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Estimating the effectiveness of ergonomics interventions through case
studies: Implications for predictive cost-benefit analysis
☆
Richard W. Goggins
a,
⁎, Peregrin Spielholz
b
, Greg L. Nothstein
c
a
Consultation Services, Washington State Department of Labor and Industries, Olympia, WA 98504-4640, USA
b
SHARP Program, Washington State Department of Labor and Industries, Olympia, WA 98504-4330, USA
c
Energy Policy Division, Washington State Department of Community, Trade and Economic Development, 906 Columbia St. SW, Olympia, WA 98504, USA
Received 13 December 2006; received in revised form 3 October 2007; accepted 12 December 2007
Available online 28 April 2008
Abstract
Problem: Cost-benefit analysis (CBA) can help to justify an investment in ergonomics interventions. A predictive CBA model would allow
practitioners to present a cost justification to management during the planning stages, but such a model requires reliable estimates of the benefits of
ergonomics interventions. Method: Through literature reviews and Internet searches, 250 case studies that reported the benefits of ergonomics
programs and control measures were collected and summarized. Results: Commonly reported benefits included reductions in the number of work-
related musculoskeletal disorders (WMSDs) or their incidence rate, as well as related lost workdays, restricted workdays, and workers'
compensation costs. Additional benefits reported were related to productivity, quality, turnover and absenteeism. Discussion: Benefits reported
were largely positive, and payback periods for ergonomics interventions were typically less than one year. Summary: The results of this review
could be used to develop predictive CBA models for ergonomics programs and individual control measures. Impact on Industry: Cost-justifying
ergonomics interventions prior to implementation may help to secure management support for proposed changes. Numbers used for the benefits
side of a cost-benefit analysis (CBA) need to be based on “real world”data in order to be credible. The data presented in this paper may help in the
development of simple cost-benefit models for ergonomics programs and control measures.
Published by Elsevier Ltd.
Keywords: Ergonomics; Musculoskeletal disorders; Cost benefit analysis; Interventions; Effectiveness
Contents
1. Introduction .............................................................. 339
2. Methods ............................................................... 340
3. Results ................................................................ 341
4. Discussion .............................................................. 343
5. Conclusions.............................................................. 343
References ................................................................. 344
1. Introduction
Proponents of ergonomics often speak of the benefits that
organizations reap when implementing both comprehensive
ergonomics programs and individual control measures to reduce
work-related musculoskeletal disorders (WMSDs). These
benefits include not only reduced number of injuries and injury
costs, but also reduced turnover and absenteeism, improved
Journal of Safety Research 39 (2008) 339 –344
www.nsc.org
☆
Note: More details on the case studies discussed in this paper, along with a
more complete reference list, can be found at the Puget Sound Human Factors
and Ergonomics Society web site: www.pshfes.org.
⁎Corresponding author. Tel.: +1 360 902 5450.
E-mail address: gogr235@LNI.wa.gov (R.W. Goggins).
www.elsevier.com/locate/jsr
0022-4375/$ - see front matter. Published by Elsevier Ltd.
doi:10.1016/j.jsr.2007.12.006
Author's personal copy
product quality, and increased productivity. However, reporting
of these benefits in peer-reviewed journals remains limited, and
there may be a bias toward reporting only positive outcomes
(Silverstein & Clark, 2004; Volinn, 1999). Fortunately, there is
an increasing trend toward performing cost benefit analyses
(CBA) related to safety and health interventions, and many
CBA models have been developed that can be used for
ergonomics interventions (Oxenburgh, Marlow, & Oxenburgh,
2004; International Labour Organization [ILO], 2002; Mossink,
2002). A significant downside to some of these models is their
relative complexity and the need to develop a set of inputs for
each individual organization. While cost data are readily
available in most organizations, the potential benefits are
often not known. These models may be better suited for post-
intervention analysis, when these data are more readily
available. However, there is a need for a model that predicts
the benefits of an intervention, or the comparative benefits of a
range of intervention options. Such a model would assist
practitioners in cost justifying an investment in ergonomics
interventions to management during the planning stage. In order
to create such a model, one would first need to determine the
likely effectiveness of different types of ergonomics interven-
tions in reducing injuries and generating other benefits.
