1. Introduction
According to a CNBC (Consumer News and Business Channel) analysis of the economic outlook report released by the World Monetary Fund (IMF), South Korea, which ranked 12th in 2019, ranked 10th in the world in terms of nominal gross domestic product (GDP) after the outbreak of COVID-19 [
1]. Meanwhile, the number of industrial accidents in Korea has been steadily increasing in proportion to its economic growth, from 89,848 in 2017 to 102,305 in 2018, and again to 109,242 in 2019.
In addition, the number of deaths from work accidents was 964 in 2017, 971 in 2018, and 855 in 2019, representing the highest level among OECD countries each of the years [
2]. Accidents in these industrial sites are not only associated with economic losses but are also problems in which human dignity is not being respected, and safety in the workplace is a basic right that must be protected.
Table 1 lists the safety and health laws that different countries (G5) have passed in an attempt to prevent occupational accidents. In this regard, South Korea has strengthened the level of punishment given to companies that have experienced serious industrial accidents by enacting the ‘Serious Accident Corporate Punishment Act (effective on 27 January 2022)’ to prevent serious industrial accidents.
In addition, the UK has enacted the ‘Corporate Manslaughter and Corporate Murder Act (effective on 6 April 2008)’, which imposes unlimited fines on companies that have caused fatal accidents.
Table 2 shows the legal punishment criteria for serious accidents in South Korea and the UK [
3].
Despite such strengthened safety and health regulations, there is a limit to how many occupational accidents can be reduced solely through state supervision and regulation, and there is an urgent need for companies to make voluntary efforts to prevent occupational accidents [
4]. The main cause of continuous safety accidents in companies has been determined to be the continuous repetition of unsafe acts, such as (1) neglecting unsafe conditions, (2) work methods, or (3) poor wearing of protective equipment [
5]. This is proof that safety activities are either not being properly carried out in the field or are not being carried out effectively. As such, according to the DuPont Bradley Curve, the current level of domestic manufacturers appears to be at the level of ‘supervision dependent’, where employees’ voluntary safety practices are insufficient [
6,
7,
8,
9,
10]. Safety practices in the field increase the effectiveness of safety management, and the results are shown in the form of safety performance, which is reported to be a key factor in the evaluation of a workplace’s safety culture [
11].
In a related study, in 1999, Jones reported that accidents resulting from ‘unsafe behavior and unsafe conditions’, as opposed to safe practices, disrupt or impede the flow of production activities, regardless of the actual losses [
12]. In 2004, Chen and Yang proposed the development of a predictive risk index based on unsafe acts and unsafe conditions in process industries, and they used this index as a safety performance indicator. It was reported to be effective in predicting the occurrence of accidents in advance by predicting the risk according to the geometric mean of the risk probability, the frequency of occupational exposure, the number of risk persons, and the estimate of the maximum possible loss [
13]. In 2016, Fruggiero reported that workplace safety practice increased productivity by reducing the cost of preventing accidents and managing performance reductions in production activities [
14]. In 2016, In-gie Hong, in a study examining safety culture evaluation methods, showed that an electronic company neglected safety management in favor of production management, quality control, and cost reduction. In particular, the results showed that safety awareness did not lead to actual field participation due to negative or passive attitudes regarding most safety and health activities, such as discovering potential risks and reporting near-miss accidents [
15]. In 2018, Ebrahim analyzed various risk factors such as ‘probability at risk’, ‘frequency of work exposure’, ‘number of persons at risk’, and ‘severity of consequence’ according to unsafe act and condition observations through work observation in the steel industry. Based on this, a customized predictive risk index was calculated, and the occurrence of safety accidents was predicted in advance [
16]. In 2018, the CCPS (Center for Chemical Process Society) stated that safety management indicators can be classified into three types of indicators: lagging metrics, near-miss metrics, and leading metrics. It has been reported that leading metrics can be a forward-looking indicator of safety management performance and help prevent potential accidents [
17]. In 2020, Pam Walaski recommended discontinuing the use of lagging indicators based on the outcome of accidents, such as the number of incidents, incident rate, and lost time rate, because of their insufficient preventive effect [
18,
19,
20,
21]. It was also recommended that companies introduce leading indicators such as participation rate in various safety activities such as safety education, risk assessment, unsafe act inspection, and high-risk improvement to prevent accidents [
22,
23,
24]. The previous studies described above are expected to be useful in predicting safety accidents through unsafe act and condition observations and allowing for advance warning of possible risks in the workplace. However, either these studies merely warn of the risk of accidents, or their applications are limited to specific industries such as chemical and process industries. Therefore, quantitative and systematic safety management based on the Safety Practice Index (SPI) is required to reduce safety accidents in the manufacturing industry.
