Date of Award

Fall 12-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Engineering Management & Systems Engineering

Program/Concentration

Engineering Management and Systems Engineering

Committee Director

Resit Unal

Committee Member

Holly Handley

Committee Member

Chuck Keating

Committee Member

Linda Vahala

Abstract

Traditional and current learning curve approaches, methods, and theories were deficient when addressing complex low-rate production systems. The purpose of this research was to address this problem and develop a learning curve approach that characterizes learning in low-rate production environments such as naval ship construction. This research identified the principal aspects that influence learning within this environment and developed a learning characterization more reflective of this environment.

There obviously exists a large body of knowledge covering learning. However, the research contained herein addresses learning as it relates to learning curves in low-rate production environments, such as naval shipbuilding. The various theories impacting learning curves has been explored in detailed as part of this research. Through the completed literature review, the researcher has confirmed that there is gap in the body of knowledge associated with learning curves specifically addressing the low-rate production of naval ships. The results of this research have addressed this gap in knowledge accordingly.

The research completed has a significant impact not only on the body of knowledge involving learning curves, but also on the expectations associated with the design, production, test, and delivery of complex naval ships. In addition, the results of the research were also a concise assessment of learning curve theories, their applicability, and the fact that, until now, there has not been published research addressing learning curves associated with the low-rate production environments.

The results of the completed research also identified the principal factors associated with learning curves in low-rate production environments. These principal aspects formed the basis of the development of a characterization of learning in low-rate production environments, which the researcher has developed the terminology of overall learning curve characterization (OLCC) defined by stability (S), procurement strategy (P), industrial and organizational culture (I), knowledge management (K), and demographic environment (E), which the researcher has also referred to this characterization as SPIKE. The results developed by this research was also generalizable to other low-rate production complex systems such as one-of-a-kind systems like the space program, oil well platforms, and other low production rate industries.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/f6zj-rf78

ISBN

9798368449067

ORCID

0000-0002-7406-6841

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