(42c) A New Method for Safety Culture Evaluation in Process Systems

A new method for safety culture evaluation in process systems

Introduction

After Chernobyl accident, the evaluation conducted by the International Atomic Energy Agency (IAEA) Safety Culture is being defined as a necessary characteristic to reach safety in nuclear installations and therefore it must be possible to assess its status in order to improve it and maintain it in optimal levels [1]. Since then, many efforts have been made to assess safety culture in organizations [2-6], aiming at the development of a safety culture management approach and recognizing that the safety culture of an organization shapes people's underlying behaviors [7, 8] with immediate implications for all other necessary safety measures. 

According to IAEA [1] the safety culture comprise three levels: (1) the work environment created by local management, (2) the attitudes of workers, and (3) the safety/operational experience at the plant. Therefore, the establishment of a positive relationship between safety culture and the actual safety of the organization depends on safety performance indicators that can be used to infer changes in safety culture and consequently predict changes in safety performance. The challenge is to identify measurable organizational factors or attributes that influence safety.

The IAEA [1] proposes a self-assessment of safety culture for use by nuclear organizations. The method uses the Safety Culture Three Level model developed by Schein [9]: artifacts which are visible, such as aspects of layout and the space where people work; espoused values which can be determined, such as equality of opportunity, teamwork, safety as a priority, etc.; and basic assumptions which are tacit and intangible such as the nature of time and space and human activities. Safety culture evaluation is then performed by means of questionnaires or interviews to collect information on employee attitudes, opinions or perceptions related to each component level of culture linked with the safety culture characteristics. 

However, the safety culture questionnaires normally used become longer than expected, non user-friendly, and sometimes they are difficult to interpret. Another problem of many other methods for the safety culture evaluation [2] do not deal with the subjectivity aspect of safety culture based on people's perceptions, and they also do not present measures of the consistency among evaluators. To deal with these issues, we develop a new method for safety culture evaluation based on Fuzzy Set Theory (FST). The method is based on leading safety performance indicators, defined according to the six safety culture attributes:

1. Top-level commitment to safety;
2. Organizational learning;
3. Organizational flexibility;
4. Risk Awareness; 
5. Just culture;
6. Emergency preparedness.

Method

The method employs leading safety performance indicators based on six safety culture attributes to evaluate safety culture. It uses the FST to minimize the subjectivity in the attribution of indicator scores and in their assessment and to calculate the consistency between experts’ opinions to produce an ideal safety culture pattern.

The first step is the construction of an ideal safety culture pattern for the process we want to evaluate based on experts` opinions. In this step, the leading indicators are linguistic variables indicated by a set of linguistic terms represented by triangular fuzzy numbers. These triangular fuzzy numbers denote the degree of importance of each leading indicator. The process of aggregating experts’ opinions for construction of the ideal safety culture pattern uses the similarity method developed by Hsu and Chen [3], and it is used as a benchmark for safety culture evaluation. The final step is the evaluation of leading indicators performed by the workers. The assessment results are compared with the ideal safety culture pattern and then defuzzified by the center of area method. The result indicates the level of safety culture compared with the ideal pattern.

The leading indicators are developed in such a way as to identify potential concerns in the organization’s performance according to the 6 safety culture attributes. The operationalization of an indicator is called a “metric.” A metric denotes how the indicator is measured, whereas an indicator denotes something that one wishes to measure with the use of one or more metrics.  The indicators and the corresponding leading indicator metrics are described in Table 1.    

Table 2 – Metrics of the leading indicators assessed in the case study.

Results

The method was tested in a radiopharmaceuticals process facility. The safety culture evaluation of the radiopharmaceuticals dispatch package sector was performed by seven workers. The average evaluation of the safety culture based on each indicator was computed. The result of the average evaluation showed that the process evaluated has a satisfactory learning culture, flexibility awareness, just culture and preparedness (all of them above 60% when compared to the safety culture pattern). However, it presented problems related to the top-level commitment.

Conclusions

We present a new model for safety culture evaluation. The model uses leading indicators and the concepts and properties of Fuzzy Sets Theory. We developed an ideal safety culture pattern using the similarity aggregation method to aggregate fuzzy individual expert opinions, in which we considered the difference in the importance of each expert. The results pointed to the strengths and weaknesses of safety culture attributes. They should be used by the overall management to monitor the safety culture level in organization processes, using the indications provided by comparison with the ideal safety culture pattern.

References

[1] International Nuclear Safety Advisory Group (INSAG), Safety culture. Safety Series No. 75, INSAG-4, IAEA, Vienna, 1991.

[2] T. Reiman, P. Oedewald, Evaluating safety critical organizations: Focus on the nuclear industry, Swedish Radiation Safety Authority, Research Report (12), 2009. 

[3] H. M. Hsu, C. T. Chen, Aggregation of fuzzy opinions under group decision making, Fuzzy Sets and Systems 79 (1996) 279 –285.