Value Engineering is simply the use of a hypothesis to drive how blocks of work are funded in tranches. Value is measured with rapid feedback loops and continuously compared with cost incurred. This information is leveraged regularly to drive investment and pivots.
In the absence of good data, people tend to get their pet projects funded. Particularly in the enterprise, we often see spectacular amounts of money poured down the drain on systems replacement projects.
According to a Hubbard's book How to measure anything, a study found that 50% of total product development time is spent on "fuzzy" front end activities. This leads to poor investment decisions and long product development cycles that create multiple negative outcomes including: reduced return on investment from long cycles, long feedback cycles from customers and business cases that are viewed as science fiction over time. Even worse, a significant amount of time is wasted on detailed planning, analysis, and estimation, which provides large amounts of information with extremely limited value.
Most large project business cases have 40 to 80 variables, such as initial development costs, adoption rate, productivity improvement, revenue growth, and so on according to Hubbard. Most variables had an information value of zero. The study also revealed that the variables that had high information values were routinely those that the client never measured and the variables that clients spend the most time measuring were usually those with a very low information value.
In the modern economy, the biggest risk is the failure to create something that delivers value to users.
Value Engineering / Lean thinking allow an enterprise to rapidly discard ideas that do not deliver value or will not be adopted sufficiently quickly so we don't waste our resources on them. However, the principles behind the Lean Startup can be applied to all kinds of activities within the enterprise, such as building internal tools, process improvement, organizational change, systems replacement, and programs.
Value Engineering consists of building a customer-centric hypothesis that includes a definition of MVP and often is accompanied by a Lean canvas. Often the OTM (one metric that matters) is often used to build the value statement for the hypotheses. Value Engineering with rapid feedback loops enables a virtuous cycle of innovation in which "Run" activities enable growth. "Growth" activities fund innovation and the "Transform" actives are then operationalized to drive new "Run" activities. With Value Engineering / Lean thinking an enterprise can:
Adopt a mindset in which all our ideas are hypotheses
Safely explore opportunities in conditions of extreme uncertainty
Invest the minimum amount of effort to obtain the maximum amount of learning
Create a clear vision and a shared understanding of the problem
Make decisions on information gleaned from fast, inexpensive experiments
Pivot or fold on bad ideas faster
Engage customers early to act as co-creators of value
Focus on learning rather than revenue
Focus on user engagement over quick financial gain