2. Methods
Existing CBA models were evaluated for common elements
and to see if any contained predictive elements. Oxenburgh's
Productivity Model (1991) does offer a “rough guide”for the
effectiveness of controls, shown graphically in Fig. 1. These
estimates are for safety measures in general, and controls such
as barriers and light curtains are not truly relevant to
ergonomics. However, Oxenburgh's model provides a good
starting point for developing ergonomics-specific estimates.
Several reviews of the effectiveness of ergonomics inter-
ventions have provided useful estimates (Department of Labor
and Industries [DLI], 2000; Grant & Habes, 1995; Guastello,
1993). Guastello (1993), as part of a review of the effectiveness
of various accident prevention programs, evaluated two
comprehensive ergonomics programs and found an average
49.5% reduction in accidents. As part of a regulatory Cost-
Benefit Analysis a team of economists and ergonomists from
the Washington State Department of Labor and Industries (DLI,
2000) estimated the benefits of ergonomic interventions by
evaluating the literature on actual ergonomic programs in the
workplace.
The DLI team carried out a literature search focusing on
reports and publications evaluating the effectiveness of
ergonomic interventions at the workplace. A total of 63 reports
and publications on the success of ergonomic programs were
evaluated and determined to be of sufficient quality for
determining rule effectiveness. These case studies, many of
which were from peer-reviewed sources, covered a wide range
of work environments. Several of the published sources were,
themselves, reviews of a number of case studies. The DLI
review focused mainly on the reduction of WMSDs and related
costs, although some information on productivity and other
benefits were also recorded. Publications and reports that were
anecdotal in nature, or lacked detailed information, were not
included in this evaluation (DLI, 2000). Table 1 summarizes the
average and median effectiveness of the workplace ergonomic
interventions, as well as the confidence intervals around the
averages.
The observed average reduction in number of WMSD
injuries was 50%, while the average reduction in WMSD costs
was 64%. The literature search also revealed a decrease in the
severity of WMSD injuries that were reported after implemen-
tation of ergonomic programs, as seen in the reduction in days
per injury and cost per claim. Confidence intervals for mean
effectiveness rate (central estimate) of 50% were established
based on the variance of the effectiveness parameter reported in
the intervention studies evaluated.
Along with other evidence, the DLI economists used the
results of this review to develop conservative estimates of a
40% reduction of WMSDs and a 50% reduction of WMSD
costs for ergonomics interventions in compliance with the
proposed rule. While the review provided adequate evidence for
this conclusion, insufficient information was available to make
assumptions regarding other benefits, or to estimate the
effectiveness of different types of individual control measures.
This raised the question: What results would one find if one
‘cast a wider net,’searching not just textbooks and peer
reviewed journals, but also the rapidly growing set of case
studies, many of them anecdotal, that are published on the
Internet?
In order to answer this question, a search of the World Wide
Web was performed on popular search engines using several
Table 1
Washington State ergonomics rule CBA effectiveness measures
Effectiveness
Measure
Number of
Studies
Average
Reduction
Median Confidence
Interval
a
Number of
WMSDs
37 49.5% 50% 42.2%–56.8%
Lost work days 24 65.0% 65% 54.6%–75.4%
Days per injury 3 56.6% 65% 36.6%–76.6%
WMSD costs 22 64.8% 64% 55%–74.6%
Cost per claim 5 43.6% 56% 8.4%–78.6%
a
Computed 95% confidence interval.
Fig. 1. Oxenburgh's (1991) estimates of the effectiveness of safety interventions.
