In majority they have been concentrated on single-stage processes such as statistical process control (SPC), design of experiments (DoE), acceptance sampling procedures, six-sigma and lean tools. This led to development of several methodologies for quality control and improvement. This requires rapid reaction to various events in the production process. In modern market conditions, manufacturers need to quickly deliver high-quality products. We illustrate the proposed method on the example of a flow-shop system with different types of product defect problem. Thus, the proposed method includes strategies such as detection, repair, prediction and prevention for defect-free production. Moreover, the collection and analysis of data related to the occurrence of disturbances in the production process helps the management board in making decisions based on analysis gathered and stored data. Production defects are detected and repaired, and consequently, production delivers components without defects, and in the shortest possible time. In particular, it uses the model switching method and combines defect detection, heuristics construction and decision support containing predictions of disturbances in the production process and enabling their prevention. Our proposal is based on the formal Algebraic-Logical Meta-Model (ALMM). We propose formal method to create predictive-reactive schedule for problems with defect detection and repair. However, most of ZDM applications focus on using the technological achievements of Industry 4.0 to detect and predict defects, forgetting to optimize the schedule on the production line. ZDM aims to improve the process efficiency and the product quality while eliminating defects and minimizing process errors. Paper proposes a formal approach for solving scheduling problems with unexpected events as extension of general frameworks for Zero-Defect Manufacturing (ZDM) strategy. A defect prevention is a part of manufacturing company practice.
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