Last two decades have seen a large scale automation or digitisation of all the operational heads like finance, purchase, HR etc. Facility is no different. One by one most of the processes are shifting to online systems.
Visitor entries, vehicle management, lease and utility billing, asset management, planned preventive management are some processes which are seeing large scale digitisation.
The good thing is that, though with time, end-users and facility managers have accustomed and at some places are using technology to real good advantage.
But what next?
Are we ready for the next level? Lots of buzz is around artificial intelligence, data science, machine learning and predictive analytics. While running the systems, lots of data is gathered. Can extracting the insightful information from this data be useful or help the management take informed and strategic decisions.
“Yes, of course”, “why not”, “definitely” such words come out instantly.
But the question is how to do it. Since facility comes at very later stages for the management to give deeper thoughts, still we must look forward to it as facility is a kind of spine of an institution if not the brain or heart or whatever.
First of all we can apply the similar methods as we apply in sales or other operations. It starts with forming KPI’s (Key Performance Indicators).
Now, what could be the KPI’s:
- Number of complaints per month
- Percentage of complaints resolved in time ( TAT)
- Number of asset breakdowns categorised by make and type.
- Wrong bills per month
- Items usually goes out of stock in spare inventory
- Number of complaints resolved by each technician in a definite time period
- Your DG efficiency analytics
- YoY trends and comparisons
- Approved vs Actual Attendance
- Budget vs Expense Analysis
And lots many.
So what exactly do we achieve by these KPI’s? We can establish some facts, then determine current status of resources and finally plan for the short term and long term. Further we can reach a level where we can make our decision from diagnostic or planned to predictive methods.
Consider following real life scenarios:
- Pull out the statistical data of asset breakdowns, make-wise in a particular category. Let’s think of HVAC units from brands like Carrier, Hitachi and Samsung. And your system gives you analytical data showing which brand suffered what number of breakdowns in the last two years with ranking. Now you are coming up with a new property and have to decide on which brand to be installed there or you have to do some replacements in existing one. Then with this analytical report you are in a better position to decide on whether to go for Samsung, Carrier or Hitachi by seeing which performed with the least number of breakdowns.
- Another example, it is a curious case of finding out who are the most efficient technicians in a facility team. Suppose your system does all the data mining and comes up with a report mentioning the top performing employees in a particular category from the historical data of the last one year. You see the real figures and know who has closed the most number of complaints in less time. How easy and transparent it becomes for the organisation to be able recognise the performing employees backed by the strong factual data.
Numerous such real life problems can be solved by putting in the predictions, actions, and forecasts deploying concepts like trend analysis, advanced data visualisation through drill-down dashboards, automatic pattern detection, feature extraction and quantified cause and effect techniques.
Though it may seem a bit ahead of times but who knew company line Google could exploit the data in such a smart way to make lives of people simple through easy and practical solutions.
Similar revolution is definitely possible in the facility management field and why can’t we be the first movers?
Please share your comments.