Hemant V. Kher

Countering forgetting through training and deployment

Abstract Although worker flexibility has several advantages, it is costly to obtain and maintain given the productivity losses that arise from worker learning and forgetting effects. In this study, we review factors that influence worker forgetting in industrial settings, and analyze the degree to which existing mathematical models conform to observed human forgetting behavior. We find that the learn–forget curve model (LFCM) satisfies many characteristics of forgetting. In the context of worker flexibility, we use LFCM to understand the extent to which cross training and deployment become important in helping reduce forgetting effects. Finally, we enhance LFCM by augmenting it to incorporate the job similarity factor. Sensitivity analysis reveals that the importance of training and deployment policies is reduced as task similarity increases.

Examination of worker assignment and dispatching rules for managing vital customer priorities in dual resource constrained job shop environments

Abstract The focus in this study is on evaluating scheduling policies that may be used to offer a near-perfect delivery performance for vital customers in dual resource constrained (DRC) job shop environments. Prior studies in this area have modeled shops where all orders are considered to be of equal importance, and shown that any reasonable combination of worker assignment and dispatching rules that are sensitive to the shop cost factors modeled works well. However, a recent survey indicates that shops do in fact distinguish among orders on the basis of customer identity, and that certain customers have very rigid delivery guidelines which must be adhered to in order to maintain their ongoing business. The present study models such environments, and shows that the choice of scheduling policies is not inconsequential, especially if the shop aims at providing near-perfect delivery performance to a limited set of customers. In such instances, policies for deploying workers to different departments, as well as dispatching rules that use customer identity based information outperform their counterparts that are not sensitive to orders placed by vital customers. Scope and purpose In todays highly competitive marketplace, an increasing number of firms are being required to provide a high level of delivery performance by their customers. Certain types of vital customers, such as those operating under the JIT manufacturing philosophy, have very rigid delivery guidelines which must be adhered to on a consistent basis in order to retain their business in the long run. Prior studies that have considered this problem from a scheduling perspective have focused only on dispatching rules in machine constrained settings. However, in most real shops, dispatching rules are not the only means available in managing orders from vital customers. Instead, managers rely on flexible workers that can be moved to different departments as the shop load varies. The role of workers, and decision rules that govern worker assignment to different departments has not been considered in the context of managing vital customer priorities. The purpose of this study is to determine the relative importance of worker assignment and dispatching rules in offering a near-perfect delivery performance to vital customers served by the firm.

The dual-phase learning-forgetting model

Abstract In this paper we develop the dual-phase learning–forgetting model (DPLFM) to predict task times in an industrial setting. The DPLFM results from integrating previously published learning and forgetting models that have been validated using experimental data. As a result, the DPLFM captures two important characteristics of the individual learning and forgetting phenomenon that are found in industrial settings. First, it expresses learning as a combination of cognitive and motor skills learning. This allows the learning rate to vary based on the experience gained in previous cycles, as well as the nature of task being performed (e.g. highly complex versus very simple tasks). Second, our model also captures forgetting based on the workers learning rate, prior experience, as well as the length of the interruption interval over which the worker experiences forgetting. We use a numerical example to illustrate the DPLFM, and perform an analysis on its parameters to gain insights into its behavior.

The production of several items in a single facility with linearly changing demand rates

In this paper we extend the ELSP model to allow for linearly changing demand rates over a fixed planning horizon. This extension of the ELSP research provides a model that can be used in coordinating the production and marketing planning activities in a firm. The model allows the user to evaluate the impact of changes in product demand on production costs and customer service. We solve the model using a standard nonlinear programming package (MINOS) and show through examples based on actual production data how the model can be used to support coordinated production and marketing planning.

Worker deployment in dual resource constrained systems with a task-type factor

This paper investigates worker learning and forgetting phenomenon in a dual resource constrained system (DRC) setting. Worker learning and forgetting in two and three stage DRC systems are modeled according to the dual-phase learning and forgetting model (DPLFM), which is based on the theory that a task has separate cognitive and motor requirements. Results show that the task-type (with respect to its learning rate and proportion of cognitive and motor requirements) affects the performance of training and deployment policies in DRC systems, and as such it should be included in future DRC research.

