I have been working with a State-Level University system over the last couple of months who has been presented with two different approaches to developing a forecast of future capital renewal needs.  Throughout the discussions, I have been providing ideas and insights into the values, limitations and drawbacks of each approach.

I wanted to share some of these ideas so that others struggling with these critical decisions could benefit from the discussions that I have had and feedback that I have received.  What started out in my head as a single post has quickly evolved into what I think will be another series of posts.    

The type of condition dataset that will make the most sense for you and for your organization will be based on a few factors including where you are in your asset management journey, the story you are trying to tell as well as the resources you have available to utilize and leverage the data into the future.  For each organization, there will be a right type of dataset for each moment in time.  What might work for you today, may not work for you in the future as you evolve and enhance your thinking and approach to asset management.  

The three main types of condition dataset are as follows:

1. Modelled/Lifecycle Data
2. Time-Limited Forecast of Needs
3. Element-Level Inventory 

There are some different permutations and combinations of the above, but I don’t think it makes sense to get too granular in the breakdown.  Future blogs will provide some options or nuances that can exist within each of these three main categories.

With regards to my conversations with the State-level University system, they were contemplating between Option #1 – Modelled/Lifecycle Data and Option 3 – Element-Level Inventory.  Although not part of that specific discussion, I felt that I should also include a discussion of Option #2 – Time-Limited Forecast of Needs, as it is an approach that many institutions utilize for a variety of reasons.  

The level of effort and associated costs associated with gathering each of these three datasets increases as you go down the list.  However, at the same time the accuracy and overall value of the dataset in terms of developing prioritized, multiyear capital plans also increases significantly. 

Future posts in this series will focus on questions that you and your team should answer at the outset of any program to make the right selection, followed by an explanation of each option and the type of circumstances where each makes the most sense.  

For me, getting out into nature is the best way for my mind to truly go quiet and tackle an issue.  Being near some sort of body of water really helps as well.  However, I can’t always get to a lake, ocean or river so the nature areas in my neighbourhood have to suffice.   In a world of opposites, a noisy coffee shop is another area where I can do some great thinking.  The background noise (conversations, orders being called, background music, etc.) provides a kind of white noise that blends into the background and allows my mind to wander.  

Although it isn’t officially a muscle, I feel that it is vitally important to work out our brain with concentrated and focused thinking mixed with general open thinking and curiosity.  If you haven’t figured out the best place to give your brain a work out, get out there and do some experimentation.