Analytics in Construction Asset Management

Leveraging Data Analytics to Optimize Asset Management

In construction, asset management is critical to ensuring your fleet is functioning to the best of its capabilities. Following standard best practices on repairing and maintaining can help, but is it possible there is a better way? Data science can help improve efficiency and offer new options for managing assets, automating inspection scheduling, and predicting breakdowns.

Asset Data

In data analytics and entry, Asset Data contains the data about the actual asset.  Brand, age, size, type, utility, current_location, estimated shelf-life, cost code, usage_to_date, description, and cost are some of the things that are captured here. You may have different data fields on different assets, that’s okay. This data is usually located in the Fixed Asset Rollforward and Detail but can actually come from a system that feeds into it.

Leveraging Fleet Maintenance Inspections and General Repairs

During routine fleet maintenance, inspection, or repairs, if data gets input electronically at any point, you might be lucky enough to have certain data points.  Some of these data points include checkboxes on results, some text being added on certain descriptions, and some numeric indicators decreasing or increasing.  This might be in another system or could be in a preferred vendor’s database.  With general repairs, it’s not always based on the maintenance report.  This data source lets you see a longitudinal view on a per asset basis.  The vendor providing the service may have this data in a clean format, so it can link to the repair bill and the specific asset.  If this is true, then the data points that can be captured with this data set is asset_id, cost of repair, and a description of what is fixed.

Unpredicted Fleet Breakdowns

Equipment will break down on-site may result in a cost that is significantly more than a repair. The data points gathered from this would be the asset_id, the location, the issue, the additional logistics cost, and the opportunity cost (can approximate based on a rule-of-thumb).

Any of these data sources cleaned and combined can offer insights into better fleet asset management, explored below:

Asset Data + Maintenance Inspections and General Repairs

Better assets by brand.  Should you lease? Can you predict more costly repairs and then mitigate it?

Asset Data + Unpredicted Breakdowns

Can you budget better for unexpected breakdown costs? Do you have more reliable assets to use? Can you make rules to use certain assets further from the home base?

Maintenance Inspections and General Repairs + Unpredicted Breakdowns

Predict breakdowns based on maintenance reports. Optimize what to repair.

Asset Data + Maintenance Inspections and General Repairs + Unpredicted Breakdowns

Predict repairs and breakdowns with asset data and maintenance inspections. Determine when to cornfield an asset. Predict the longevity per asset (rather than using a standard depreciation schedule)

Your business holds secrets to success within the data collected in every piece of the business – from inventory to sales to the core product/service. Data Science can unlock those secrets and permanently position your business for improvement and growth.

Keiter is now proud to offer Data Science and Analytics Services with our latest practice department, Innovative Data Solutions.

Please contact us if you have any questions regarding this content or how data science can serve your unique business needs. Contact Us | 804.747.0000