Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity

Abstract

Probabilistic intelligence is vital in current management and technology. It is simpler to persuade readers when a management or engineer reports connected difficulties with objective statistical data. Statistical data support the evaluation of the true status, and cause and effect can be induced. The rationale is proven using deductive logic and statistical data verification and induction. Quality practitioners should develop statistical thinking skills and fully grasp the three quality principles: “essence of substance,” “process of business,” and “psychology.” Traditional quality data include variables, attributes, faults, internal and external failure costs, etc., obtained by data collection, data processing, statistical analysis, root cause analysis, etc. Quality practitioners used to rely on these so-called professional qualities to get a job. If quality practitioners do not keep up with the steps of times, quality data collection, organization, analysis, and monitoring will be confusing or challenging. Increasingly, precision tool machines are embedded in various IoTs, gathering machine operation data, component diagnostic and life estimation, consumables monitoring and utilization monitoring, and various data analyses. Data mining and forecasting have steadily been combined into Data Science, which is the future of quality field worth worrying about.