Prioritization and assessment of safety key performance indicators in an automotive industry Prioritizacija i procjena ključa sigurnosti pokazatelja uspjeha u automobilskoj industriji
DOI: 10.31306/s.63.4.1
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of establishing an effective performance evaluation system for occupational health and safety management in the automotive industry, a sector characterized by high hazard levels and frequent accidents. The research aimed to identify, prioritize, and assess Key Performance Indicators (KPIs) to monitor safety performance, moving beyond traditional lagging indicators to include leading indicators that facilitate proactive risk management. The motivation stemmed from the need to reduce the complexity of decision-making caused by an overwhelming variety of potential indicators and to ensure that selected metrics are specific, measurable, achievable, relevant, and time-bound (SMART). The researchers employed a descriptive-analytical design conducted in four phases. First, they identified potential indicators through semi-structured interviews with 11 safety experts and a review of scientific literature, guidelines, and company documentation. Second, they assessed the content validity of the collected indicators using a panel of experts, calculating Content Validity Ratio (CVR) and Content Validity Index (CVI) scores, retaining only those with acceptable validity. Third, they prioritized the valid indicators using the Analytic Hierarchy Process (AHP), where experts performed pairwise comparisons based on SMART criteria. Finally, they evaluated the relationships between the prioritized indicators and actual safety outcomes using historical data from a major Iranian automotive company spanning 2009 to 2018, applying Pearson and Spearman correlation tests. The results indicated that from an initial set of 70 indicators, 23 demonstrated acceptable validity. The AHP prioritization identified the "number of risk assessments conducted" as the most critical safety indicator, followed by the "number of maneuvers carried out," the "percentage of budget allocated for risk management," and the "Frequency Severity Index (FSI)." Among educational indicators, the "percentage of safety educational programs implemented" ranked highest. Correlation analysis revealed significant relationships between leading and lagging indicators; specifically, the percentage of corrected non-compliance and the number of risk assessments showed significant negative correlations with the total number of work-related lost time injuries. Conversely, the FSI and total lost time injuries showed positive correlation, confirming that improvements in leading indicators corresponded with reductions in accident rates. The study concludes that a balanced approach incorporating both leading and lagging indicators is essential for effective safety management in the automotive industry. The findings provide a validated and prioritized set of KPIs that organizations can use to benchmark performance, communicate with stakeholders, and drive internal improvements. By highlighting the predictive value of leading indicators such as risk assessments and corrective actions, the research supports the shift from reactive accident reporting to proactive safety management, offering a structured framework for enhancing occupational health and safety systems.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: crash risk outcomes