# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 5 Discussion
### Implications
#### 5.1.2 Benchmarking for Improving PSEI  
As pointed out before, the DEA provides a powerful application for benchmarking the most efficient DMU. The DEA facilitates benchmarking by comparing the performance of similar units or organizations within an industry, enabling organizations to identify the best industry practices, set performance targets, and improve competitiveness. By finding the weaknesses of both safety performance and productivity in each industry, the policymakers gain insight into strengthening productivity and reducing incident injuries.  
Table 9 shows the DMU that can improve the PSEI by reducing inputs or by increasing outputs. There are two types of benchmarking revealed from DEA results. The first type is to decrease input variables only. In the chemical industry in the year 2017 and the non-metallic mineral in the year 2018, we identify the need for a decrease in IR, FR, and SR. For instance, by decreasing the IR to 2.12, the FR to 0.96, and the SR to 0.24, the chemicals industry became the most efficient DMU, such as like precision instruments in the year 2018. The second type is to control both inputs and outputs. For example, to achieve an efficiency of 100, the textile industry in2015 should have decreased the IR by 0.91, the FR by 0.39, and the SR by 0.17, and increased the LP by 0.01 and PC by 4.17. This type could meet the need for more investment in both safety measures and production innovation to achieve PSEI improvement. Other cases also represent the amount of change in input and output variables. Concerning these two types of improvement, the support plan for industries should be formulated by prioritizing the safety and productivity factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pattern** & **Respective Industries** & **General Production Type** \\ \hline
1. Worker–Operation ratio & Coal and petroleum, Basic metal & Continuous production \\
2. Injury–Labor productivity & Transportation equipment & Flow shop system \\
3. Injury–Operation ratio & Metal fabrication & Mass production \\
4. Loss time–Labor productivity & Precision instruments, Electrical equipment & Batch production \\
5. Loss time–Production capacity & Food and beverage, Automobile & Job shop system \\  & Non-metallic mineral, Rubber and plastic, & Continuous production \\
6. Loss time–Operation ratio & Wood, Pulp and paper, Chemicals, Textile, & Flow shop system \\  & General machinery and equipment & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Patterns of strength on safety performance and productivity.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type** & **Example of** **Landustry** & **Year** & **Score** & \multicolumn{2}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\  & & & **IR** & **FR** & **SR** & **LP** & **PC** & **OR** \\ \hline Most efficient & Precision & 2018 & 100 & 0 & 0 & 0 & 0 & 0 \\ DMU & instruments & 2017 & 82.49 & \(-\)2.12 & \(-\)0.96 & \(-\)0.24 & 0 & 0 & 0 \\ \hline \#1. Control & Chemicals & 2017 & 82.49 & \(-\)2.12 & \(-\)0.96 & \(-\)0.24 & 0 & 0 & 0 \\ only input & Non-metallic & 2016 & 66.22 & \(-\)9.11 & \(-\)4.1 & \(-\)1.37 & 0 & 0 & 0 \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Benchmarking for improving the performance of industries.