340 R.W. Goggins et al. / Journal of Safety Research 39 (2008) 339–344
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different combinations of the terms “ergonomics,”“solutions,”
“interventions,”“cost,”“benefit,”“analysis,”and “effective-
ness.”The search revealed numerous case studies on the
benefits of both ergonomics programs and individual control
measures, including several collections of “success stories”that
were judged to be from reliable sources (Hendrick, 1996;
OSHA, 1999; U.S. Department of Defense, 2004; Ohio Bureau
of Workers' Compensation [BWC], 2002). Case studies were
included only if there was enough detail on the intervention to
determine that it was likely to have resulted in the benefits that
were attributed to it. Case studies were excluded from the
analysis if they were related more to human factors design than
ergonomics. Also, case studies that reported other safety and
health interventions in addition to ergonomics were excluded if
they did not estimate the effect of the ergonomics interventions
alone. Effectiveness measures were included in the analysis
only if they were reported either as a percentage change, as pre/
post data from which a percentage change could be calculated,
or as a standard financial outcome (e.g., benefit:cost ratio or
return on investment). Finally, effectiveness measures that were
obvious outliers (e.g., a reported productivity increase of 400%)
were excluded.
The remaining case studies were combined with the 63
published studies from the DLI regulatory CBA, along with
additional, more recently published articles. The industry, type of
intervention, analysis period, costs, benefits, and resulting
savings for each of the case studies were entered into a database.
Duplicates were screened out by searching the database by
industry type for identical numerical values among the
effectiveness measures. Descriptive statistics and confidence
intervals were calculated using Microsoft Excel 2003. The 95%
CI was simply computed as +/−1.96 standard deviations from
the mean of the observed values, even when only two values
were observed. In order to assess for publication bias, a funnel
plot of sample size versus percentage injury reduction was
created using a subset of 30 case studies that reported these data.
3. Results
The search resulted in a collection of 250 case studies,
including the 63 studies from the DLI review, representing a
variety of industries and types of intervention. Eighty-seven of the
case studies described interventions in manufacturing industries,
40 were in an office environment, and 36 were in a healthcare
Table 3
Effectiveness measures from 114 ergonomics program case studies (excluding office)
Effectiveness Measure Number of Studies Average Median 95% CI Range
Number of WMSDs 66 57% ↓55% ↓51%–63% 8%–100% ↓
Incidence rate⁎24 57% ↓50% ↓45%–69% 9%–100% ↓
Lost workdays⁎44 72% ↓79% ↓65%–79% 15%–100% ↓
Restricted days⁎9 46% ↓37% ↓31%–61% 16%–77% ↓
Workers' comp costs⁎42 67% ↓68% ↓60%–74% 15%–100% ↓
Cost per claim⁎6 32% ↓32% ↓3%–61% −20%–76% ↓
Productivity 6 46% ↑40% ↑22%–70% 10%–80% ↑
Labor costs 2 28% ↓28% ↓12%–44% 20%–36% ↓
Turnover 9 36% ↓40% ↓23%–49% 3%–68% ↓
Absenteeism 2 79% ↓79% ↓42%–116% 60%–98% ↓
Payback period 1 0.19 years 0.19 years - -
Cost:Benefit ratio 2 1:2.8 1:2.8 1:2.7–1:3.2 1:2.5–1:3
⁎Due to WMSDs.
Table 2
Effectiveness measures from all 250 case studies
Effectiveness Measure Number of Studies Average Median 95% CI Range
Number of WMSDs 90 59% ↓56% ↓54%–64% 8%–100% ↓
Incidence rate⁎53 65% ↓67% ↓57%–73% 9%–100% ↓
Lost workdays⁎78 75% ↓80% ↓70%–80% 3%–100% ↓
Restricted days⁎30 53% ↓58% ↓42%–64% 5%–100% ↓
Workers' comp costs⁎52 68% ↓70% ↓62%–74% 15%–100% ↓
Cost per claim⁎7 39% ↓50% ↓11%–67% −20%–81% ↓
Productivity 61 25% ↑20% ↑20%–30% −0.2%–80% ↑
Labor costs 6 43% ↓32% ↓17%–69% 10%–85% ↓
Scrap/errors 8 67% ↓75% ↓59%–85% 8%–100% ↓
Turnover 34 48% ↓48% ↓40%–56% 3%–100% ↓
Absenteeism 11 58% ↓60% ↓43%–63% 14%–98% ↓
Payback period 36 0.7 years 0.4 years 0.4–1 year 0.03–4.4 years
Cost:Benefit ratio 5 1:18.7 1:6 1:−7.6–1:45 1:2.5–1:72
⁎Due to WMSDs.
