Fuente: Sustainability - Revista científica (MDPI) Sustainability, Vol. 18, Pages 5597: Analysis of Non-Quality Drivers and Their Root Causes in Development-to-Production Processes
Sustainability doi: 10.3390/su18115597
Authors:
Amir Gamliel
Yonit Barron

This study examines upstream non-quality drivers and their root causes in development-to-production transition processes, with a focus on New Product Introduction (NPI) environments characterized by high technological and organizational complexity. Accelerated innovation and compressed development cycles increase exposure to early-stage organizational and process-related deficiencies that may later materialize as quality failures and cost overruns. While extensive research addresses downstream quality failures and the Cost of Poor Quality (COPQ), fewer studies focus on structured prioritization of upstream drivers at early NPI stages, where empirical failure-based data remain limited. Building on a previously developed Quality Deviation Index (QDI), this study applies the framework empirically within a complex NPI context to support structured prioritization of upstream organizational and process drivers. The analytical approach integrates root cause classification, frequency-based prioritization, and QDI-derived ranking to organize observed patterns among drivers, without introducing new quality metrics or inferential models. The findings illustrate how QDI-based prioritization can be applied to identify highly ranked upstream drivers within the examined context, thereby supporting early-stage organizational awareness and structured decision consideration prior to the manifestation of downstream quality failures. Sustainability-related aspects, such as waste generation and rework, are discussed strictly as interpretive downstream implications of improved early-stage prioritization rather than as empirically measured outcomes. Overall, the study provides a context-bound empirical illustration of how structured prioritization mechanisms may be applied in early-stage NPI environments characterized by high uncertainty and limited failure data, without implying statistical generalization beyond the studied setting.