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 journalArticle

13 Citations (Scopus)

Abstract

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
Volume23
Issue number5
DOIs
StatePublished - Sep 1 2010

Fingerprint

safety assessment
Resource Allocation
resource allocation
Resource allocation
Safety
safety factor
Niger
leadership
Safety factor
human resources
Nigeria
concentrates
planning
analysis of variance
oils
methodology
testing
Analysis of variance (ANOVA)
Evaluation
Factors

All Science Journal Classification (ASJC) codes

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

Cite this

@article{3ed9bae5bcbf43b482c1dda37d4e19e2,
title = "Statistical evaluation and analysis of safety intervention in the determination of an effective resource allocation strategy",
abstract = "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.",
author = "Oyewole, {Samuel A.} and Haight, {Joel M.} and Andris Freivalds and Cannon, {David J.} and Ling Rothrock",
year = "2010",
month = "9",
day = "1",
doi = "10.1016/j.jlp.2010.05.014",
language = "English (US)",
volume = "23",
pages = "585--593",
journal = "Journal of Loss Prevention in the Process Industries",
issn = "0950-4230",
publisher = "Elsevier BV",
number = "5",

}

Statistical evaluation and analysis of safety intervention in the determination of an effective resource allocation strategy. / Oyewole, Samuel A.; Haight, Joel M.; Freivalds, Andris; Cannon, David J.; Rothrock, Ling.

In: Journal of Loss Prevention in the Process Industries, Vol. 23, No. 5, 01.09.2010, p. 585-593.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Statistical evaluation and analysis of safety intervention in the determination of an effective resource allocation strategy

AU - Oyewole, Samuel A.

AU - Haight, Joel M.

AU - Freivalds, Andris

AU - Cannon, David J.

AU - Rothrock, Ling

PY - 2010/9/1

Y1 - 2010/9/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77956613689&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956613689&partnerID=8YFLogxK

U2 - 10.1016/j.jlp.2010.05.014

DO - 10.1016/j.jlp.2010.05.014

M3 - Article

AN - SCOPUS:77956613689

VL - 23

SP - 585

EP - 593

JO - Journal of Loss Prevention in the Process Industries

JF - Journal of Loss Prevention in the Process Industries

SN - 0950-4230

IS - 5

ER -