Economic Design of Quality control by Using Triangular Fuzzy Number with Alpha Cut

Manufacturers today prioritize increasing output quality while reducing per-unit costs to remain competitive. Statistical Process Control (SPC) is a useful tool for boosting quality and output. However, decision-makers in industries face difficulties in cost and quality control due to uncertainty in data. Fuzziness can be applied to uncertain data to manage the economic design of a control chart, allowing for better control over the control chart's economic design. This study seeks to enhance the economic design of X control charts by using fuzzy set theory, specifically utilizing triangular fuzzy numbers for cost parameters and the signed distance approach for defuzzification. The objective is to enhance the adaptability of these charts in unpredictable situations. The proposed model enables the incorporation of cost parameters inside a fuzzy framework, with the objective of minimizing the control chart while adhering to the permissible limit. The applicability and improved accuracy of the model in optimizing control chart parameters for a glass bottle production process are exemplified by an effective illustration. This study highlights the need of integrating fuzzy logic into SPC. It suggests a technique that improves the cost-effectiveness and operational effectiveness of control charts in situations when data is uncertain and imprecise. . KEYWORDS :Statistical quality control, Economic quality control, Triangular fuzzy number, Signed-distance, Alpha cut.