Global pressure to reduce costs lead to a real estate portfolio rationalization program.
The client wanted to identify which locations to focus on first, and how much cost could be avoided without impacting productivity.
The program involved moving from traditional assigned seating to unassigned hoteling.
Used physical access control (security badge) data to model attendance
Analyzed large volumes of data from multiple facilities to identify the factors affecting attendance
Applied sophisticated models and space plan reviews to predict the future demands and cost saving opportunities
Cost saving opportunity identified
More effective than traditional methods
Of maximal saving potential as measured after project was predicted by analytics
Additional annual cost savings identified beyond traditional methods
How we delivered these results:
Six-to-twelve months of historic security badge data was analyzed for key facilities where there were either or both high costs or known low-occupancy
Typical attendance behavior expectations were modelled and compared to the data collected. This allowed us to confirm which attendance patterns each individual exhibited, and also the people or days that fell outside expected patterns
Regression analysis was used to isolate these outliers, and the environmental factors influencing the period of analysis were identified
Where causality between these factors and the measured attendance could be identified, these were incorporated into the predictive model, and Monte Carlo simulations provided very accurate forecasts of demand
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