↓Down arrows represent a reduction in the effectiveness measure.
↑Up arrows represent an increase in the effectiveness measure.
341R.W. Goggins et al. / Journal of Safety Research 39 (2008) 339–344
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setting, with the remainder in a variety of other industries. Just
over 150 of the case studies reported the results of ergonomics
programs, with the remainder being cost-benefit analyses of
individual control measures. Individual control measures were
further broken out by the type of risk factor they addressed (e.g.,
lifting, awkward postures) and the way in which they eliminated
or reduced exposure to risk factors (e.g., substituted mechanical
equipment for manual lifting, reduced level of exposure by
improving location of the lift or reducing weight of the object).
Table 2 shows the benefits reported for all 250 case studies.
Tables 3–5show reported benefits from case studies in non-
office environments, office environments, and healthcare,
respectively. Table 6 shows the reported effectiveness measures
for case studies on individual control measures such as lift assist
devices, workstation redesign, and tool redesign.
The individual control measure case studies were further
reviewed in order to break them out into four categories of
effectiveness:
1. Controls that, as described, were likely to eliminate the
hazardous exposure (e.g., substituting a mechanical lift
device for a manual lift; semi-automation of a process);
Table 4
Effectiveness measures from 40 office ergonomics intervention case studies
Effectiveness Measure Number of Studies Average Median 95% CI Range
Number of WMSDs 5 61% ↓50% ↓41%–81% 43%–100% ↓
Incidence rate⁎1 64% ↓64% ↓--
Lost workdays⁎4 88% ↓91% ↓74%–102% 70%–100% ↓
Restricted days⁎1 100% ↓100% ↓--
Workers' comp costs⁎3 81% ↓80% ↓72%–90% 74%–89% ↓
Cost per claim⁎1 81% ↓81% ↓--
Productivity 25 17% ↑12% ↑11%–23% −0.2%–64% ↑
Errors 2 32% ↓32% ↓−15%–79% 8%–56% ↓
Turnover 2 87% ↓87% ↓85%–89% 86%–88% ↓
Absenteeism 3 46% ↓50% ↓11%–81% 14%–75% ↓
Cost:Benefit Ratio 3 1:1.78 1:1.5 1:1–1:2.6 1:1.3–1:2.6
Payback period 9 0.4 years 0.4 years 0.18–0.62 yrs. 0.06–1 year
⁎Due to WMSDs.
Table 5
Effectiveness measures from 36 healthcare program case studies
Effectiveness Measure Number of Studies Average Median 95% CI Range
Number of WMSDs 21 61% ↓60% ↓51%–71% 18%–100% ↓
Incidence rate⁎10 56% ↓46% ↓37%–75% 16%–100% ↓
Lost workdays⁎15 74% ↓80% ↓64%–84% 38%–100% ↓
Restricted days⁎8 49% ↓43% ↓23%–75% 5%–100% ↓
Workers' comp costs⁎13 70% ↓73% ↓58%–82% 35%–99% ↓
Cost per claim⁎1 20% ↑20% ↑--
Turnover 8 37% ↓33% ↓15%–59% 3%–100% ↓
Absenteeism 1 98% ↓98% ↓--
Payback period 4 0.28 years 0.17 years −0.18–0.74 yrs. 0.06–0.71 years
⁎Due to WMSDs.
