Going the Extra Mile
Estimating the impact of a platform decision, especially regarding a platform switch, is a critically important exercise. CMOs will certainly need estimated impact in order to get approval.
However, CFOs may be a bit more demanding.
CFOs tend to be very analytical, especially when it comes to the expenditure of hard dollars. So, given that the estimated impact we generated using our impact model relies on a variety of input assumptions, the CFO is probably going to ask:
So for good measure, let’s go the extra mile and provide some additional analysis to go alongside the impact estimates we prepared using our impact model. That’s where sensitivity analysis comes in.
Sensitivity Analysis
What we do is start with our $9.1 million expected NPV (as shown in our earlier post about “estimating impact” — see links below). Then, we vary the productivity levers in 20% intervals, from 100% lower to 100% higher, and re-run the model for each set of input values.
First Conclusion: when we do that, what we find is that by far, the work effort/time assumption has the most effect on our results — which is what we’d expect, since it affects all aspects of job production.
But, as see above in the dark blue line, we still achieve a solid NPV of around $6 million, even with a 60% reduction in our work productivity assumption.
Second Conclusion: we also discover that our other assumptions regarding incidence and magnitude of cancellations, rework and re-compliance have little effect on our results.
Which again makes sense, since they are comparatively small portions of the overall job output.
Third Conclusion: when combined with the NPV and payback estimate from our impact model (see links below), these enlightening and confidence-building sensitivity analyses are sure to make your CFO smile in appreciation!
Next Steps
We’ve hardly scratched the surface — so much goes into a carefully considered platform decision! Don’t worry, we’ve got you covered. And, as a next step, we invite you to check out some of our other posts:
- Platform Decisions: When & How to Build a Case
- Platform Decisions: Evaluating Fit
- Platform Decisions: Assessing Viability
- Platform Decisions: Estimating Impact
- Platform Decisions: Testing Assumptions
- Platform Decisions: Differentiators & Productivity Levers
- Platform Decisions: Thoughts About Change
- Platform Decisions: Getting All The Juice
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