Learning Curve and Rate Adjustment Models: Comparative Prediction Accuracy Under Varying Conditions

O. Douglas Moses
Additional contact information
O. Douglas Moses: Naval Postgraduate School, Department of Administrative Science

Chapter 4 in Cost Analysis and Estimating, 1991, pp 65-102 from Springer

Abstract: Abstract Learning curve models have gained widespread acceptance as a technique for analyzing and forecasting the cost of items produced from a repetitive process. Considerable research has investigated augmenting the traditional learning curve model with the addition of a production rate variable, creating a rate adjustment model. This study compares the predictive accuracy of the learning curve and rate adjustment models. A simulation methodology is used to vary conditions along seven dimensions. Forecast errors are analyzed and compared under the various simulated conditions, using ANOVA. As in all simulation studies, findings must be interpreted in light of the assumptions underlying the simulation. Overall results indicate that neither model dominates; each is more accurate under some conditions. Conditions under which each model tends to result in lower forecast errors are identified and discussed. This work was sponsored by the Cost Estimating and Analysis Division of the Naval Sea Systems Command and the Naval Postgraduate School.

Keywords: Production Rate; Unit Cost; Fixed Cost; Cumulative Quantity; Estimation Period (search for similar items in EconPapers)
Date: 1991
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-3202-5_4

Ordering information: This item can be ordered from
http://www.springer.com/9781461232025

DOI: 10.1007/978-1-4612-3202-5_4

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().