Industry 4.0 as a Key Enabler toward Successful Implementation of Total Quality Management Practices

Industry 4.0 refers to the new technological development occurred at the industrial production systems. It evolved as a result of integrating Internet of Things, Cyber-Physical Systems, Big-Data, Artificial Intelligence, and Cloud Computing in the industrial systems. This integration aided new capabilities to achieve a higher level of business excellence, efficiency, and effectiveness. Total Quality Management (TQM) is a managerial approach to achieve an outstanding business excellence. There are several approaches to apply TQM principles at any organization. Industry 4.0 could be utilized as a key enabler for TQM especially by integrating its techniques with the TQM best practices. This paper suggests a theoretical framework for integrating Industry 4.0 features with the TQM principles (according to ISO 9000:2015 standards family) in order to open the door for further research to address the real impact of utilizing Industry 4.0 for serving the TQM implementation approaches

Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and effects analysis in a more efficient way as compared to the conventional techniques