Operational Availability (A o )
5.5.9 Facilities Condition Assessment
Introduction 5.5.9.1
The purpose of this section is to provide an overview of the facility condition assessment process which is designed for preparing, understanding, and presenting consistent,
repeatable, and auditable deferred maintenance estimates for each facility listed in the NASA real property inventory.
Guidelines and procedures are presented to do the following:
Provide for a common understanding from which to produce consistent facility systems inspections and evaluations.
Ensure process uniformity in validating and developing real property information.
Background 5.5.9.2
In FY02, NASA-HQ used the DoD15 Model to develop a NASA-wide, consistent parametric cost estimating method for documenting facility condition and estimating deferred maintenance. Designed to be a simplified approach using existing empirical data, the method is based on:
Condition assessments performed at the system level rather than the component level which is consistent with the NASA Reliability-Centered Maintenance approach.
A limited number of systems to assess (nine). Use of generalized condition levels (five).
Current replacement values (CRV) of the systems and the facility they support. The Deferred Maintenance (DM) Model uses cost estimating relationships (CERs) based on existing engineering data and associated algorithms to establish cost estimates. A building system (e.g., a plumbing system) can be developed using very precise work measurements. However, if history has demonstrated that repairs normally cost about 25 percent of the original value, then a detailed estimate need not be performed and can be computed at the 25 percent (CER) level.
Different cost models were developed and are applied according to facility type and condition rating to best represent estimated repair costs for the many facility types within the NASA real property inventory.
The DM Method begins with a rapid visual inspection. Assessors rate each of the following nine building systems, from Excellent (5) to Bad (1) for 43 different facility types.
Structure
Exterior Roof HVAC Electrical Plumbing Conveyance Interior Finishes
Program Support Equipment
When the assessments are complete, the ratings are entered into a database where the ratings are processed through a parametric estimating model that uses the current
replacement value (CRV) as its basis. The CRV is apportioned among each of the nine facility systems, using different System CRV Percentage models for each of 43 different facility types. The DM Model produces the following three sets of metrics:
System Condition Index (SCI). SCI is a rating derived from the condition
assessment ratings for one of the nine building systems.
Facility Condition Index (FCI). FCI is the sum of the nine weighted SCIs,
providing an overall condition rating for each facility.
DM Cost Estimate. The cost estimate is a measure that indicates the degree of
facilities work that has been deferred for budgetary reasons and that is required to restore the facilities to a good, usable condition.
Methodology 5.5.9.3
The following encompass the methodology of facilities condition assessments.
Parametric Estimating
“An estimating technique that uses a statistical relationship between historical data and other variables (for example, square footage in construction, lines of code in software development) to calculate an estimate for activity parameters, such as scope, cost, budget, and duration. This technique can produce higher levels of accuracy depending upon the sophistication and the underlying data built into the model. An example for the cost
parameter is multiplying the planned quantity of work to be performed by the historical cost per unit to obtain the estimated cost.”16 Existing NASA Classification Codes were mapped and linked, based on facility similarity, into 43 DM Category Codes.
16 For more information, see
Parametric cost estimating methods are based on physical or performance characteristics and schedules of the end items. The estimate is derived from statistical correlation of historic system costs with non-cost parameters, such as quality characteristics of performance or physical attributes of the system. Parametric estimating techniques focus on cost drivers. Figure 5-6 provides a generic parametric model.
Figure 5-6. Generic Parametric Model
Cost Models • System CRV % - PACES
Model
• System Condition CRV % - RSMeans estimate
Data Analysis and Correlation • Statistical analysis • Sensitivity analysis • Histograms • Qualitative analysis
Data Evaluation and Normalization • RPI, CRV
• Land Values
• Low value and remote sites • Normalization of data • Remapping facility types
Test Relationships
Estimated
Actual
Post Processor • System Condition Index • Facility Condition Index • Deferred Maintenance Estimate Selection of Variables • Facilities to be assessed by category • Facility systems to be assessed • Rating system Data Selection • Assessment Teams – On-site
visits w/guides
• Remote assessment using interviews, pictures and property cards
Multiple Regression and Curvefit C = aX C = aXn C = aX + by Cost Models • System CRV % - PACES Model • System Condition CRV % - RSMeans estimate
Data Analysis and Correlation • Statistical analysis • Sensitivity analysis • Histograms • Qualitative analysis
Data Evaluation and Normalization • RPI, CRV
• Land Values
• Low value and remote sites • Normalization of data • Remapping facility types
Test Relationships Estimated Actual Test Relationships Estimated Actual Estimated Actual Post Processor • System Condition Index • Facility Condition Index • Deferred Maintenance Estimate Selection of Variables • Facilities to be assessed by category • Facility systems to be assessed • Rating system Data Selection • Assessment Teams – On-site
visits w/guides
• Remote assessment using interviews, pictures and property cards
Multiple Regression and Curvefit
C = aX
C = aXn
C = aX + by
Parametric estimating relies on simulation models that are systems of statistically and logically supported equations. The impacts of a product's physical, performance, and programmatic characteristics on cost are captured by these equations. The object to be estimated is described by choosing specific values for the independent variables in the equation which represent the characteristics of the object. The equations are then used to extrapolate from past and current experience to forecast future cost.