Evidence map›Paper›PMID 41963584›Full record

ArticleScientific reports2026

A department level performance metric architecture for operationalizing behavior-based safety in high-risk manufacturing.

Dhamotharan Valaguru, Arumugaprabu Veerasimman, Giuseppe Melis, Nataliia Kochkina

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Dhamotharan ValaguruDepartment of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India.
Arumugaprabu VeerasimmanDepartment of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India. v.arumugaprabu@klu.ac.in.
Giuseppe MelisDepartment of Economics and Business Sciences, University of Cagliari, 09123, Cagliari, Italy.
Nataliia KochkinaDepartment of Economics and Business Sciences, University of Cagliari, 09123, Cagliari, Italy. nataliia.kochkina@unica.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present research aims at department-level Behaviour-Based Safety (BBS) framework operationalized through Key Performance Indicators (KPIs) in a high-risk electrolyser manufacturing setting. By giving departments the authority to monitor, report, and be assessed on both leading and lagging safety measures, this approach aims to decentralize safety ownership. Hazard self-identification, toolbox discussions, safety kaizens, training, and permit adherence are the important elements that are tracked on a weekly basis and are rewarded and recognized according to departmental standards. Over the course of the whole implementation cycle, data was gathered from 18 departments spread across three locations. By means of SPSS statistical tool able to examine post-intervention performance and replicate pre-intervention performance using historical audit data. The efficiency of the framework was validated using Wilcoxon signed-rank tests and paired t-tests, which showed a substantial 47% increase in average KPI values (p < 0.001).Additionally, the study showed behavioural changes such as dangerous acts decreased by 32%, kaizen participation increased by 36%, and hazard reporting improved by 27%. The scoring approach shifted cultural ownership toward proactive safety involvement by rewarding departmental vigilance and penalizing the safety team for discovering external hazards. For industrial businesses looking to transition from compliance-based safety systems to behaviourally driven safety cultures, this model provides a scalable, affordable, and replicable approach. The results have applications in policy creation, workforce engagement, and safety leadership.

Indexed as

Manufacturing IndustrySafety ManagementHumansBehaviour-based safetyHazard reportingKey performance indicators (KPI)Manufacturing safetySafety cultureSafety performance metrics

Identifiers

PMID41963584
PMCPMC13230972

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.