BY ORDER OF THE AIR FORCE PAMPHLET 91-215 SECRETARY ...

Figure A5.2. Example Maximizing Bang for the Buck.
Anyone can throw money at a problem. A real manager finds the optimum level of resources
producing an optimum level of effectiveness, i.e. maximum bang for the buck. Consider an
ergonomics training program involving training 400 supervisors from across the entire
organization in a 4 hour (3 hours training, 1 hour admin) ergonomics training course that will
cost $30,500 including student time. Ergonomics losses have been averaging $300,000 per year
and estimates are that the risk control will reduce this loss by 10% or $30,000. On the basis of a
cost benefit assessment over the next year (ignoring any outyear considerations), this risk
control appears to have a one year negative cost benefit ratio i.e. $30,000 in benefit, versus a
$30,500 investment, a $500 loss. Apparently it is not a sound investment on a one year basis.
This is particularly true when we consider that most decision-makers will want the comfort of a
2 or 3 to 1 cost benefit ratio to insure a positive outcome. Can this project be turned into a
winner?
We can make it a winner if able to access risk information concerning ergonomics
injuries/illnesses from loss control office data, risk management concepts, and a useful tool
called “Pareto’s Law”. Pareto’s Law, as previously mentioned, essentially states that 80% of
most problems can be found in 20% of the exposure. For example, 80% of all at fault traffic
mishaps might involve only 20% of the driver population. We can use this law, guided by our injury/illness
data, to turn the training program into a solid winner. Here is what we might do.
Step 1. Lets assume that Pareto’s Law applies to the distribution of ergonomics problems
within this organization. If so, then 80% of the ergonomics problem can be found in 20% of our exposures.
Our data can tell us which 20%. We can then target the 20% (80 students) of the
original 400 students that are accounting for 80% of our ergonomics costs ($240,000).
Step 2. Lets also assume that Pareto’s Law applies to the importance of tasks that we
intend to teach in the training course. If the three hours of training included 10 tasks, lets
assume that two of those tasks (20%) will in fact account for 80% of the benefit of the course.
Again our data should be able to indicate this. Lets also assume that by good luck, these two
tasks only take the same time to teach as the other eight. We might now decide to teach only
these two tasks which will require only 36 minutes (20% of 180 minutes). We will still retain
80% of the $240,000 target value or $192,000.
Step 3. Since the training now only requires 36 minutes, we will modify our training
procedure to conduct the training in the workshops rather than in a classroom. This reduces our
admin time from 1 hour (wash up, travel, get there well before it actually starts, and return to
work) to 4 minutes. Our total training time is now 40 minutes.
Summary. We are still targeting $192,000 of the original $300,000 annual loss but our cost
factor is now 80 employees for 40 minutes at $15/hour, with our teaching cost cut to 1/5th of the
$6000 (80 students instead of 400) which is $1200. We still have our staff cost so the total cost
of the project is now $2500. We will still get the 10% reduction in the remaining $192,000 that
we are still targeting, which totals $19,200. Our cost benefit ratio is now a robust 7.68 to 1. If
all goes well with the initial training and we actually demonstrate at 20% loss reduction, we may
choose to expand the training to the next riskiest 20% of our 400 personnel which should also
produce a very positive return.
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