Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0

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

“…The articles of Ngo & Schmitt (2016), Ren et al (2019), Kozjek et al (2018) and Rossit et al (2019) considered issues with high technological demand (CBS, IoT, BD) but low human involvement and were classified as studies focusing on micro technology and macro human involvement. Due to the high human and technological involvement, the studies of Tsai & Lai (2018) and Para et al (2019) were classified as addressing microlevel engagement. Tsai & Lai (2018) developed mathematical programming with TOC tools and ABC to prepare and make decisions regarding the production plan for a broad mix of products.…”

“…Tsai & Lai (2018) developed mathematical programming with TOC tools and ABC to prepare and make decisions regarding the production plan for a broad mix of products. Para et al (2019) performed, through AI, the detection of defects in an annealing process, remodeling the programming to correct unbalanced production. Yadegaridehkordi et al (2018) and Irani et al (2018) were also classified as addressing microlevel engagement because they used computational simulation methodologies to aid decision-making in companies.…”

“…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.…”

“…A real case study was used to validate the study by Para et al (2019), in which a reduction in costs (above 43%) was achieved with the use of sensors, achieving a non-quality rate (NQR) lower than the target value imposed at the beginning of ASPPID (Analyze, Sense, Preprocess, Predict, Implement, and Deploy). Thus, to improve the results obtained, the authors recommend the hybridization of existing production models for data mining and the development of agile software in the ASPPID methodology.…”

“…For future studies, we suggest continued research on decision-making and data analysis in Industry 4.0. Authors such as Para et al (2019) and Yadegaridehkordi et al (2018) suggest the refinement of their methods for decision-making in this new industrial context. Referring to data analysis, Para et al (2019), Telukdarie et al (2018), Kozjek et al (2018), Kampker et al (2018), Irani et al (2018) and Ngo & Schmitt (2016) highlight the importance of continued studies related to data mining, big data, and data analytics.…”