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Investigating the influence of learning curve theory on production process optimization

Plakas Aristotelis

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Summary

This thesis delves into the significance of learning curves in optimizing the performance and productivity of construction activities. The theory of learning curves is widely used in the industrial sector as it offers solutions to demanding and time-consuming tasks, such as creating schedules, optimizing costs, reducing production times, and estimating future performances.For the implementation of the study, five learning curve models are selected for analysis. The research methodology includes the examination of these models using unit data and cumulative average data. The models under consideration include the straight-line model or Wright's model, the Stanford 'B' model, the cubic model, the piecewise model, and the exponential model. The analysis focuses on the models' ability to adapt to historical data of completed tasks and their capability to forecast future performances. The application of the models mentioned above in a production process related to defense systems revealed the following findings: Regarding the models' ability to adapt to historical data, results suggest that the cubic model is more precise when using both data representation techniques. In examining the models' ability to predict future behaviors, the piecewise model stood out when using unit data, while the straight-line model showed the best performance when using cumulative average data.The conduct of this research offers a deeper understanding of the usefulness and effectiveness of applying the learning curve theory to a construction process, while simultaneously highlighting the importance of choosing the optimal model for the optimization of the production process.

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