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.

No abstract

Industry 4.0 is a recent trending topic which is widely being discussed in research from different perspectives. Industry 4.0 refers to the fourth industrial "revolution"; some literature defines it as a further industrial "evolution", resulting from the integration of innovative technologies such as the Internet of things, cyber-physical systems, big data, robotics, artificial intelligence, and cloud C computing with industry. This development hascreated new techniques to improve different segments of industry. Such integration has significantly improved production systems, created smarter and highly responsive supply chains, and boosted the product quality due to instant, massive, and real-time quality control systems. Industry 4.0 has a strong impact on different socioeconomic fields, thus, several researchers increasingly focus on addressing different impacts on different levels. Accordingly, this paper aims at discussing the impact of Industry 4.0 on quality management systems and practices, such as quality control, quality assurance and total quality management. The paper reviews the best quality practices and proposes a modern framework of an (Industry 4.0-Quality) integrated model, where Industry 4.0 is directly linked with quality practices to produce a new level of quality practices.

Handicraft production is usually chaotic and difficult to monitor, since its products and manufacturing processes are complex. As all the manufacturing steps rely on varied skill levels of the workers, the situation is even more stochastic. There are several common problems, such as inappropriate production method, line unbalance, excessive stock, lack of production planning and control phases, etc. They stem from the lack of suitable operation model, redundant workforce usage, and insufficient internal training activities, which lead to the waste of human resources. In this paper, a roadmap to improve the operational efficiency of handicraft manufacturing is suggested, using Lean-Six Sigma methodology and tools. A case study is conducted in a Vietnamese firm to show the validity of the approach.

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.