Decision-making trends in quality management: a literature review about Industry 4.0

Abstract

RS, Brasil *lucas_goecks@hotmail.com Abstract Paper aims: Due to the scarcity of research on current scenarios of quality management in the 21 st century, this article addresses the concepts of big data and Industry 4.0 for decision-making in quality control. Originality: This article contributes to completing categorizations and answering questions that have been previously suggested. Research method: This study presents a systematic literature review and qualitative data. The methodological framework shows the process of the selection and review of articles according to their alignment with the objective of the study. Main findings: Seventeen articles were selected to structure the study and were classified according the categories presented in the literature. The vast majority of the research gaps pointed out in previous review have been filled since their publication. Implications for theory and practice: In addition, this article presents new gaps to be filled and complements the literature and concepts about quality management neuro-fuzzy inference systems (ANFIS). The study identified critical adoption factors from a literature review and categorized them into technological, organizational, and environmental dimensions. Data were collected from 234 industrial managers who were involved in the decision-making process regarding IT acquisition in manufacturing companies in Malaysia. The results of the study showed that technological factors have the most considerable influence on the adoption of big data and the performance of companies. The study shows that complexity and technological resources for adoption are the main barriers to adopting technological factors. The study of Para et al. (2019) presents the methodology Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID), an interactive decision flow to improve the production stage. By putting the data analyst at the center of the workflow, this methodology helps improvement teams make decisions about what parts of a process need to be investigated and how to exploit that information for verifiable improvement in the production cycle. The methodology implementation is exemplified by a real case study of the automotive industry, where the detection of defects in an annealing process can be modeled as a classification problem in a highly unbalanced data set. The results obtained after applying the ASPPID methodology showed that the scrap rate was reduced, thus highlighting the crucial role of the data analyst in the plant management team. In the study developed by Tsai et al. (2019b), the authors present a structure for the study of the relationship between the manufacturing execution system (MES) and activity-based standard costing (ABSC). These results demonstrate the concept of ABSC, combining activity-based costing (ABC) and MES. Finally, the authors describe the ABSC mixed decision model using mathematical programming (with LINGO software). With limited resources, the optimal solution and the ideal profit are obtained. ABC implementation can meet the cost information needs of a company’s managers. However, ABSC can be a cost-effective tool to improve operational skills in terms of quality, cost, delivery, service, features and productivity in a modern and intelligent factory that uses high-technology uncrewed vehicles, advanced robots, and various sensors, among others. A roadmap was presented to operationalize a smart ABSC,