## 5 Lean Methodology: Effort-Impact Matrix Application for Decision Making  
Lean methodology [17; 18; 19; 20; 21] has several principles. In this study, one of them was applied: optimizing the whole, the objective of which is to optimize the value stream of any process. Every business represents a value stream which is the sequence of activities required to design, produce, and deliver a product or service to customers, in this study the value stream is to deliver an efficient and fast result, deciding what parts will be prioritized for improving quality levels. Then the value stream defined in the study should be optimized. The main tool used to optimize the result of the AHP and ANP was an effort-impact matrix. The effort-impact matrix is a Lean tool used mainly by businesses to set priorities and determine ideas, strategies, and projects that will provide the best solutions. It is applied when facing multiple options for solutions to a specific issue. It provides a visual image about potential work and the expected benefit and determines the return of investment in an efficient way. Once parts to be improved were selected through multi-criteria analysis methods, an additional parameter was introduced, re-engineering cost of the parts selected, to focus the effort on those parts with low economic impacts for re-engineering that will cause a high impact for the customers. Re-engineering cost and AHP and ANP results are shown in Table 12. Re-engineering costs of each part were estimated by engineering and design departments of the manufacturer and the figures were extracted from the ERP of the manufacturing company. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Top 4 Result AHP** & **Top 4 Result ANP** \\ \hline Part 1 & Part 8 \\ Part 2 & Part 5 \\ Part 3 & Part 3 \\ Part 4 & Part 4 \\ Annual warranty cost reduction & EUR 2.186.353 & EUR 1.582.996 \\ \% Annual warranty cost reduction & 18.45\% & 13.36\% \\ Number of parts with failure reduction & 1.483 & 3.18\% \\ \% parts failure decrease & 2.83\% & 5.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison ANP and AHP results. An effort-impact matrix was designed, where the cost of re-engineering of each part was considered as the effort and the result of the AHP or ANP for each part was the impact. Each part was located in the matrix depending on its costs for reengineering and its prioritization ranking in AHP and ANP methods. In the matrix shown in Figure 9, the parts located in the top right area were \"the quick wins\" and it meant that with low reengineering cost, the manufacturing plant will have a high impact in terms of quality improvements and consequently in customer satisfaction. After analyzing the effort-impact matrix results, it may be stated that the quality department should prioritize Parts 1, 8, and 5 to apply re-engineering, as a second priority Parts 3 and 4 should be improved. Comparing the effort-impact matrix result with the ANP and AHP combined result, Part 2 was excluded, which has a very high reengineering cost of EUR 2.380.000, warranty cost of EUR 575.147, and number of incidents of 245, so applying improvements in this part has reengineering costs that are too high with low return of investment. This proves that the Lean effort-impact matrix maximized and optimized the result of the multi-criteria analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part Description** & **Re-Engineering Cost** & **AHP Result** & **ANP Result** \\ \hline Part 1 & 357.000 & 1 & 9 \\ Part 2 & 2.380.000 & 2 & 10 \\ Part 3 & 595.000 & 3 & 2 \\ Part 4 & 1.700.000 & 4 & 4 \\ Part 5 & 357.000 & 8 & 3 \\ Part 6 & 350.000 & 5 & 6 \\ Part 7 & 2380.000 & 9 & 5 \\ Part 8 & 357.000 & 6 & 1 \\ Part 9 & 140.000 & 7 & 7 \\ Part 10 & 1.700.000 & 10 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Re-engineering cost and multi-criteria analysis results. Figure 9: Effort–impact matrix.