Table 6
Effectiveness measures from 96 individual control measure case studies
Effectiveness Measure Number of Studies Average Median 95% CI Range
Number of WMSDs 18 64% ↓62% ↓51%–77% 25%–100% ↓
Incidence rate⁎28 71% ↓73% ↓61%–81% 14%–100% ↓
Lost workdays⁎30 77% ↓84% ↓67%–87% 3%–100% ↓
Restricted days⁎20 54% ↓61% ↓39%–69% 5%–100% ↓
Workers' comp costs⁎6 69% ↓79% ↓49%–89% 33%–91% ↓
Productivity 30 28% ↑25% ↑21%–35% 7%–67% ↑
Labor costs 4 51% ↓54% ↓14%–88% 10%–85% ↓
Scrap/errors 6 79% ↓90% ↓58%–100% 35%–100% ↓
Turnover 23 50% ↓58% ↓40%–60% 3%–100% ↓
Absenteeism 6 57% ↓64% ↓40%–74% 23%–75% ↓
Payback period 25 0.82 years 0.40 years 0.41–1.23 yrs. 0.03–4.40 years
Cost:Benefit ratio 3 1:29.3 1:10 1:−12.6–1:71.2 1:6–1:72
⁎Due to WMSDs.
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2. Controls that would reduce the level of exposure (e.g.,
improving the location or reducing the weight of the lift;
modifications to workstations or tools)
3. Controls that would reduce the time of exposure (e.g.,
rotating to tasks with different or no risk factor exposure); and
4. Controls that primarily relied on employee behavior (e.g.,
training only or team lifting).
Fig. 2 shows the results of this effectiveness categorization.
The effectiveness estimates are based on the range of reductions
in WMSD numbers and incidence rates, as well as lost
workdays and workers' compensation costs, both of which
are indicators of injury severity. Effectiveness estimates are
presented as a range rather than as an average due to the
variability of the measures and the relatively small number of
studies in each category. While the estimates of the effective-
ness of controls that eliminate or reduce the level of exposure
are based on a reasonable number of case studies (37 and 57
studies, respectively), the estimates for reducing time of
exposure and controls that rely on behavior are based on only
one case study each, and therefore should be considered more
professional opinion than evidence-based estimates.
4. Discussion
The theory that reporting of results is biased toward positive
outcomes would appear to be borne out by the fact that, out of
525 individual effectiveness measures reported, only two were
negative, a 0.2% reduction in productivity in an office setting
(offset by a 7.75% reduction in errors) and a 20% increase in
costs per claim for a healthcare employer (although overall
workers' compensation costs for this employer were reduced by
35% for the same period). The scatter plot of sample size versus
injury reduction did show a funnel shape, with more variability
in effect size among the studies with smaller sample sizes. There
did not appear to be any asymmetry in the shape of the plot,
such as would be expected if there were a lack of reporting of
studies with no or negative effects (Peters, Sutton, Jones,
Abrams, & Rushton, 2006). Comparing Tables 1 and 2, the
results with the Internet case studies added in were, in general,
more positive than those from the DLI review alone, although
the case study numbers are mostly within the 95% confidence
interval used by DLI. Comparing Figs. 1 and 2, it appears that
the effectiveness estimates from the case studies compare fairly
well with those proposed by Oxenburgh (1991).
In addition to the injury-related effectiveness measures,
productivity, turnover, absenteeism, and payback period were
some of the more commonly reported metrics. The types of
effectiveness measures reported seem somewhat dependent on
their importance to the industry in which the intervention took
place. For example, productivity was reported in most of the
office ergonomics case studies; however, it was not reported at
all in the healthcare sector, which instead chose lost workdays,
restricted days, and turnover as the next most important metrics
to consider after injuries and costs. This seems logical, since
productivity is less important in patient care than is quality,
which can be negatively affected by staffing shortages. The
non-injury-related effectiveness measures also proved quite
positive; for example, 28 out of the 36 reported payback periods
were less than one year.
5. Conclusions
These results can have implications for the design of CBA
models. For example, the fact that most interventions have
payback periods of less than one year would allow for simpler
models that do not have to account for depreciation or
discounted cash flow. Since productivity was a commonly
reported benefit, it can be argued that it should be included in
even a basic model, while the effects on absenteeism, turnover,
and error rates might be reserved for more complex models.
