A statistical process control approach to process diagnosis in discrete manufacturing environments

Suggests that there is justification for the use of a new methodology for process diagnosis which is simple to understand and realistic to implement. The control of quality of a process typically requires that multiple process variables be monitored simultaneously. Due to the multi‐dimensionality of the data collected, process diagnosis is complex and the data often are not efficiently integrated to capitalize on the wealth of available information. A two‐phased diagnostic approach for process diagnosis and identification of suspect causes for this multi‐dimensional problem is introduced in Krishnamurthi et al. (1993). Provides an in‐depth analysis of phase two of the statistical process control (SPC) diagnostic approach. Specifically, simulation is used to generate different cause and effect scenarios to determine the effectiveness of the SPC approach in correctly diagnosing a process disorder. The analysis utilizes analysis of variance to estimate the effect of various process variables, process steps, and associated out‐of‐control conditions on the performance of the SPC approach and its ability to diagnose correctly an out‐of‐control condition. As a result of these findings, the critical means are plotted and the findings are presented. Additionally, a comparison between the SPC approach and parsimonious covering theory (PCT) is made. Concludes that for the process scenarios considered, which are of practical size, the more simple approach of the SPC diagnostic approach is recommended.