When it is said that *we can't forecast or estimate*, it brings a smile. Since in fact forecasting and estimating is done all the time. Not always correctly, and not always properly used once the estimate is made, but done all the same, every day in some domains, every week and every month in the domains I work.

In our domains the Estimate At Complete is submitted to the customer every month. And the Estimate At Completion quarterly on most projects we work. These are software intensive projects and some time software only projects. All innovative development, sometimes *never been done before*, sometimes *inventing new physics*.

Some of these estimates are very formal, using tools, reference class forecasting, Autoregressive Integrated Moving Average (ARIMA) projections of risk adjusted past performance and compliance with System Engineering Measures of Effectiveness (MOE) and Performance (MOP), traceable to Technical Performance Measures (TPM) and Key Performance Parameters (KPP). Some are simple linear projects of what it will cost give a few parameters - the *is it bigger than a bread box* type estimates. Here's how to estimate any software deliverable in an informal way.

At last week's ICEAA conference where a colleague and I presented two papers. *Cure of Cost and Schedule Growth* and Earned Value Management Meets Big Data, along with the briefing deck, we were introduced to this book. It says it's name, *you can measure anything*.

Chapter 2 opens with a powerful quote

*Success is a function of persistence and doggedness and the willingness to work hard for twenty-minutes to make sense of something that most people would give on after thirty seconds* - Malcolm Gladwell, *Outliers: The Story of Success.*

That chapter and others speak to making estimates about the things we want to measure. Along with Monte Carlo Simulation - another powerful estimating tool we use on our programs. The process entering our domain (space and defense) is Bayesian estimates - *adding to what we all ready know*.

The instinctive Bayesian approach is very simple

- Start with a calibrated estimate
- Gather additional information
- Update the calibrated estimate subjectively, without doing additional calculations

So if we hear, we can't forecast the future, estimates are a waste, we can't know anything about the future until it arrives — stop, think about all the estimating and forecasting activities you interact with every day, from the weather, to the stock market, to your drive to work, to the estimated cost of the repainting of your house, or the estimated cost of a kitchen remodel.

Anything can be estimated or forecast. All that has to happen is the desire to learn how. Since the purpose of estimates is to improve the probability of success for the project, the estimates start by providing information to those paying for the project. This is a immutable principle of business

Value is exchanged for the cost of that value. We can't know the value of something until we know it's cost. From the kitchen cabinets, the the garden upgrade, to the software for Medicaid enrollment. It's this simple

**ROI = (Value — Cost) / Cost**