Finally, these results provide an opportunity to develop CBA
models for different work settings, such as healthcare, office, or
industrial, as well as different situations, such as implementing a
comprehensive program versus an individual control measure.
There are numerous limitations to the use of these data,
however. The aforementioned bias toward reporting successful
interventions would suggest that more conservative numbers
might be appropriate in a predictive model, although one could
argue that the goal of implementing an ergonomics intervention
is to be successful, and therefore the more optimistic numbers
are appropriate. This large a number of positive case studies
would tend to reinforce the assertion that, when implemented
correctly, ergonomics interventions can provide considerable
benefits to organizations. Despite the large overall number of
examples, some of the categorized results such as the benefits
from individual control measures have relatively few examples
and quite a bit of variability, and so these numbers should be
used cautiously. Additionally, a relatively small proportion of
the case studies came from peer reviewed sources, and even
fewer are from randomized and controlled studies. Therefore, it
is possible that some of the benefits seen were not solely due to
the ergonomics interventions. In fact, the wide range of reported
effectiveness for essentially similar control measures may be
due to factors such as variations in the quality of each
organization's overall safety and health program, and the
level to which they have addressed other potential contributors
to injury rates, such as psychosocial risk factors.
Some questions also remain about the way in which the case
studies report productivity increases, and how this would
Fig. 2. A proposed relationship between level of control and estimates of
effectiveness based on results from case studies.
343R.W. Goggins et al. / Journal of Safety Research 39 (2008) 339–344
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translate into a benefit to the organization's bottom line. For
example, an intervention may increase productivity for a single
process by 25%, but does this translate to measurable savings in
reduced overtime costs or increased sales for the organization?
The wide range of productivity numbers that were reported does
seem to indicate that organizations use different definitions
when reporting productivity. There were also considerable
differences in time periods over which the benefits were
reported. While some organizations may have reduced WMSD
costs by 80% over a one-year period, others may have reported
the same benefit but over a three- or five-year period, or not
reported a time period at all. This makes estimating a typical
annual benefit for an intervention difficult. Issues such as these
must be resolved before these results can be put to practical use
in a predictive model. Certainly once a predictive model is
developed, it will need to be validated through more
intervention case studies similar to those upon which it is based.
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Rick Goggins, M.S., CPE is a senior-level ergonomist with the Washington
State Department of Labor and Industries' Consultation Services group. He has
a B.A. in Biology from Columbia University and an M.S. in Human Factors/
Ergonomics from the University of Southern California. The primary focus of
his work is helping employers in all industry sectors to identify risk factors for
injury, and implement and evaluate solutions. He is a frequent presenter at local
and national conferences. He is also a Past President of the Puget Sound Human
Factors and Ergonomics Society.
Peregrin Spielholz, Ph.D., CPE, CSP, is a principal investigator with the
Washington State Department of Labor and Industries' SHARP safety and
health research program. He completed his BSE and MSE degrees at the
University of Michigan and his PhD at the University of Washington. His areas
of focus are accident investigation, safety engineering, human-centered design,
and the assessment of workplace risk factors and interventions. Spielholz has
worked with companies and organizations across the country in most industry
sectors. He has published extensively in ergonomics and occupational safety
and health. Spielholz is the Principal Investigator for the NIOSH-funded
Fatality Assessment and Control Evaluation (FACE) and Trucking Injury
Reduction (TIRES) Programs in Washington State and is an Affiliate Assistant
Professor in the University of Washington Department of Environmental and
Occupational Health Sciences.
Greg Nothstein, MS, MA, is now an energy policy analyst for the State of
Washington Energy Policy Office. Prior to this he was the Legislative
Economist at Washington State Department of Labor and Industries. He has a
BS in Chemistry from Pacific Lutheran University, a MS in Environmental
Engineer and a MA in Economics from the University of Washington. He was
part of the team that developed the Washington State Ergonomics rule in 2000.
344 R.W. Goggins et al. / Journal of Safety Research 39 (2008) 339–344
