Evaluating statistical process control techniques for short run production

Statistical process control (SPC) techniques play a very important role in improving process quality. Traditional Shewhart control chart techniques are considered very effective in managing process variation in a mass production environment. However, these techniques are inappropriate in a short run production environment where high varieties of products each with a very small batch size are produced. The purpose of this research is to analyze and identify which SPC techniques perform better under different process conditions in a short run production environment. Exponentially weighted moving average (EWMA), cumulative sum of deviations (CUSUM) and individual (I) control chart techniques are used with and without data transformation techniques. From the simulation results and subsequent statistical analysis, it is observed that EWMA control chart technique outperforms the other two SPC techniques under different process scenarios in a short run production environment.