Variant versus invariant time to total forgetting: the learn–forget curve model revisited

Abstract Understanding and quantifying the learning–forgetting process helps predict the performance of an individual (or a group of individuals), estimate labor costs, bid on new and repeated orders, estimate costs of strikes, schedule production, develop training programs, set time standards, and improve work methods [IIE Trans. 29 (1997) 759]. Although there is agreement that the form of the learning curve is as presented by [J. Aeronaut. Sci. 3 (1936) 122], scientists and practitioners have not yet developed a full understanding of the behavior and factors affecting the forgetting process. The paucity of research on forgetting curves has been attributed to the practical difficulties involved in obtaining data concerning the level of forgetting as a function of time [IIE Transactions 21 (1989) 376]. The learn–forget curve model (LFCM) was shown to have many advantages over other theoretical models that capture the learning–forgetting relationship. However, the deficiency of the LFCM is in the assumption that the time for total forgetting is invariant of the experience gained prior to interruption. This paper attempts to correct this deficiency by incorporating the findings of [Int. J. Ind. Ergon. 10 (1992) 217] into the LFCM. Numerical examples are used to illustrate the behavior of the modified LFCM (MLFCM) and compare results to those of the LFCM.

Shop Performance Implications of Using Cells, Partial Cells, and Remainder Cells*

This paper considers the application of cellular manufacturing (CM) to batch production by exploring the shop floor performance trade-offs associated with shops employing different levels of CM. The literature has alluded to a continuum that exists between the purely departmentalized job shop and the completely cellular shop. However, the vast majority of CM research exists at the extremes of this continuum. Here, we intend to probe performance relationships by comparing shops that exist at different stages of CM adoption. Specifically, we begin with a hypothetical departmentalized shop found in the CM literature, and in a stepwise fashion, form independent cells. At each stage, flow time and tardiness performance is recorded. Modeling results indicate that, depending on shop conditions and managerial objectives, superior shop performance may be recorded by the job shop, the cell shop, or by one of the shops between these extreme points. In fact, under certain conditions, shops that contain partially formed cells perform better than shops that use completely formed cells. Additional results demonstrate that in order to achieve excellent performance, managers investigating specific layouts need to pay especially close attention to changes in machine utilization as machine groups are partitioned into cells.

Comparing variance reduction to managing system variance in a job shop

Variance within the manufacturing system leads to uneven shop loads, long manufacturing lead times, and unreliable customer service. This study compares techniques that reduce system variance to techniques that manage system variance. The study is placed in a dual resource constrained job shop. Results indicate that reducing system variance improves flow time and customer service performance measures, such as mean tardiness and percent tardy jobs more than techniques that react to system variance.

Decision Rules for the Academy Awards Versus Those for Elections

Many researchers have evaluated various decision rules to determine how well they perform in selecting winners in elections. They have established criteria to measure how well these rules perform in selecting winners with the greatest mass appeal in general elections. We evaluate such decision rules on their performance in determining winners of awards for outstanding accomplishment. We examined the procedures the Academy of Motion Picture Arts and Sciences uses to choose nominees and winners for Academy Awards. We chose this example for two reasons. First, the academy uses several decision rules to select nominees and to select the winners from the lists of final nominees. Second, Academy Awards have an enormous impact on earnings and careers. We found that decision rules that can have negative effects in elections based on mass appeal can have positive aspects in determining winners of awards for outstanding accomplishment.

An Interdepartmental Care Model to Expedite Admission from the Emergency Department to the Medical ICU.

BACKGROUND Early evidence suggests that multidisciplinary programs designed to expedite transfer from the emergency department (ED) may decrease boarding times. However, few models exist that provide effective ways to improve the ED- to-ICU transition process. In 2012 Christiana Care Health System (Newark, Delaware) created and implemented an interdepartmental program designed to expedite the transition of care from the ED to the medical ICU (MICU). METHODS This quasi-experimental study compared ED length of stay (LOS), MICU LOS, and overall hospital LOS before and after the MICU Alert Team (MAT) intervention program. The MAT consisted of a MICU nurse and physician assistant, with oversight by a MICU attending physician. The ED triggered the MAT after patients were stabilized and determined to require MICU admission. Following bedside face-to-face hand off, the MAT providers then assumed responsibly of a patients care. If no MICU bed was available, the MAT cared for patients in the ED until they were transferred to the MICU. RESULTS ED LOS was reduced by 30% (2.6 hours) from baseline (p < .001). There were no significant differences in MICU LOS (p = .26), overall hospital LOS (p = .43), or mortality (p = .59). ED LOS was shortened (p < .001) at each increasing level of MICU bed availability (31% when 0 MICU beds available; 26% when 1 or more MICU beds available). Time series analysis identified a 1.5-hour drop in ED LOS (p = .02) for patients transferred from the MICU immediately following intervention implementation and was sustained over time. CONCLUSION Early outcomes demonstrate that the MAT intervention can reduce ED LOS for critically ill patients. Additional studies should determine optimal approaches to improve clinical outcomes.