Large scale production systems contain dynamically unequal distributing lead times and improvement rates. This has made it very problematic to statistically predict the ability of global partners to meet common deadlines simultaneously This system behavior can be attributed to the effects of asynchronous stochastic learning curves (ASLC) among all partners. There is no known prior research that provides an analytical model of the synchronized performance among partners in a large scale production system (LSPS) with these ASLC effects.

In large scale production systems, integration of complicated products such as commercial airplanes brings unique concerns. These concerns involve the synchronized integration of customized products amongst processes of different lead times and stochastic learning curves. The source of the processes may include the system integrator, partners, suppliers, product designers, and custom feature configurers.

Determining statistically predictable performance based on the roles and influence of all participants across different domains of production systems has been more of an art than a science. The fact that people improve efficiencies when doing the same task repeatedly was addressed as the learning curve effect, which was first studied in the aerospace industry in the 1930s (Wright 1936). The larger the integrated production system, the more complicated it becomes to customize product design and deployment. Challenges in unstable individual production starting times have significant impacts on the productivity of the overall system. Other challenges in modeling this system are associated with the speed of process improvements, unequal lead times, and production rate fluctuations. Low volume and highly customized products of a large scale production system have not been previously analyzed through the ASLC approach combined with dynamic interactive discrete event simulation modeling.

This dissertation addresses research and findings associated with customized products during the early product inception stages in large scale production system integration with the ASLC effects. Data is collected and partially derived through an ASLC model with statistical linear regression analyses and augmented with some discrete event simulation modeling.

Deliverables of this research will provide a theoretical definition and practical model of the ASLC, and contributions in the following areas: ASLC impacts in large scale production system integration, discrete event simulation modeling methods in LSPS integration, and statistical presentation of ASLC in LSPS.

This research will further enhance analyses of LSPS in the following: mass customization influences, production rate change impacts to all parties in the supply chain, logistics in transporting large size items globally, customized product design and production considerations, customer ordering points for fixed and for customized configurations, impacts of unexpected events and change orders in LSPS, postponement of engineering-to-order decision points, and production balance between existing learning curves and increasing planned/unplanned changes.