Abstract
Zero Defect Manufacturing (ZDM) is a quality management strategy aimed at ensuring the optimal use of materials and machinery by analysing data from products and equipment. Recent advancements in data-driven technologies enhance ZDM by learning from past defects and utilizing predictive approaches to prevent future ones. However, the number of successful ZDM implementations in manufacturing remains limited, with few studies providing empirical evidence, especially outside the semiconductor and automotive sectors. Therefore, this study proposes an implementation framework which integrated the Cross Industry Standard Process for Data Mining (CRISP-DM) with the Design, Measure, Analyse, Improve, and Control (DMAIC) cycle that is then applied to a real case study at the Datwyler Pharma Packaging Italy s.r.l. This research provides practical insights into the implementation of ZDM, demonstrating how these methodologies can translate theoretical concepts into actionable procedures. Moreover, this research highlights the effectiveness of combining CRISP-DM and DMAIC and serves as a guideline for companies aiming to achieve zero defects by proposing an implementation model to guide successful ZDM applications in manufacturing companies and providing empirical evidence of this integration from an application in a manufacturing company.