Business Case Risk: Will We Really See The Predicted Results?

Deliver Case Results and Business Case Risk Comes With the Turf.

Business case risk means that results come with uncertainty.

The business case predicts financial results, but these predictions come with business case risk.

Those proposing business investments and actions rely on robust business case analysis (BCA) to predict the likely outcomes. However, business case risk is always present.

Everyone knows, however, that not even the most rigorous BCA cannot eliminate business case risk—uncertainty about the results of business decisions. Through BCA can, however, cut uncertainty, measure what remains, and deliver tools for managing risk as the action goes forward.

Senior business managers say, increasingly, that the margin of tolerance for management error is shrinking, visibly and tangibly. In the current business climate, therefore, there is urgency to questions like these:

  • How do we know that we’re going to see the results your case projects?
  • Are we sure this is the best business decision?
  • How can I prove, later, that I’m acting responsibly now?

Most people know that business case analysis   attempts to answer questions like these, firmly and finally. That is what decision support is all about, after all. Nevertheless, decision makers with business case results in front of them, still ask the fundamental question:

Is there any certainty possible in the business world?

Business Case Risk Analysis: The Underlying Principles Are Simple

There is no way to avoid the reality that credible, practical answers to questions like those above call for rigorous quantitative risk analysis. The subjective “heat maps” that many proposal authors give as “risk analysis” just will not do. In fact, the kind of certainty described here comes from the world of elementary statistics.

Unfortunately, merely mentioning “statistical thinking” raises a roadblock for many.  As a result, they make proposals hoping a subjective risk assessment is an adequate substitute for credible quantitative analysis. It is no surprise when these investments and actions fail.

Fortunately, applying what follows below calls only for the analyst to understand a few fundamental probability concepts from a basic introductory statistics course—the one that schools offer for “non-statistical people”. No one needs to go beyond that.

Moreover, most of the leading risk analysis software comes with excellent user guides that do not even assume that much background.

Case Study: Can You Trust Business Case Results?

Management in an internet services company is deciding whether to launch a new customer service offering. They will go to market with the new service only if they are confident it will turn a profit. This calls for a decision support business case, with a few estimates for cost and benefit items.

Here is their approach for estimating just one cost item, “Annual labor expenses” for customer support with delivering the new service.  The estimated derives from just three assumptions.

Least likely value Average
Highest Likely
of service calls per month
70 100 135
The onsite time required per call (hrs)
1.00 4.00 7.00
  Labor rate plus overhead ($ / hr)
60.00 80.00 100.00

The average figures are the “most likely” values in the eyes of the analyst and the company’s technical experts. Using the expected figures:

Annual labor cost estimate
= onsite time * Labor rate
= 12 * 100 * 4.00 * $80
= $384,000

The road from assumption to cost estimate is transparent, for everyone to understand and check. If the assumption values are correct, no doubt about it, the annual cost of labor will be $384,000.

Should management trust that estimate? Remember, these decision makers are highly risk-averse. They will go forward only if they have confidence in the projections. They have a keen interest in the critical issue: If the cost of labor really exceeds $500,000, the service becomes unprofitable. How likely is that?

Run and Replay the Future 10,000 Times

Risk analysis addresses questions above by applying Monte Carlo simulation1 to a financial model. In this case for the single cost item, the “financial model” is just the formula above.

Notice first the ranges of possible values for each assumption: the case builder expects an average 100 calls for each month, but in fact, the number could be different. However, the case builder is very certain that it will fall between 70 and 135. The other assumptions also can—and probably will—differ from the expected value, but the case builder is very sure that reality for all falls within the given ranges.

Treating  the assumptions  as ranges of possibilities, and using just a few more assumptions about the likelihood of different assumption values2 the analyst uses Monte Carlo simulation to “replay the future” 10,000 times, capturing the labor cost estimate each time. The chart below summarizes these cost estimates.

Risk analysis cost results

Cost results risk probabilities from Monte Carlo simulation. The graph shows the probability of reaching or exceeding total cost figures.

The chart shows a 50% chance of seeing an actual cost of $384,000 or more. That’s no surprise: we already knew the expected average. But these risk-averse decision makers are more than a little uncomfortable with a 20% chance of having real costs hit $500,000 or more. Moreover, the 90% confidence interval for the cost estimate is very wide: $240,000 through $530,000.

Reducing Business Case Risk

Can the analyst do anything to build confidence in seeing that labor costs stay low enough to make the service profitable? Some very clear guidance comes from another result of the simulation exercise: sensitivity analysis:

Risk analysis cost results

Cost results risk probabilities from Monte Carlo simulation. The graph shows the probability of reaching or exceeding total cost figures.

While performing the risk analysis, the simulation program was also keeping track of the correlation between each assumption and the forecast result (here, the cost figure). This chart says that the dominant assumption, by far, in controlling different cost results, is the assumption about “time spent per service call.”

If case builders can cut the uncertainty in the “time per call” assumption, they can lower the uncertainty in the projected cost.

Re-visting the Risky Assumption

Here, the analyst had more discussions with the service product manager, and with the services personnel training manager. As a result of these discussions, the analyst could confidently narrow the range of “near certain” average on-site time requirements from 1-7 hours to 3-5 hours. Very occasionally, a call may occur that is much longer or  shorter, but they are very sure now that the average on-site time will be in the narrower range, 3-5 hours.

Case builders also reduced the range of estimates for “average labor rate” from $60-$100/Hr to a much narrower range of $75-$85 per hour. Very sure now that the assumptions will fall in the narrower ranges, the case builders re-ran the simulation program and produced this result:

The most likely result is still $384,000, but now the chance of a $500,000 cost or more is almost 0—if you can believe the new assumptions about the assumptions!

Furthermore, management is 90% confident now that the real cost figure will fall between $344,000 and $422,000. Trusting these new results, they are ready to launch the service.

Should You Trust These Results?

The trust question comes down to what you believe about your assumptions. If the (now narrower) ranges of possibilities for the assumptions stand, then everyone can trust the probabilities in the final summary chart.

Your Next Steps: Take Action!

For in-depth coverage of risk and sensitivity analysis, please see the full article on Business Case Risk online.

For a practical introduction to applied risk and sensitivity analysis, as shown on this page, see the best-selling eBook Business Case Essentials. For guidance in building a dynamic financial model for your case for simulation-based risk and sensitivity analysis, see Financial Modeling Pro.

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By Marty Schmidt. Copyright © 2004-2018.
Solution Matrix Limited, Publisher.
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