Quality control is essential in the manufacturing and production sectors. This study advances Incoming Quality Control (In-QC) by proposing a new data-driven sampling plan that optimizes decision-making for quality inspection with a system of partial control. This system is characterized by specific parameters that facilitate strategic decisions. The methodology’s efficacy is validated through targeted simulations, followed by a practical application in a real-world case study. This dual-phase evaluation underscores the approach’s utility and effectiveness, aiming to improve In-QC practices with significant implications for the industry.

This paper proposes a novel approach for the improvement of production quality in knitting workshops in the textile industry. The method is based on two steps. The first step consists of analyzing the performance of each operator to detect the different types of quality defects on the different machines of the workshop. The second step consists of optimizing the assignment of the operators to the different machines taking into account their performance in order to maximize the detection rate of the quality defects. The evaluation of the operator's performance is carried out using a multi-criteria analysis method called TOPSIS: Technique for Order by Similarity to the Ideal Solution. This performance indicator serves as a crucial input for the second proposed method, which is an optimized model for assigning operators in the workshop taking into account different constraints. The proposed approach is tested based on a real industrial configuration and the obtained results show its effectiveness.