Optimization of zero defect manufacturing strategies: A comparative study on simplified modeling approaches for enhanced efficiency and accuracy

This paper presents a comparative analysis of three distinct Zero Defect Manufacturing (ZDM) strategies: Detection - Repair (DR), Detection - Prevention (DP), and Prediction - Prevention (PP). We evaluated these strategies based on their effectiveness in optimizing ZDM parameters, considering the specific needs and constraints of various manufacturing setups. Our analysis found that while DR and DP simulation models closely reflected original results, PP models demonstrated lower predictability, underscoring the need for further research and specialized modeling approaches. Nonetheless, the selection of an optimal strategy was determined to be context-dependent, hinging on the characteristics of the manufacturing system. The study also highlights the necessity of validating these strategies across diverse manufacturing setups to assess their performance and suitability. This research augments the existing body of knowledge on ZDM, offering insights to drive future investigations for the development of robust, accurate, and efficient ZDM modeling techniques. The ultimate objective is to move modern manufacturing industries towards a zero-defect environment, thereby enhancing their efficiency, reliability, and overall productivity.