This series of posts is going to explore the different types of Facility Condition datasets, with a goal of helping you and your team decide which approach is right for you, right now.

The first, and highest-level type of condition dataset that we see clients leveraging is what we call Modeled or Lifecycle Data.  This essentially entails developing a lifecycle forecast of future renewal needs for a building or portfolio.  With the date of installation, Expected Useful Life (EUL) for an element (based typically on Uniformat II codes), an estimate quantity as well as unit costs, you can develop a model of your future capital renewal needs.

The benefits of modeling include:

1. Significantly lower costs compared with conducting on-site assessments; 
2. The dataset provided is typically at a higher level (Uniformat II Leve 2 or 3) so maintaining the dataset overtime tends to require fewer internal resources; and
3. Models can be developed in a shorter timeframe than conducting on-site assessments as there is no need to mobilize to site.  Finally, in avoiding the on-site assessment, you limit the disruption to your on-site staff and occupants.  

The downside of modeling include:

1. Greater potential for unexpected failure of elements as the forecast is based solely on EUL, a theoretical lifespan for an element, and has not been adjusted for real-world situation;
2. Dataset won’t match the real-world situation within each building, meaning that at a portfolio-level your forecast should be generally accurate, at a building-level the actual needs will likely be very different;
3. Dataset will not provide sufficient detail to easily build a prioritized, multiyear capital plan that can be executed without additional assessment or study; and
4. Integration with Operations and Maintenance programs is limited, as the dataset is very high-level and does not represent the actual elements within buildings.

Modeling generally works best for organizations that are early in their asset management journey and are somewhat resource constrained in terms of managing both the project associated with the data collection and the on-going data management.  

If you have no concrete idea of what your future capital renewal needs will be, a modeling exercise can get you a reasonable, high-level forecast in a relatively short period of time.  Using the high-level dataset, you can start to engage the non-facility stakeholders (finance, program, etc.) by developing visualizations at the portfolio-level to begin to tell the story of your future capital renewal needs.

However, given the high-level and theoretical nature of the dataset, we always recommend that clients communicate the limitations of the dataset, the critical assumptions made and the likely future potential changes to the dataset as the program evolves.  This avoids angst or issues when the dataset experiences significant changes as more and more reality is built into the program.

In the next few posts we will provide an overview of the most common types of building models that can be applied to your portfolio, if you decide that modeling is the way to go for you and your team.