ZQC Technique through Poka-Yoke Mechanism Under Data Mining in Steel Industries (Direct Reduction Iron- Production of Sponge Iron) Case Study (Gol Gohar Iron and Steel Development)

The industrial sector consistently produces vast amounts of data, accumulating in databases and data warehouses across various domains such as process design, materials, assembly, manufacturing, quality control, process control, scheduling, error diagnosis, repairs, and customer relationship management. In particular, data mining has emerged as a potent tool for identifying patterns and acquiring knowledge in industrial processes and activities, specifically focusing on the steel and iron industries. This scientific article analyzes the impact of error prevention techniques, specifically "mistake-proofing" or "error prevention," on various indices related to sponge iron production. The research examines several key indices, including the effective utilization of machinery and human resources, total unemployment, breakdown time, production stoppage due to rework, and the correction of waste and lost production index. The findings of this study demonstrate the effectiveness of implementing the Poka-Yoke mechanism in enhancing all the above indices, thereby improving production efficiency. Consequently, this improvement contributes to Iran's enhanced competitiveness in the global steel industry markets.

کلیدواژه ها:

Data Mining ، Poka-Yoke ، Patterns ، Decision Making ، Reduction Iron ، Zero Quality Control Strategy