In this study, repetitive factors for unsafe acts, which represent a major cause of safety accidents, were classified into: (1) lack of safety awareness among members and (2) insufficient safety management system establishment for risk factors. To calculate the safety practice index (SPI), the safety activity index (SAI) was derived from safety activity participation, and the risk management index (RMI) was derived from the risk management level. SAI and RMI can be derived as a geometric mean of each detailed item, and SPI is expressed as the product of SAI and RMI. After classifying the calculated SPI into four grades and grading the safety practice level (SPL), the correlation was verified by comparing the SPI measurement results in ‘18 and ‘19 with safety accidents. After that, SPI was improved in ‘20 by supplementing the weaknesses of the SPI measurement results in ‘18 and ‘19 and proved effective in reducing safety accidents. As a result, it was confirmed that the SPI rating can be used as a quantitative indicator for accident prevention, and it was found to be effective.
3. Consideration of Application Cases
The feasibility was verified by applying the quantitative evaluation of safety culture scale and safety improvement in this study to the field for company A.
Table 11 presents the status of the unit factory in Company A. Company A is considered to be suitable for the evaluation of this study as it involves seven unit factories that are independently operated and which apply different safety management techniques.
To verify the correlation between the SPI calculation result and safety accidents, the statuses of safety accidents in ‘18 and ‘19 were analyzed using the evaluation suggested in this study for unit factories A to G in the workplace applied to the case study. The ‘20 evaluation proved the effectiveness of the SPI application by analyzing changes in the occurrence of safety accidents after intensively strengthening the items that were insufficient in the ‘18 and ‘19 SPIs.
3.1. ’18 Safety Practice Index (SPI)
The safety activity and risk management detail items for each unit plant in 2018 were measured as presented in
Table 12 and the results are shown in
Figure 4.
The average grade for each item was measured as safety education (SEd) 3, safety event (SEv) 2.71, emergency drill (EDr) 1, risk assessment (RAs) 3.29, and both high-risk improvement (HRi) and unsafe act (UAc) 2.71. Therefore, the results of calculating the safety activity index (SAI) and risk management index (RMI) are as follows:
The result of calculating the safety practice index (SPI) by the product of the two indices (SAI, RMI) measured according to Equation (3) is as follows:
The SPI calculation results for each unit factory were measured as presented in
Table 12 and the results are shown in
Figure 5a; the results are also compared with
Figure 5b, the number of accidents per unit factory, to confirm the correlation with safety accidents. When SPI was grouped into safety practice level (SPL), the A, B, and F unit factories were grade I, and an average of 5.67 safety accidents occurred in this group. The E and G unit factories were grade II, and an average of five safety accidents occurred in this group. The C and D unit factories were grade III, and an average of 0.5 safety accidents occurred in this group. There was no unit factory corresponding to grade IV.
3.2. ’19 Safety Practice Index (SPI)
The safety activity and risk management detail items for each unit factory in 2019 were measured as presented in
Table 13 and the results are shown in
Figure 6.
The average grade for each item was measured as safety education (SEd) 3.71, safety event (SEv) 2.86, and emergency drill (EDr) 1, risk assessment (RAs) 4.14, high-risk improvement (HRi) 2.71, and unsafe act (UAc) 3.71. Therefore, the results of calculating the safety activity index (SAI) and risk management index (RMI) are as follows:
The result of calculating the safety practice index (SPI) by the product of the two indices (SAI, RMI) measured according to Equation (3) is as follows:
The SPI calculation results for each unit factory were measured as presented in
Table 13 and the results are shown in
Figure 5a. To confirm the correlation with safety accidents, these results were compared with those in
Figure 5b, which shows the number of accidents per unit factory. When SPI was grouped into safety practice levels (SPL), the A, B, E, and F unit factories were grade II, and an average of 4.25 safety accidents occurred in this group. The C, D, G unit factories were Grade III, and an average of 2.33 safety accidents occurred in this group. There were no unit factories corresponding to either grade I, or grade IV.
