Timothy L. Smunt

Bio: Timothy L. Smunt is an academic researcher from University of Wisconsin–Milwaukee. The author has contributed to research in topics: Learning curve & Batch production. The author has an hindex of 15, co-authored 26 publications receiving 700 citations. Previous affiliations of Timothy L. Smunt include University of Wisconsin-Madison & Saint Petersburg State University.

Optimal and Heuristic Models for Lot Splitting in a Flow Shop

Abstract: This paper investigates optimal lot-splitting policies in a multiprocess flow shop environment with the objective of minimizing either mean flow time or makespan. Using a quadratic programming approach to the mean flow time problem, we determine the optimal way of splitting a job into smaller sublots under various setup times to run time ratios, number of machines in the flow shop, and number of allowed sublots. Our results come from a deterministic flow shop environment, but also provide insights into the repetitive lots scheme using equal lot splits for job shop scheduling in a stochastic environment. We indicate those conditions in which managers should implement the repetitive lots scheme and where other lot-splitting schemes should work better.

The Effects of Learning on Optimal Lot Sizes: Further Developments on the Single Product Case

Abstract: This paper reviews the previous research on the problem of determining optimal lot sizes when production costs decrease on a log-linear function. Problems due to simplifying assumptions of the previous work are discussed and modifications of previous models are presented to address more realistic conditions. Using a dynamic programming approach, the new results for two cases of learning, total transmission of learning (from lot to lot) and partial transmission, indicate that optimal lot sizes are increasing in the long-run and long transient states exist, contrary to conclusions of prior papers. The effect of forgetting indicates that operations managers should consider longer production runs for many processes. The revised model formulation presented in this study is also capable of handling demand characteristics of variance, growth and seasonality, significantly extending the application possibilities of the previous models which were limited to a continuous, deterministic demand pattern.

Flexibility in Manufacturing: A Survey

Abstract: This article is an attempt to survey the vast literature on flexibility in manufacturing that has accumulated over the last 10 to 20 years. The survey begins with a brief review of the classical literature on flexibility in economics and organization theory, which provides a background for manufacturing flexibility. Several kinds of flexibilities in manufacturing are then defined carefully along with their purposes, the means to obtain them, and some suggested measurements and valuations. Then we examine the interrelationships among the several flexibilities. Various empirical studies and analytical/optimization models dealing with these flexibilities are reported and discussed. The article concludes with suggestions for some possible future research directions.

Flexibility in Manufacturing: A Survey

Abstract: This article is an attempt to survey the vast literature on flexibility in manufacturing that has accumulated over the last 10 to 20 years. The survey begins with a brief review of the classical literature on flexibility in economics and organization theory, which provides a background for manufacturing flexibility. Several kinds of flexibilities in manufacturing are then defined carefully along with their purposes, the means to obtain them, and some suggested measurements and valuations. Then we examine the interrelationships among the several flexibilities. Various empirical studies and analytical/optimization models dealing with these flexibilities are reported and discussed. The article concludes with suggestions for some possible future research directions.

Learning curves in manufacturing.

Abstract: Large increases in productivity are typically realized as organizations gain experience in production. These "learning curves" have been found in many organizations. Organizations vary considerably in the rates at which they learn. Some organizations show remarkable productivity gains, whereas others show little or no learning. Reasons for the variation observed in organizational learning curves include organizational "forgetting," employee turnover, transfer of knowledge from other products and other organizations, and economies of scale.

Learning Curves In Manufacturing

Abstract: Large increases in productivity are typically realized as organizations gain experience in production. These "learning curves" have been found in many organizations. Organizations vary considerably in the rates at which they learn. Some organizations show remarkable productivity gains, whereas others show little or no learning. Reasons for the variation observed in organizational learning curves include organizational "forgetting," employee turnover, transfer of knowledge from other products and other organizations, and economies of scale.

The persistence and transfer of learning in industrial settings

Abstract: The persistence of learning within organizations and the transfer of learning across organizations are examined on data collected from multiple organizations. Results indicate that knowledge acquired through production depreciates rapidly. The conventional measure of learning, cumulative output, significantly overstates the persistence of learning. There is some evidence that learning transfers across organizations: organizations beginning production later are more productive than those with early start dates. Once organizations begin production, however, they do not appear to benefit from learning in other organizations. The implications of the results for a theory of organizational learning are discussed. Managerial implications are described.