ORGANIZATIONAL CLIMATE METRICS AS A LEADING SHE PERFORMANCE INDICATOR AND AN AID TO RELATIVE RISK RANKING WITHIN INDUSTRY  

This website details a joint industrial research project carried out by AstraZeneca and the University of Manchester examining the relationship between organizational climate metrics and safety performance.  The work detailed in this website formed the basis of a thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences.   Elements of the research project have been published by M. Dodsworth, K. E. Connelly, C. J. Ellett and P. Sharratt.  Vol 85 (B1) 59-69.  Process Safety and Environmental Protection.  Trans ICHemE, Part B, January 2007.   

ABSTRACT

The chemical, pharmaceutical and other related process industries are characterised by inherently hazardous processes and activities.  To ensure that considered risk management decisions are made it is essential that organisations have the ability to rank the risk profiles of their assets and operations.  Current industry risk ranking techniques are biased toward the assessment of the risk potential of the asset or operation.  Methodologies used to assess these risks tend to be engineering based and include, for example, hazard identification and event rate estimation techniques.  Recent research has associated lagging safety performance indicators with metrics of organisational safety climate.  Despite the evidence suggesting their potential usefulness, organisational climate metrics have not yet been exploited as a leading safety, health and environmental performance indicator and as an aid to relative risk ranking.   The purpose of this research is to formulate a leading performance indicator to enable prediction of the safety, health and environmental performance of a site, and hence to allow relative risk ranking of sites based upon organisational climate metrics.   The responses to an industrial organisational survey are examined for a pharmaceutical company’s sites in the United Kingdom, Sweden and the United States.  Projection to Latent Structures Discriminatory Analysis, Soft Independent Modelling of Class Analogy and Projection to Latent Structure analysis are performed on the survey responses.  The resultant models are shown to be able to i) Discriminate between ‘good’, ‘average’ and ‘poor’ safety performing sites,  ii) Discriminate between United Kingdom, Swedish and United States sites, iii) Correlate organisational climate metrics with significant-injury frequency rates at United Kingdom, Swedish and United States sites. The organisational climate metrics that discriminate the safety performance of sites and sites of different nations are identified.

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