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Abstract
The automotive industry continues to face numerous challenges that demand ongoing process improvement and system optimization. Incidents reported in the media have highlighted the relevant impact regarding companies’ sustainability and brand image and consequently, the urgent need for stronger traceability mechanisms and faster response capabilities throughout the supply chain. This paper focuses on enhancing a key logistic process within automotive companies – the incoming material inspection – by applying the DMAIC (Define, Measure, Analyse, Improve, Control) methodology. This process is critical, as it directly influences production flow, product quality, and overall supply chain efficiency. The DMAIC approach was selected due to its proven effectiveness in driving systematic process improvements, reducing defects, and establishing measurable control standards across manufacturing and logistics operations. Each phase of the methodology provides a structured framework with clearly defined objectives, control mechanisms, and deadlines to ensure consistent progress and accountability. Implementation of the DMAIC methodology resulted in a more transparent, standardized, and efficient inspection process. Quantitative and qualitative outcomes demonstrated significant improvements in process control and traceability. Furthermore, the initial project objectives were fully achieved and, in several instances, surpassed – validating the robustness of the applied methodology. This analysis highlights the critical role of structured quality improvement frameworks, such as DMAIC, in managing increasingly complex logistics operations. In an industry shaped by digital transformation and sustainability imperatives, adopting data-driven methodologies fosters agility, consistency, and a culture of continuous improvement is essential. Beyond enhancing operational efficiency, such approaches strengthen collaboration, accountability, and long-term competitiveness within the automotive sector.
Suggested Citation
Cristina-Mihaela FLEACĂ & Radu-Marian BUJDOIU & Irina SEVERIN, 2025.
"Logistic process improvement in automotive industry using quality tools,"
Cognitive Sustainability, Cognitive Sustainability Ltd., vol. 4(4), pages 59-69, December.
Handle:
RePEc:bcy:issued:cognitivesustainability:v:4:y:2025:i:4:p:59-69
DOI: 10.55343/CogSust.21122
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JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
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