The aim of this research is to apply machine learning techniques to the reconfiguration of automated manufacturing system, the
objective is to provide a model for fast decision making in automation processes in the manufacturing industry, to use the dataset
in product reconfiguration to predict a product, to design an intelligent model that could provide an easy and faster
reconfiguration of products in a manufacturing industry. The motivation towards this work is caused by the high rate of delay
in the production processes caused by the disturbance, taking proper corrective actions to complete the production orders on
time and to minimize the impact of the disturbances. Humans can break down during product production leading to reduction
and delay in product production, there is need for an intelligent model that does not require human effort, the model would be
able to take decision, automate processes and facilitate production processes. The data which is on the production of semiconductors in an industry will be analyzed with R and R-Studio platform sourced from UCI machine learning repository. The
methodology adopted in this project was SEMMA which stands for Sample Explore Modify Model Access which focuses on the
main modeling tasks in the project without venturing into the business understanding and deployment according to oreilly.com.
The expected result after the experiment is to develop an intelligent model for the reconfiguration of product in a manufacturing
company and also facilitate production and decision making in the company using the dataset on the production of semiconductor as a use case.