Mohamad Y. Jaber

Production breaks and the learning curve: The forgetting phenomenon

Abstract In this paper, the forgetting slope is shown to be mathematically dependent on the following factors: 1. (1) the learning slope, 2. (2) the quantity produced to date, and 3. (3) the minimum break at which total forgetting occurs. It is also shown that it is possible to determine the value of the forgetting rate once the curves mathematical form is assumed. This answers some potential considerations inherent in the arbitrary assumption of forgetting rate. This paper also investigates the effects of learning and forgetting on both the optimum production quantity and the minimum total inventory system cost.

A comparative study of learning curves with forgetting

Abstract Although there is almost unanimous agreement that the form of the learning curve is as presented by Wright,1 scientists and practitioners have not yet developed a full understanding of the behavior and factors affecting the forgetting process. Mathematical models of the forgetting process are reviewed, and then three models, the VRIF, VRVF, and LFCM models are compared and their differences and similarities are discussed.

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.

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.

A note on "An empirical comparison of forgetting models"

In the above paper, Nembhard and Osothsilp (2001) empirically compared several forgetting models against empirical data on production breaks. Among the models compared was the learn-forget curve model (LFCM) developed by Jaber and Bonney(1996). In previous research, several studies have shown that the LFCM is advantageous to some of the models being investigated, however, Nembhard and Osothsilp (2001) found that the LFCM showed the largest deviation from empirical data. In this commentary, we demonstrate that the poor performance of the LFCM in the study of Nembhard and Osothsilp (2001) might be attributed to an error on their part when fitting the LFCM to their empirical data.

Optimal lot sizing under learning considerations: The bounded learning case

In this paper we present a mathematical model for determining the optimal manufactured quantity under the consideration of learning. The learning phenomenon we assumed, considers the situation of nonzero lower limit bounded learning to occur, known as the De Jong learning curve. We also present an efficient approximation for the closed algebraic form of the total holding cost expression developed by Fisk and Ballou. Because the learning phenomenon is believed to have economic and decision-making implications in an inventory management system, it is the objective of the study presented herein to investigate the effect of learning on both optimum production quantity and minimum total inventory system cost.

The effect of learning and forgetting on the economic manufactured quantity (EMQ) with the consideration of intracycle backorders

Abstract This paper, extends the work of Karwan et al. (1988) for the finite production model by studying the effect of intracycle, within cycle, backorders on the economic manufactured quantity and the total inventory system cost. This phenomenon, is studied under two cases: (1) full transmission of learning, and (2) partial transmission of learning. The optimum operating inventory doctrine is obtained by trading off procurement cost (i.e., the sum of set-up, material and labour costs) per unit time, the inventory carrying cost per unit per unit time, as well as the backorder cost per unit per unit time, so that their sum will be a minimum. Examples illustrating the calculation procedure are provided.