Based on the analysis of the two years of ‘18 and ‘19 presented thus far, it was verified that the higher the SPL grading, the fewer safety accidents, and the lower the SPL grading, the more safety accidents. In the safety practice index of ‘18 and ‘19, emergency drill (EDr) as a detailed item of safety activity was found to be low with a grade average of 1 in ‘18 and ‘19. In addition, as a detailed item for risk management, high-risk improvement (HRi) was low with an average grading of 2.71 in ‘18 and ‘19, so it was selected as an item subject to intensive management in ‘20, and it was applied to improve SPI and evaluate the safety accident prevention effect.
3.3. ’20 Safety Practice Index (SPI)
In ‘20, the results for intensive supplementation for emergency drill and high risk improvement, which are the items with the lowest index grade average in ‘18 and ‘19, are as follows.
Conduct emergency scenario and drill evaluation with the presence of the safety department to increase each unit factory’s participation rate in emergency drills.
Strengthening periodic inspections through safety meetings to increase high risk improvement.
The grades for safety activity and risk management detail items in ‘20 for each unit factory with the above contents improved were measured as presented in
Table 14 and the results are shown in
Figure 7. The grade average for each item was measured as safety education (SEd) 3.86, safety event (SEv) 3.0, emergency drill (EDr) 1.57, risk assessment (RAs) 4.86, high-risk improvement (HRi) 4.71, and unsafe act (UAc) 3.57. Therefore, the results of calculating the safety activity index (SAI) and risk management index (RMI) are as follows:
The SAI for 2020 was 2.63, representing an improvement of 25% compared to the average of 2.11 in ‘18 and ‘19; this was largely due to a 57% improvement in the emergency drill measurement value from 1 in ‘18 and ‘19 to 1.57 in ‘20. The RMI for 2020 was 4.34, up 36% from the average of 3.18 in ‘18 and ‘19; this was largely due to the 73% improvement in the high-risk improvement measurement value from 2.71 in ‘18 and ‘19 to 4.7 in ‘20.
The result of calculating the safety practice index (SPI) by the product of the two indices (SAI, RMI) measured according to Equation (3) is as follows:
The SPI in ‘20 was 11.41, which was a 70% improvement from the average of 6.72 in ‘18 and ‘19. The SPI calculation results for each unit factory were evaluated as presented in
Table 14 and the results are shown in
Figure 8a. These results were compared with those in
Figure 8b, which depicts the number of accidents per unit factory to confirm the correlation with safety accidents. When SPI was grouped into safety practice level (SPL), the B, F, and G unit factories were grade III, and an average of 3.33 safety accidents occurred in this group. The A, C, D, and E unit factories were Grade IV, and an average of 1.75 safety accidents occurred in this group. There was no factory corresponding to either grade I or grade II. As a result, it was confirmed that it is effective in improving SPI and preventing safety accidents by intensively strengthening emergency drills and high-risk improvement, which were the items with a low average index grade in ‘18 and ‘19. However, the safety accident reduction pattern according to the improvement of SPI requires long-term data acquisition to increase the accuracy of each safety practice index (SPI).
4. Conclusions
In this study, after calculating safety practice as an index from safety activity and risk management data, the trend of the occurrence of safety accidents was verified, and the insufficient items of the SPI index were intensively improved and supplemented to increase the SPI, as well as effectively applied for the prevention of safety accidents.
First, the safety practice index, an integrated evaluation index of safety practice, was proposed by introducing SAI, an index that oversees safety activity, and RMI, an index that oversees risk management; further, quantified grades were presented to increase the safety practice index.
Second, as a result of reflecting the safety practice index (SPI) for seven factories, it was confirmed that the safety practice level grade and safety accidents are quantitatively inversely proportional, revealing that it can be used as an effective index for safety management. In addition, by reinforcing leadership and safety policies such as classifying and managing the level of safety management for a specific period or department, weaknesses in safety management can be supplemented with strengths and used for safety accident prevention activities.
Third, to increase the safety activity index, it is necessary to expand participatory education, conduct regular emergency drills, and induce voluntary participation in safety events. To increase the risk management index, these results indicate that risk assessment, high-risk improvement, and the activation of unsafe act checks should be supported.
Fourth, by setting the safety practice index proposed in this study as a key performance indicator (KPI) and reflecting it in advance in safety policy, this study proved that it can be used for workplace safety management and the prevention of safety accidents.
Finally, reflecting this evaluation, the number of industrial accidents in 2020 decreased by nine cases compared to the average of 26 in 2018 and 2019, resulting in a decrease in industrial accident losses by KRW 22.5 billion (based on KRW 250 million per person in 2018).