# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 4 Result
### Dynamic DEA  
In addition to the overall DEA, with respect to the CCR model, the dynamic analysis of industries' PSEI of all years, from 2015 to 2018, is conducted to understand the change  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Manufacturing Industry** & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\  & **IR** & **FR** & **SR** & **LP** & **PC** & **OR** \\ \hline
1. Food and Beverage & 0.0 & 26.4 & 73.6 & 16.4 & 45.0 & 38.7 \\
2. Textile (except apparel) & 38.4 & 35.3 & 26.3 & 41.4 & 17.0 & 41.8 \\
3. Wood & 0.0 & 44.1 & 56.0 & 0.0 & 7.3 & 92.7 \\
4. Pulp and paper & 29.7 & 28.5 & 41.8 & 0.0 & 0.0 & 100.0 \\
5. Coal and petroleum products & 50.0 & 47.0 & 3.1 & 16.3 & 8.3 & 75.5 \\
6. Chemicals & 0.0 & 47.4 & 52.6 & 15.3 & 0.3 & 84.4 \\
7. Rubber and plastic products & 15.6 & 34.6 & 49.8 & 8.1 & 8.4 & 83.6 \\
8. Non-metallic mineral & 0.0 & 18.3 & 81.7 & 24.6 & 0.4 & 75.0 \\
9. Basic metal & 66.7 & 14.6 & 18.7 & 44.2 & 11.0 & 44.8 \\
10. Metal fabrication & 0.0 & 80.2 & 19.8 & 16.3 & 29.2 & 54.6 \\
11. General machinery and equipment & 0.0 & 43.8 & 56.2 & 50.1 & 0.0 & 49.9 \\
12. Precision instruments & 0.0 & 46.3 & 53.7 & 66.7 & 0.0 & 33.3 \\
13. Electrical equipment & 0.0 & 41.2 & 58.8 & 8.0 & 0.0 & 92.0 \\
14. Automobile & 0.0 & 26.4 & 73.6 & 16.4 & 45.0 & 38.7 \\
15. Transportation equipment & 25.9 & 42.4 & 31.8 & 45.4 & 11.2 & 43.5 \\ \hline Average & 15.1 & 38.4 & 46.5 & 24.6 & 12.2 & 63.2 \\ Standard deviation & 22.0 & 15.7 & 22.6 & 20.1 & 15.6 & 23.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average of CCR weight of safety performance (input) and productivity (output) (2015–2018).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Manufacturing Industry** & \multicolumn{2}{c}{**Average**} & \multicolumn{1}{c}{**Rank**} & \multicolumn{2}{c}{**Average**} & \\  & **\% Score (CCR)** & **\% Score (BCC)** & **Rank** \\ \hline
1. Food and Beverage & 30.49 & 10 & 34.54 & 11 \\
2. Textile (except apparel) & 61.88 & 4 & 64.08 & 5 \\
3. Wood & 13.10 & 15 & 15.00 & 15 \\
4. Pulp and paper & 24.79 & 11 & 25.81 & 12 \\
5. Coal and petroleum products & 45.57 & 5 & 77.92 & 4 \\
6. Chemicals & 38.28 & 7 & 50.17 & 8 \\
7. Rubber and plastic products & 24.35 & 12 & 25.33 & 13 \\
8. Non-metallic mineral & 15.76 & 14 & 60.27 & 6 \\
9. Basic metal & 95.34 & 2 & 96.52 & 2 \\
10. Metal fabrication & 19.13 & 13 & 20.19 & 14 \\
11. General machinery and equipment & 31.35 & 9 & 53.58 & 7 \\
12. Precision instruments & 96.79 & 1 & 98.29 & 1 \\
13. Electrical equipment & 81.04 & 3 & 91.14 & 3 \\
14. Automobile & 40.62 & 6 & 42.90 & 9 \\
15. Transportation equipment & 35.49 & 8 & 37.15 & 10 \\ \hline Average & 41.86 & & 50.30 & \\ Standard deviation & 26.41 & & 27.18 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Score of PSEI-based DEA.  
in safety performance and productivity as presented in Table 7. As a result, the PSEI of 2017 is the highest than other years and the change in industries' PSEI could be found concerning trends of increase and decrease. Some industries' PSEI fluctuated, but we can find the overall trend from 2015 to 2018. For instance, several industries show an increase trend overall, such as _food and beverage, wood, non-metallic minerals, precision instruments_, and _electrical equipment_, while other industries indicate a decrease trend in general, such as _textile, coal and petroleum products, general machinery, and equipment_, and _automobile_. By using the dynamic analysis of PSEI, the policymakers prepare the enhancement strategy of productivity or safety performance, considering the weight of the inputs and outputs. This implication will be addressed in the Section 5.1.3.