Chancification in Cost Estimation

by Sam L. Savage

In my blog post Making Your S Curves Actionable,” I described my introduction to ICEAA, the International Cost Estimating & Analysis Association. Christina Snyder, Vice President of the ICEAA, and Patrick Malone of Systems Planning and Analysis Inc., an ICEAA member, have assisted me in developing cost estimation examples for future versions of my Welcome to the Chance Age webinar series. We have recently added webinars on September 28 and 29.

ICEAA members already make probabilistic cost estimates based on standalone simulations using such packages as @RISK and Crystal Ball, so they are already halfway to Chancification. What would it take to get them all the way, and so what if they did?

What Would It Take?

Very little. As a reminder, Chancification conveys arrays of simulation trials (SIPs or Stochastic Information Packets) between applications much as electrification conveys electricity between power plants and end users. The simplest way to do this is with the cockroach of all data formats, the CSV file, which can be easily generated from virtually any simulation system. But just as in electrification there is both direct and alternating current of various voltages, in Chancification there are more structured formats, including the open SIPmath™ 3.0 Standard that can generate up to 100 million random variates of almost any distribution along with metadata in a very small JSON file.

So What?

Here are several benefits of Chancification:

  1. Large simulations may be aggregated from small simulations. Imagine a simulation of operating expense for a single aircraft, which takes the cost of fuel into account. To simulate a fleet of 100 such aircraft, today one might just add 99 planes to the simulation you already had. But sooner or later such models collapse under their own weight. Chancification provides an alternative. Each of the 100 aircraft can be simulated separately in various computational platforms. The only requirement is that they use the same SIP of fuel price (and any other global inputs). That way the output SIP of each plane is coherent with the other SIPs in the fleet, as they use precisely the same fuel price on each trial. This in turn means they may be added together to create the SIP of fleetwide cost.

  2. Once the SIP Libraries are generated by the data scientists and statisticians, they may be used in chance-informed decisions by non-statisticians in any environment, including native Excel.

  3. If the 18th century economist Adam Smith was correct, we may see specialization within the industry, with some firms focused on selling high fidelity stochastic libraries of the costs of components, while others focus on assembling these libraries into models of large systems. This will only be possible with standard data formats for moving SIPs from producers to consumers.

I hope you can join us at my Welcome to the Chance Age webinar series to learn more about Chancification in cost estimation and many other areas.