1 / 18

The Cost Modeling Process

The Cost Modeling Process. Introduction. What makes a good cost model? Good Statistics Quality Data Relevant Data Analogous or Applicable Data “Causality” between independent and dependent variables. The Cost Estimating Process.

Download Presentation

The Cost Modeling Process

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. The Cost Modeling Process

  2. Introduction • What makes a good cost model? • Good Statistics • Quality Data • Relevant Data • Analogous or Applicable Data • “Causality” between independent and dependent variables

  3. The Cost Estimating Process • Estimates are always based on history…otherwise, they are mere guesses. History (Data) Predict Future Tools • We use the tools to make the historical data look as much as possible like the future system.

  4. LCCE Process Identify Data Sources & Collect Data Understand the Assignment • Define the Scope • Cost Element Structure • Life Cycle Duration Generate Final Documentation / Presentation Determine Cost Estimating Methodologies Develop & DocumentLCC Elements Perform Sensitivity Analysis

  5. The Modeling Process • Identification of potential cost drivers • Specification of functional forms • Selection of analogous systems • Data Collection • Data Normalization

  6. Identification • Determine what “causes” cost for each cost element • Question experts from government and industry • Identify major cost drivers • Technology • Size • Performance

  7. Causality (Correlation)

  8. Building a Cost Estimating Relation (CER)

  9. Cost Drivers • Technology • New, high risk technology is generally more expensive than existing technology • Difficult to capture • Size • Generally, the bigger, the more expensive • Easy to capture • Performance • The greater the performance, the higher the cost • Also easy to capture

  10. Specification • Determination of functional form • The functional form must make sense • Avoid letting the data determine the shape of the line (unless you have a lot of it) • Get engineering opinions if possible • Remember the goal is to obtain good predictions, not good statistics • Make sure cost behaves as expected when the cost driver varies

  11. Specification Increasing at a steady rate Decreasing at a steady rate Increasing at a decreasing rate Decreasing at a decreasing rate Increasing at an increasing rate Decreasing at an increasing rate

  12. Selecting Analogous Systems • Ideally, we would like systems that smell, taste and look like the items we will be estimating • In reality, DoD has few systems which employ similar technology, performance and size • In general, do not overly constrain yourself when selecting analogous tasks • In order to be called “analogous” the system need only have a similar cost driver and a similar functional form when mapped to cost

  13. Collecting Data • Select systems relevant to system being costed • Choose analogous systems or components based upon elements identified/defined in WBS • Typical cost drivers include physical and performance characteristics • physical characteristics: weight, volume, number of holes drilled, number of parts to assemble, materials of composition, etc. • performance characteristics: power, thrust, bandwidth, range, speed, etc. • Improvements in technology are an extremely important consideration • measures of technology include: % composite material, radar cross section, etc.

  14. Collecting Data • Identify relevant historical cost, technical, and programmatic data to be collected • Program schedule, development quantity, production quantity • Physical and performance data from operating (NATOPS) manuals, manufacturer’s specifications, test data

  15. Data Sources • Data sources include any or all of the following: contractor accounting records, contractor cost data reports (CCDR), cost performance reports (CPR), cost/schedule status reports (C/SSR), cost proposals/bids, or other sources within industry and government • Common denominator is contractor

  16. Data Analysis • Review data collected to insure homogeneity (i.e., standard quantities, constant $), adequate coverage of all WBS elements, consistency with proposed system complexity • Allocate data to WBS elements • Organize data on a consistent basis (system to system, contractor to contractor, WBS element to WBS element) • Ideally would like to distinguish between recurring and non-recurring costs, support costs, direct and indirect costs, profit • Identify problems and anomalies with the data • Gaps in data, jumps in technology, type of program (design to cost vs. other), major failures in development/testing phase, strike by work force, etc.

  17. Data Analysis • Normalize the data as necessary • Consistent units/elements of cost • Adjust for inflation • Develop learning curve to adjust for quantity differences • 1st unit cost • Account for absent cost items, remove inapplicable cost items

  18. Develop Cost Estimate • Four common approaches to developing a Cost Estimating Relationship (CER) • Analogy • Engineering cost estimate • Expert opinion • Statistical/parametric approach

More Related