Delivering proof is the motivation and intent behind many if not most case building efforts. The authors might as well write in the opening lines,
"I want to prove that ...
" ... my proposed acquisition is financially justified."
" ... funding my project is a good business decision."
" ... I acted wisely and responsibly in taking this action."
Case builders intent on proving something often go on to produce the return on investment (ROI), internal rate of return (IRR), or net present value (NPV) expected from their proposals. These financial metrics may be very attractive, but for some reason, they just don't "make the case" with CFOs, review boards, or other senior management. There may be doubts all around that anything can be "proved" with a business case.
Not everyone appreciates, however, how business case analysis can indeed deliver a very strong form of proof—if everyone involved understands the nature of business case reasoning and business case evidence. Many are surprised to learn that this has very little to do with finance and quite a lot to do with understanding the scientific method. Solid business case proof relies on the same reasoning that makes possible proof in physics and chemistry laboratories. In brief, business case proof has much in common with the laboratory controlled experiment.
Explaining Business Case Proof Concepts in Context
This article explains business case proof rationale—the same reasoning that supports proof in the science laboratory. Discussions below explain proof reasoning in context with related terms and concepts, including the following:
- What is business case proof? How does the case builder make the case?
- Proof with a controlled experiment: The scientific method.
- How do you prove your proposal is the better choice with a business case?
- Will anyone trust your business case proof?
- See Business Case for a complete overview of case structure and contents, including case elements that support the author's reasoning and conclusions.
- The article Business Case Cash Flow Statements shows how scenario-specific cost and benefit estimates follow from underlying assumptions.
- The article Risk and Sensitivity Analysis for the Business Case shows how case builders build credibility for projected outcomes and address the question: "Will we really see these results?"
In chemistry, physics, and other kinds of laboratory research, the scientist tests the idea that one factor causes another, or that one theory accounts for reality better than another, with a controlled experiment. The researcher first creates two or more experimental test conditions. Throughout the experiment, all receive exactly the same treatment in all ways, except for one or more "experimental manipulations." At the end of experimental trials, when different outcomes appear in the different test conditions, the "only reasonable explanation" for the difference is the experimental manipulation. The lab researcher writes:
The experimental manipulation caused the different outcomes!
Laboratory Example: Proving penicillin Kills Bacteria
It is one thing to suspect that one factor impacts another. But suspicion is not proof. The discovery that penicillin kills certain bacteria, for example, had to be accomplished in two steps: Suspicion first, then proof.
Suspicion Comes First: The Observation
Alexander Fleming discovered the efficacy of penicillin in 1928. As a physician and as a research microbiologist working at St. Mary's hospital in London. Fleming was searching for ways to combat an especially virulent strain of Staphylococcus bacteria. The bacteria—Staphylococcus aureus—was the known cause of especially destructive post-operative infections.
For this research, Fleming had some bacterial cultures in the laboratory, growing in open dishes. He was surprised to discover, one morning, some of the cultures were contaminated with small deposits of a certain fungus mold—Penicillium notatum—that had drifted in from the atmosphere. The especially striking finding was that staphylococcus bacteria surrounding the mold had died. At the same time, bacteria in uncontaminated cultures were still thriving. At this point, Fleming had a strong suspicion that this particular mold was effective in killing bacteria.
Proof Comes Second: The Controlled experiment
Before taking Penicillium from the laboratory into the clinic, Fleming needed proof that this particular fungus caused bacteria death, not something else. Proof, in such cases, requires an experimental control (actually a series of quite a few experimental tests).
The experimental design for each test, however, was straightforward: Fleming divided a group of healthy bacterial culture dishes was divided into two groups: (1) Cultures in one group received a small deposit of Penicillium notatum, while (2) Cultures in the other group received a small deposit of another mold. Other than that, all cultures were allowed to grow under the same conditions for a period.
Throughout, researchers monitored both groups for bacterial growth and longevity. Because these experiments were carefully controlled in this way, the only reasonable explanation for the effect on bacteria, was the presence of Penicillium. These tests produced acceptable proof of efficacy.
By the same kind of reasoning, the business case author “proves” that one proposal or another is the better choice for action by comparing two or more carefully designed scenarios. Generally speaking, a scenario is an account, or story, that describes what happens under one course of action.
Comparing Scenarios Instead of Test Conditions
Business case scenarios—just like test conditions in the lab—are identical in all respects except for one or a very few proposed actions. The business case author analyzes outcomes in each scenario in business terms, including:
- Financial metrics such as total cost, net cash flow, NPV, IRR, and IRR.
- impacts on key performance indicators (KPIs) for nonfinancial outcomes.
Proof Lies in the Comparison
If the business outcomes in a scenario called "Implement the Proposal" differ from outcomes in another scenario, "Business as Usual," and if the differences are important, large enough to matter, and likely, the business case author may write:
"Proposal scenario" actions lead to better business outcomes than continuing under "Business as Usual."
The business case writer—just like the laboratory scientist—provides statistical evidence that the different scenario outcomes are not due simply to random variability in assumptions underlying cost/benefit estimates. For the business case author, risk and sensitivity analyses serve this purpose: For instance, the author may produce confidence interval estimates such as these:
- The 95% confidence interval for net gains under the proposal scenario is $8.00 million to $12.00 million.
- The 80% confidence interval for the 5-Yr ROI under the proposal scenario,
is 26% to 34%.
To make such proof credible, the writer must show that different scenarios belong to a "controlled experiment," that is, the casebuilder created and analyzed all scenarios objectively by the same cost-benefit rules. The author's case report—just like the chem lab report—has a "Methods" section with the cost model, benefits rationale, scope and boundary statements, and major assumptions under each scenario.
Like a good trial lawyer summing up a case, the business case author finishes by taking the business case "jury" once more through the proof rationale:
I believe you will decide that funding my proposal is the better business decision for this company!
- Better is a relative term.
I have shown that proposal outcomes are better, relative to "Business as Usual" outcomes, using our key financial metrics and KPI's.
- I have shown that scenarios were compared objectively and fairly.
- I have shown that the uncertainty in assumptions underlying projected outcomes is small and acceptable.
For a brief, complete coverage of business case "proof" and the role of case design, risk, and sensitivity analysis, see 4th Edition of "Business Case Essentials."