Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Dakotah Hogan, John Elshaw, Clay Koschnick, Jonathan Ritschel, Adedeji Badiru and Shawn Valentine
Additional contact information
Dakotah Hogan: Air Force Cost Analysis Agency, Deputy Assistant Secretary for Cost and Economics, Joint Base Andrews, MD 20762, USA
John Elshaw: Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Clay Koschnick: Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Jonathan Ritschel: Department of Systems Engineering & Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Adedeji Badiru: Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
Shawn Valentine: Estimating Research & Technology Advising Branch, Cost and Economics Division, Air Force Lifecycle Management Center, Wright-Patterson AFB, OH 45433, USA

Forecasting, 2020, vol. 2, issue 4, 1-23

Abstract: Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates.

Keywords: learning curve; forecasting; production cost; cost estimating (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-9394/2/4/23/pdf (application/pdf)
https://www.mdpi.com/2571-9394/2/4/23/ (text/html)

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:gam:jforec:v:2:y:2020:i:4:p:23-451:d:429151

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().