COTS Estimating Metrics for Increased Cost Accuracy
Observed cost analysis issue was that multiple Commercial Off-The-Shelf (COTS) Hardware/Software cost estimates were significantly higher than recent contract award values. Using data from those contract awards, an analytical approach and metrics were developed to ensure increased accuracy on future estimates. For COTS Hardware (HW) and Software (SW), a metric of the actual contract price as a percentage of the mean online price/list price (Part I) was developed. Additionally, an estimating relationship was developed (Part II) to calculate the cost of annual maintenance support as a percentage of the actual HW/SW initial cost and the annual maintenance support actual contract price as a percentage of a vendor quote. The awarded contracts used for analysis differ among the three metrics developed. In all cases, as these ratios are risk-adjusted to higher confidence levels (increased), the cost outcomes increase as well.
Part I: The discount off of mean online pricing metric allows for discounting from a common point (i.e. after all competitive market forces have played themselves out). For each awarded contract, the COTS HW/SW List of Materials (LOM) was sorted by part number, description, and vendor, using an Excel pivot table. Next, we researched online pricing for each unique part number. The mean online price was computed for each unique part number, and both the mean online price and contract price were normalized to BY2011$. Within each contract, each unique part number was multiplied by its corresponding quantity, and the contract price as a percentage of the mean online price ratio (for that contract) was computed. Analysis of the dataset was performed using the CO$TAT Distribution Finder tool, to determine the best fit distribution and risk bounds. The resulting discount percentage can be applied to any future LOM for which online pricing can be found. Utilizing the mean online price offers a more useful result than list price; it shows a lower dispersion of data (i.e. lower Coefficient of Variance — CV). However, in instances where online prices cannot be found, the contract price as a percentage of the list price was computed, and can be used where only list pricing data is available.
Part II: For each awarded contract where its associated LOM was detailed enough to differentiate between the total initial maintenance support costs and actual HW/SW initial costs, the cost of annual maintenance support actual contract price as a percentage of the actual HW/SW initial costs was calculated. Analysis of the dataset was performed using the CO$TAT Distribution Finder tool, to determine the best fit distribution and risk bounds. The resulting percentage can be utilized to estimate maintenance support renewal costs for a given HW/SW LOM.
For each awarded contract, the annual maintenance support cost on contract and vendor quotes were normalized to BY2011$. Again, analysis of the dataset was performed using the CO$TAT Distribution Finder tool, to determine the best fit distribution and risk bounds. The resulting discount percentage can be applied to any future vendor quote for the estimation of annual recurring maintenance support costs. Both annual maintenance support approaches can be used as primary and secondary methodologies.
Tecolote Research, Inc.
Joshua Patapow has a total of 3 1/2 years of cost estimating experience. He is a SCEA member and is a Certified Cost Estimator/Analyst (PCEA). He is currently employed with Tecolote Research. He graduated from the State University of New York (SUNY): College at Potsdam with a Bachelor of Arts degree in Economics in 2008.
Tecolote Research, Inc.
Eric Timinski has a total of 8 1/2 years of cost estimating experience and is a Certified Cost Estimator/Analyst (CCEA). He is currently employed as an Advanced Analyst with Tecolote Research. Prior to working for Tecolote, Eric worked for Eagle Window New England (a local window and door company) as an Estimating Manager. At Eagle Window New England, he was responsible for preparing bids and negotiating price breaks with manufacturers. He graduated Magna Cum Laude from Clark University with a Bachelors Degree in Mathematics in 2003. Eric is married with two daughters, Temple and Eden.