Applying systematic diagnosis and product classification approaches to solve multiple products operational issues in shop-floor integration systems

Enterprises usually install a computerized information system to improve production efficiency. However, operational problems still occur from time to time, with different products usually requiring different solutions. This study discusses operational problems and proposes a diagnostic method for integrated shop-floor systems that manufacture multiple products. This study uses the multivariable statistics method to conduct a relevance analysis to determine the important attributes that influence production operations. Then, a neural network is used as the diagnostic system to detect operational problems. Support vector learning machines (SVM) are used to confirm the correct product classification. Finally, the diagnostic results are stored in a case-based reasoning system database for future use.