Statistical evaluation and analysis of safety intervention in the determination of an effective resource allocation strategy

Samuel A. Oyewole, Joel M. Haight, Andris Freivalds, David J. Cannon, Ling Rothrock

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This paper provides an analytical background for the development of an effective safety intervention program with the aim of minimizing incident rates. Safety intervention data were collected from the environmental health and safety department of an American-owned oil company in the Niger-Delta region of Nigeria. A safety model was developed to determine the safety intervention factors and interactions which minimize incident rates, with the aim of predicting a better resource allocation strategy. Five main safety intervention factors (factor A: leadership and accountability; factor B: qualification selection and pre-job; factor C: employee engagement and planning; factor D: work in progress; factor E: evaluation, measurement and verification) were highlighted and investigated to show their effects on incident rate performance. Analysis of variance test showed that four safety factors (A, C, D, and E) were significant. Statistical techniques such as response surface design plots were used to determine the resource allocation method. The developed safety model recommended the allocation of 16.66% of the available resources to the significant safety intervention activities in order to achieve the desirable incident rate. In order to reap the benefits of this research, it will be important to concentrate more efforts and resources on significant factors which have positive impacts in minimizing incident rates.

Original languageEnglish (US)
Pages (from-to)585-593
Number of pages9
JournalJournal of Loss Prevention in the Process Industries
Issue number5
StatePublished - Sep 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Food Science
  • General Chemical Engineering
  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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