November 7, 2017 | Written by: Jeff Golden
Categorized: AI/Watson | Analytics | Education
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If you walk around a university like ours, one of the really cool things you see is how our facilities management people are woven into the buzz of student activity all around campus. It’s refreshing because it shows that students are aware that things like heat, air conditioning and lighting – things they can easily take for granted – are kept on by the efforts of dedicated people 24 hours a day.
There are about 800 people who work in facilities management at the University of Maryland. They cover thousands of spaces – totaling 13 million square feet – in about 250 buildings. And every one of our staff – from top to bottom – has a huge amount of pride in what they do to create a healthy, positive environment for learning and research. These are people who know their way around very complex systems, have a ton of experience and the judgment to know which buildings and systems need their attention at any given time.
Where every dollar counts
Here’s another thought. I’m not sure our facilities people – or the students they serve – recognize just how important they are to holding tuition costs down. It’s all about resource efficiency and smart decision-making. When you have facilities that range from 100 years old to brand-new and everywhere in between, it’s a major challenge to stay ahead of maintenance problems and focus resources efficiently. We recognize that every dollar counts and we take it very seriously. That’s why we adopted IBM TRIRIGA Facility Operations & Maintenance with Watson Analytics. We’ve made it central to how we prioritize our maintenance activities. It’s about applying cognitive analytics to resource decisions.
The University’s Facilities team receives over 65,000 work orders a year. Anyone on campus can submit a request for work into the TRIRIGA Facilities system. An enormous volume of data is created when managing thousands of requests, work orders, assets and hundreds of vendors and maintenance shop personnel. This is all captured in TRIRIGA, and fed to IBM Watson. The work is assigned and billed to the University division or department that makes the request – sometimes this is billable and sometimes it is not. TRIRIGA captures that full lifecycle from request to completion.
Now the thing about our technical ops people is that – for the most part – they’re more comfortable with physical tools like wrenches than they are with analytics, much less cognitive technology. We saw that to get the most out of analytics – and to drive the adoption of analytics among our middle managers – we needed to minimize the cognitive intimidation factor while demonstrating clear, simple and compelling benefits right off the bat. That’s what led to our biggest “aha moment.”
An epiphany sparks excitement
Early on in the project, we were analyzing our billable service tickets and we saw a declining trend that we first interpreted as a natural, seasonality thing. It wasn’t. Watson’s analysis uncovered what all of us had missed: a data error. Fixing that one error enabled us to recover $30,000 for a billable service ordered. Without Watson, the service would have been missed and absorbed. The bigger impact of this cognitive discovery was the ripple effect it had at the cultural level. When we presented it to our operations people, manager started coming up to me basically saying, “What is that thing? What can it do for me? Show me, teach me.” That’s just the kind of excitement you need to build to get non-technical people on board the analytics train. And we had it.
University of Maryland is like just about any other organization when it comes to making the case for investing in new technologies. Reducing costs is everything and numbers speak louder than words. That’s one reason the $30,000 made such a splash. But I think the bigger impact of this episode is that it demystified cognitive analytics and opened the eyes of our middle managers as to what it can do. This plays directly into our self-service strategy, giving our middle managers -who aren’t statisticians by training – a way to dig down into their operations to make better decisions that will save lots more down the road.
Marrying facilities management and analytics
When it comes to the role of facilities management in university life, students are likely to point to whether an overhead projector works or whether their dorm has hot water. And that’s the way it should be. But here at University of Maryland, students should also know that our facilities management people are also committed to doing those things smarter, better and more cost effectively. Building cognitive analytics into the core of our facilities management planning represents a huge step in that direction.
For more stories about how IBM is transforming facilities management with IoT:
Hear more from the University of Maryland’s Jeff Golden in the video interview below: