# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 5 Discussion  
### Implications  
#### 5.1.1 Pattern of Productivity-Safety Effectiveness  
As a result of DEA, we found the weight of input and output variables influencing the PSEI in each of the industries. By combining these variables, the patterns can be defined with strength in safety performance and productivity. As described in Table 8, six patterns are derived from the results of DEA, and the industries' patterns are found.  
First, the pattern of worker-operation ratio means the industry that has less IR and higher OR. _The coal and petroleum industry_ and _basic metal industry_ are included in this pattern because these two industries have mostly continuous production systems, streamlining production from input to output. The second pattern focuses on the reduction in FR with higher LP. The _transportation equipment_, such as rail, subway, and wagon, is made by order in general. It means that this production is customized and labor-intensive. Third, in _metal fabrication_, the management of operation ratio, is important because most firms are second- or third-tier to meet the demand of assembly companies. The fourth pattern includes the _precision instruments_ and _electrical equipment_ industries that require skillful workers using numerical tools. The LP is thus important in these industries, and there is less possibility of serious accidents that influence the loss of time because most of the tasks are used by the numerical tools. Fifth, in the _food and beverage_ industry and automobile industry, they have the advantage of high PC based on conveyor systems. Finally, the major patterns are unveiled as less loss time and higher OR. This result implies that most industries focus on increasing OR while trying to  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Industry** & **2015** & **2016** & **2017** & **2018** & **Trend** \\ \hline
1. Food and Beverage & 20.99 & 22.19 & 25.58 & 24.41 & Increase \\
2. Textile (except apparel) & 54.71 & 43.93 & 45.63 & 35.65 & Decrease \\
3. Wood & 8.2 & 9.34 & 10.13 & 10.57 & Increase \\
4. Pulp and paper & 17.6 & 17.7 & 19.13 & 17.2 & Stable \\
5. Coal and petroleum products & 41.1 & 37.62 & 46.38 & 30.05 & Decrease \\
6. Chemicals & 26.12 & 26.86 & 29.32 & 27.27 & Stable \\
7. Rubber and plastic products & 18.83 & 19.6 & 17.47 & 18.8 & Stable \\
8. Non-metallic mineral & 10.48 & 12.07 & 12.35 & 16.27 & Increase \\
9. Basic metal & 75.39 & 85.93 & 81.45 & 81.46 & Increase \\
10. Metal fabrication & 14.05 & 16.02 & 16.02 & 16.27 & Stable \\
11. General machinery and equipment & 20.21 & 21.44 & 22.85 & 16.27 & Decrease \\
12. Precision instruments & 63.89 & 90.46 & 70.08 & 100 & Increase \\
13. Electrical equipment & 58.24 & 58.86 & 63.65 & 63.65 & Increase \\
14. Automobile & 30.86 & 27.77 & 41.72 & 21.72 & Decrease \\
15. Transportation equipment & 23.64 & 25.48 & 23.01 & 21.72 & Stable \\ \hline Average & 32.29 & 34.35 & 34.98 & 33.42 & \\ \hline \end{tabular}
\end{table}
Table 7: Change in PSEI of industries from 2015 to 2018.  
reduce the time lost due to incidents. By finding the strengthening and weakness of both safety performance and productivity in terms of production type, the policymakers gain insight into enhancing productivity and reducing injuries.