Abstract

Traditionally the learning curve technique has been applied to the case of continuous production. In such cases the cost par unit declines by a constant or staged percentage due to the learning phenomenon. Although this technique has been an important management tool to predict cost and productivity, there are other factors that wil~ affect the learning curves and its forecasts. One of these factors is "forgetfulness," which is caused by program interruptions for a significant period of time. It is expected that due to the interruption a relearning process may have to take place. The objective of this report is to review the learning curve technique and explore the behavior of the curve in the case of the interrupted production. A "forgetting" factor 1s incorporated in the original learning function. This factor depends on the elapsed time between endina of the job and starting of the next, as well as the operator level of experience prior to interruption. For each job a learning curve is determined. This curve decreases exponentially from initial cycle time to the time in which learning is completed. The curve is used together with a similar exponential "forgetfulness" curve to calculate an allowance for starting a new job or restarting a previous job after a lapse. For a successful production cost estimation and plannina system, industrial engineers and managers need to consider the effect of the "forgetting" factor on learning curve in a batch or discrete production environments. A computerized learning curve program with particular application to interrupted production environment is developed for an IBM Personal Computer and compatibles. This proaram uses imformation retrieved from a data base and the user's input to calculate learning and its associated forgetfulness curves. The program estimates man-hrs., forecasts manpower requirements and unit cost for selected production quantities. Despite the apparent complexity of some of the calculations, the program is developed so that it can be used to estimate small- to medium-sized batch problems without the extensive knowledge of either learning or forgetfulness algorithms. The information required as an input to determine the learning curve and forgetting factor is gathered from the shop floor by time study or by job comparison.

Notes

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Graduation Date

1988

Semester

Spring

Advisor

Hosni, Yasser A.

Degree

Master of Science (M.S.)

College

College of Engineering

Department

Industrial Engineering and Management Systems

Format

PDF

Pages

74 p.

Language

English

Rights

Public Domain

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Identifier

DP0025791

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

Accessibility Status

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