## 6 Discussion  
The aim of this study was to develop a decision-making system that prioritizes the actions in quality departments within manufacturing plans, considering the total cost of the quality (tangible and intangible cost) and optimizing reengineering cost, time to market, and improving customer satisfaction. This study is scalable due to the applicability in manufacturing plants of any sector and was proven in the automotive industry  
Other authors indicate the complexity of measuring all quality costs, especially intangible costs and the high cost that reengineering would cause if the entire problem of quality is addressed.  
Nevertheless, both issues pointed out by other researchers were covered in this study in a novel way. A method to measure all quality costs through multi-criteria analysis and optimization of reengineering cost was found and undertaken using an effort-impact matrix.  
This innovative study combined two multi-criteria methods, AHP and ANP. These methods allow to include intangible cost in analyses of the root cause of quality issues. As it was mentioned, in quality departments only tangible cost is used for analysis as there is no available methodology for analyzing intangible ones and by not analyzing all quality costs, manufacturing plants are in a competitive disadvantage.  
Owing to the complexity and the importance of the problem, a complete decision support system based on multi-criteria analysis was developed. Two different multi-criteria processes (AHP and ANP) were applied to achieve the most efficient result, combining the results of both methods. This approach is totally innovative, analyzing the results of both processes and choosing a combined result that maximizes and improves the outcomes.  
The result of the AHP process prioritizes Parts 1, 2, 3, and 4. ANP prioritizes Parts 8, 3, 4, and 5. Therefore, both processes have a common result prioritizing Parts 3 and 4, so based on the previous analysis it may be stated that an appropriate decision will be found for improving quality levels in Parts 3 and 4, due to both methods determining the same outcome.  
After applying multi-criteria processes, the last significant parameter was introduced which was the cost of reengineering to improve quality in the parts.  
As all manufacturing plants have limited resources, they must invest in the most efficient actions. Based on the previous affirmation a Lean manufacturing tool, effort-impact matrix, was implemented to select the parts that will be more efficient if they are eligible for re-engineering actions. The result of the effort-impact matrix shows that the priority should be Parts 1, 8, and 5. If the manufacturing company prioritizes re-engineering actions in these parts, a significant improvement in terms of quality levels and customer satisfaction will be obtained with the lowest investment in reengineering.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parts Prioritized**} & \multirow{2}{*}{**Re-Engineering Cost**} & \multicolumn{2}{c}{**Warranty Spend**} & \multirow{2}{*}{**Number of Incidents**} \\  & & **(EUR)** & \\ \hline Part 1 & 357.000 & 608.854 & 859 \\ Part 8 & 357.000 & 205.560 & 275 \\ Part 5 & 357.000 & 375.083 & 2.535 \\ Part 3 & 595.000 & 503.982 & 113 \\ Part 4 & 1.700.000 & 498.370 & 266 \\ \hline Total & 3.332.000 & 2.191.850 & 4.048 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Result (parts prioritized) and financial statement.  
Parts 1, 8, and 5 were also included in the top 4 priorities of the AHP and ANP processes.