Optimizing Quality Across Manufacturing and Storage Systems Strategies for Efficiency and Integrity

Abstract

The manufacturing and energy industry are typical complex large systems which cover a long cycle such as design, production chain, production or operation, after-sales, etc. This review is a thorough analysis of the current quality management practices in manufacturing and warehousing, which have undergone changes due to Industry 4.0. It combines articles about classical quality control models, conditions of utilization of statistical methods, and cutting-edge artificial intelligence-software-powered optimization tools. The evaluation shows that these technologies such as machine learning, deep learning, reinforcement learning, and digital twins will contribute to the processes becoming more stable, defects' catching being improved, and predictive maintenance being the most efficient. Other topics discussed include the impact of cloud and edge computing on the capacity of real-time decision-making to be enlarged and the decision-making to be more efficient. Besides, in warehousing, the review points out smart systems, RFID, blockchain, temperature–humidity monitoring, and automation as the main factors that contribute to operational excellence and sustainability. All in all, it is a matter of how integrated, data-driven, and environmentally friendly practices that are able to handle the quality, reliability, and resilience concerns of modern industrial systems.