Statistically Analyzing Assembly Line Processing Times Through Incorporation of Product Variation

Timing methods and performance metrics are important in the heavily industrialized world we live in. Industrial plants use metrics to measure quality of production, help make decisions, and drive the strategy of the organization. However, there are many factors to be considered when measuring performance based on a metric; of which we will be analyzing the importance of product variation. We will be analyzing assembly line timings, whilst controlling for product variance, to show the importance differences between products makes in one’s ability to predict performance. In addition, we will be analyzing the current “statistical” methods used by an industrial partner and comparing it to a new method we will be creating. The data will be analyzed with statistical methods such as: Akaike information criteria, ANOVA, ANCOVA, residual analysis, multiple linear regression, and others, with most of the calculations being done with the statistical software, R