introductions

This work considers the value of the flexibility offered by production facilities that can easily be configured to produce new products. We focus on technical uncertainty as the driver of this value, while prior works focused only on demand uncertainty. Specifically, we evaluate the use of process flexibility in the context of risky new product development in the pharmaceutical industry. Flexibility has value in this setting due to the time required to build dedicated capacity, the finite duration of patent protection, and the probability that the new product will not reach the market due to technical or regulatory reasons. Having flexible capacity generates real options, which enables firms to delay the decision about constructing product-specific capacity until the technical uncertainty is resolved. In addition, initiating production in a flexible facility can enable the firm to optimize production processes in dedicated facilities. The stochastic dynamic optimization problem is formulated to analyze the optimal capacity and allocation decisions for a flexible facility, using data from existing literature. A solution to this problem is obtained using linear programming. The result of this analysis shows both the value of flexible capacity and the optimal capacity allocation. Due to the substantial costs involved with flexibility in this context, the optimal level of flexible capacity is relatively small, suggesting products be produced for only short periods before initiating construction of dedicated facilities.

Applying JIT Principles to Resident Education to Reduce Patient Delays: A Pilot Study in an Academic Medical Center Pain Clinic

OBJECTIVES This study investigated the effect on patient waiting times, patient/doctor contact times, flow times, and session completion times of having medical trainees and attending physicians review cases before the clinic session. The major hypothesis was that review of cases prior to clinic hours would reduce waiting times, flow times, and use of overtime, without reducing patient/doctor contact time. DESIGN Prospective quality improvement. SETTING Specialty pain clinic within Johns Hopkins Outpatient Center, Baltimore, MD, United States. PARTICIPANTS Two attending physicians participated in the intervention. Processing times for 504 patient visits are involved over a total of 4 months. INTERVENTION Trainees were assigned to cases the day before the patient visit. Trainees reviewed each case and discussed it with attending physicians before each clinic session. PRIMARY AND SECONDARY OUTCOME MEASURES Primary measures were activity times before and after the intervention. These were compared and also used as inputs to a discrete event simulation to eliminate differences in the arrival process as a confounding factor. RESULTS The average time that attending physicians spent teaching trainees while the patient waited was reduced, but patient/doctor contact time was not significantly affected. These changes reduced patient waiting times, flow times, and clinic session times. CONCLUSIONS Moving some educational activities ahead of clinic time improves patient flows through the clinic and decreases congestion without reducing the times that trainees or patients interact with physicians.

Using process analysis to assess the impact of medical education on the delivery of pain services: A natural experiment

Background: The medical, social, and economic effects of the teaching mission on delivery of care at an academic medical center (AMC) are not fully understood. When a free-standing private practice ambulatory clinic with no teaching mission was merged into an AMC, a natural experiment was created. The authors compared process measures across the two settings to observe the differences in system performance introduced by the added steps and resources of the AMCs teaching mission. Methods: After creating process maps based on activity times realized in both settings, the authors developed discrete-event simulations of the two environments. The two settings were comparable in the levels of key resources, but the AMC process flow included three residents/fellows. Simulation enabled the authors to consider an identical schedule across the two settings. Results: Under identical schedules, the average accumulated processing time per patient was higher in the AMC. However, the use of residents allowed simultaneous processing of multiple patients. Consequently, the AMC had higher throughput (3.5 vs. 2.7 patients per hour), higher room utilization (82.2% vs. 75.5%), reduced utilization of the attending physician (79.0% vs. 93.4%), and a shorter average waiting time (30.0 vs. 83.9 min). In addition, the average completion time for the final patient scheduled was 97.9 min less, and the average number of patients treated before incurring overtime was 37.9% greater. Conclusions: Although the teaching mission of the AMC adds processing steps and costs, the use of trainees within the process serves to increase throughput while decreasing waiting times and the use of overtime.

Case—Miller Pain Treatment Center

On a cool Saturday morning Dr. Keith Weems walked briskly down the hallway toward the main entrance to Eastern Hospital (JHH). He was on his way to visit in-patients in several wards as part of his duties as the pain specialist on call for that weekend. However, his mind kept drifting to issues he was facing as the new manager of the Miller Pain Treatment Center in the neighboring Eastern Outpatient Center (E-HOC). Dr. Weems had been working as part of the Eastern system for several years. However, he recently merged his successful private practice into the Miller Pain Treatment Center and was appointed as its director. In so doing he moved from a setting where he was the clear boss to one in which he was dealing with a collection of more established doctors already set in their ways. He also had to figure out how to improve the care delivery process in this environment, which considered itself to be part of the premier teaching hospital in the world. After reviewing the operations and meeting the staff h...

Case Article—Miller Pain Treatment Center—Eastern Hospital Outpatient Center

Introductory courses in operations management typically introduce students to process analysis and queuing theory. We apply these tools to consider patient flows in an outpatient clinic where processes are made more complex by inclusion of the teaching mission of an Academic Medical Center. The case narrative deals with a physician who moved his practice from a setting with no teaching mission to the academic setting. This created a natural experiment because he began treating the same patients using a different process flow. Students are asked to use data collected at both settings to compare and contrast these flows. The protagonist weighs options designed to improve an appointment schedule, change patient punctuality, and introduce a type of pre-processing of patients. Evaluation of these proposals calls for a different style of analysis. Students are introduced to the use of discrete event simulation to address such questions. Simulation models are provided corresponding to the two clinic settings. Th...