Application of Neural Network in Shop Floor Quality Control in a Make to Order Business

Abstract

A make to order business has to produce the products that are customized to the customer's current need. The customization can be realized by assembling different standard parts with various ‘configurations’. The oil field service industry is a typical example where most products produced are cylindrical assemblies made up of standard parts customized in their size, material specifications, coating specifications, and threading suited for the particular load rating and environment. As business cycles go up and down, hiring and firing of personnel is the routine of the day. Thus, it is very hard to keep experienced inspectors due to high turnover of the staff on shop floor and thus intensive endeavor to train the inspectors for the same recurrent problems of the same standard parts is required. This paper proposes a neural network model to help the industrial practitioners address such a concern. The neural network is trained with ample ‘judgment calls’ from the manufacturing experts so that it can properly generate the decision to ‘scrap’, ‘rework’ or ‘use as is’ for the inspected parts. The real quality data from an oil field service industry is used to validate the effectiveness of the proposed tool.