The author, who has written nine textbooks in 26 editions over the past two decades, discusses his ideas of how to bring a production and operations management course alive. He introduces a model that ties together the use of text with both ancillary materials that accompany that book as well as with other tools available independent of the text. Some of these tools are technology driven (e.g., CD ROMS and the Internet), while others bring real business exposure to the course (e.g., field study consultancies, guest speakers, and plant tours).
Continued globalization, extensive use of information technologies, increasing cross‐functional dependencies, and new organizational forms are rapidly changing the learning context of operations management. This paper describes an example of a mba elective course that has been created to address and learn from these new realities. We have taken a process view of the global firm, focusing mainly on the new product/service development, order fulfillment, and supply chain/ after‐sales service processes. We have designed our course to contain most of the traditional topics in operations management (om) with information systems (is) woven in to support the key business processes. Our underlying belief is that these two functional areas should be seen as a blend, a seamless merging of the two fields, to obtain a competitive advantage. Besides teaching om and is in this integrated manner, the course engages in face‐to‐face student consulting projects with multinational companies. This paper describes our rationale for the course design and how it has been implemented over the last 6 years‐as well as our plans for the future.
A manufacturing optimization strategy is developed and demonstrated, which combines an asset utilization model and a process optimization framework with multivariate statistical analysis in a systematic manner to focus and drive process improvement activities. Although this manufacturing strategy is broadly applicable, the approach is discussed with respect to a polymer sheet manufacturing operation. The asset utilization (AU) model demonstrates that efficient equipment utilization can be monitored quantitatively and improvement opportunities identified so that the greatest benefit to the operation can be obtained. The process optimization framework, comprised of three parallel activities and a designed experiment, establishes the process‐product relationship. The overall strategy of predictive model development provided from the parallel activities comprising the optimization framework is to synthesize a model based on existing data, both qualitative and quantitative, using canonical discriminant analysis, to identify main effect variables affecting the principal efficiency constraints identified using AU, operator knowledge and order‐of‐magni‐tude calculations are then employed to refine this model using designed experiments, where appropriate, to facilitate the development of a quantitative, proactive optimization strategy for eliminating the constraints. Most importantly, this overall strategy plays a significant role in demonstrating, and facilitating employee acceptance, that the manufacturing operation has evolved from an experienced‐based process to one based on quantifiable science.
We consider a monopolistic situation where the retailer aims to find the profit‐maximizing selling price, and the order and backorder quantity of an item. The price and demand are assumed to be inversely related. We develop a simple optimal approach, the proportion‐balancing algorithm (PBA), which utilizes the proportions of cost components to sales revenue. We demonstrate that the PBA can handle three commonly used demand functions. However, applicability of the PBA depends on how the price‐demand relationship is specified. A sensitivity analysis shows that the effects of changes in cost parameters on the order and backorder quantities can be very different from those of the classical economic order quantity model with backorders.
Service operations courses are often too concerned with concepts or techniques and not enough with the solution of real‐world service problems. This article presents a case study of the successful turnaround of such a course by rebuilding it around a field project. The project is presented in some details. It largely dictates the content and paces the delivery of the course. Strategic and tactical aspects in the design of such a course are discussed with the benefits and limitations of the approach.
Recently we have developed solution procedures for the optimal replacement problem when the distribution of the time‐to‐failure (ttf) is partially specified by the first two moments, partial and complete. However, we have later learned, using Monte‐Carlo simulation, that when moments are unknown and have to be estimated from sample data, the most accurate procedure developed therein is in practice extremely sensitive to sampling fluctuations. In this paper we modify the procedure to render it less susceptible to sampling variation. In addition, we introduce a new solution procedure that requires specification of only the median and the partial means of the ttf distribution. For both procedures, it is demonstrated that when the moments required for the distribution fitting are known, highly accurate optimal solutions are obtained. Conversely, when the moments are unknown and sample estimates based on small samples are used, both procedures result in stable solutions (low mean‐squared‐errors).
This article revisits an old problem; “systematically explore the information contained in a set of operating data records and find from it how to improve operational performance by taking the appropriate decisions in the space of operating conditions,” thus leading to continuous process improvement. A series of industrial case studies within the framework of the internships in the Leaders for Manufacturing (LFM) program at Massachusetts Institute of Technology led us to a reexamination of the traditional formulations for the above problem. The resulting methodology is characterized by the following features: (1) problem statement and solutions are expressed in terms of hyperrectangles in the decision space, replacing conventional pointwise results; (2) data‐driven, nonparametric learning methodologies were advanced to produce the requisite mapping between performance and decisions; (3) operating performance is in essence multifaceted, leading to a multiobjective problem, which is treated as such. The proposed methodology has been applied to a number of industrial examples and in this paper we provide a brief overview only of those that can be discussed in the open literature.
A new teaching paradigm, called the information/control/buffer (I/C/B) portfolio is described. The I/C/B portfolio has proven itself effective in introducing students to what managing operations is all about: using information, control, and buffers to manage the production of products and services. Even more important, the I/C/B viewpoint motivates students to think creatively about designing and developing new management systems. The I/C/B portfolio also provides a link between operations management, technology management, and information system management.
Recently, there has been much concern over the dumbing down of production and operations management (pom) courses in response to the repudiation of the theory‐heavy operations research (or) approach that became dominant during the 1970s. However, although almost everyone agrees that a new POM framework is needed, there is as yet little agreement on what it should be like. As a result, there is currently a huge variance among POM courses at different universities, ranging all the way from traditional OR courses to almost purely anecdotal case‐oriented courses. Although academics have struggled with the search for an appropriate level of methodological rigor in POM courses, our customers (i.e., students and the firms that hire them) have been inundated by a blizzard of management buzzwords. Although many of these undoubtedly contain kernels of truth, the very nature of the buzzword approach is such that it provides little balanced guidance as to what methods work well in a given situation. In recognition of these disparities, POM researchers have begun trying to systematically describe the underlying behavior of production systems. The goal is to provide a framework that will help organize educational approaches and business practices in a consistent fashion. In this paper, we describe our attempt at the needed “science of manufacturing,” which we call factory physics, and illustrate how it fits into a new paradigm of POM teaching.
This case study of a process improvement project from the Hewlett‐Packard Analytic Products Group illustrates a number of important approaches for operations strategy. First, we show how process control in capillary tube manufacturing led to a significant improvement in tubing strength, a quality measure that relates to the frequency of tube defects at a particular stress level. Next, we show how one particular approach used for experimental design and process improvement was considerably more economical than another standard method. In conclusion, we link the process characteristics such as defect frequency to strategic measures such as company revenue and investment to underscore the strategic importance of process control.