Presentation om "Smart buildings and the internet of real estate things: from hype to value" av Koen Matthijs från MCS Group.

1. 1
Software & Advisory for Integrated Real Estate,
Facility & Workplace Management
www.MCSsolutions.com 09/02/2017
SMART BUILDINGS
from hype to value

2. 2
The importance of choice
Death of the office phone
Kill meetings before they kill you
Get out of PPT jail
The changing workplace

3. 3
Personas vary in :
Mobility (indoor, offsite)
Maverick vs process
Technology adoption
Content or Social
Catering to different worker personas

4. 4
4 main drivers today
Why Smartening the building?
OCCUPANCY
USAGE
WELL-BEING
ENERGY
METERING
WASTE
USER SATISFACTION
CONVENIENCE
It seems that we can improve the performance in these domains
using technology which has become “accessible”….
Technology which is commonly identified generically as IoT

5. 5
Newest and easy to implement technologies can help the (Facility) Manager to reach their goal…
Smart Buildings – how?
• Affordable, multi-purpose
• Easy to deploy
• Low activation cost
Wireless
sensors
• Accurate
• Multi-purpose
• Easy to setup
Indoor
positioning
• Descriptive – Predictive – Prescriptive
• Machine learning
• Connecting to existing data
Big Data
Analytics
• Touchscreen kiosk / Digital Signage
• Mobile apps / Wearables
• Skype
Touchpoints

6. 6
Connected Building – what?

7. 7
Start with the end in mind : Top scenarios
Refill the coffee/water
points when empty
Replenish paper in the
printer pro-actively
Choose a flex desk based on real-
time availability
Track meeting room
occupancy in real time
Triggers are sent to mobile
devices
Clean the toilets based on
actual usage
Collect elevator data to get
insight on usage

8. 8
Room/Parking
Assistant
Well-being
Monitor
Energy
Monitor
Cleaning
Controller
Maintenance
Assistant
SMART PRODUCTS

9. 9
Input based model (Hours) Output based model (Result) Outcome based model (Satisfaction)
Service Models

10. 10
Direct the daily
cleaning capacity
where needed
Traffic camera
Occupancy sensor
Case 1 : Cleaning Controller
Ad hoc requests from
users
Clean-me-now button
Detect poor quality from
user feedback
User polling
Trigger intervention
based on usage
frequency
Door counters

11. 11
Benefits
Case 1 : Cleaning Controller
• See impact where most needed
• See responsiveness to specific demandsUsers
• Feel connected to the business
• Less repetitive
Cleaning
personnel
• More value for cleaning budget
Facility
Manager

12. 12
Room
Assistant
Well-being
Monitor
Energy
Monitor
Cleaning
Controller
Maintenance
Assistant
SMART PRODUCTS

13. 13
Room Assistant

14. 14
The best UI is no UI
A new Skype friend “Sumi”
Answers lots of questions on room
availability, room location,…
Learns and improves content based on
past questions
This “friend” appears in your conversation
history in the same way as a chat with
another user
Natural Language based Personal Assistant

15. 15
Benefits
Case 2 : Room Assistant
• Avoid walking to find room
• Activity based working – find the room
that fits
Users
• Optimize occupancy
• Avoid booked rooms that remain empty
Workplace
Manager

16. 16
Room
Assistant
Well-being
Monitor
Energy
Monitor
Cleaning
Manager
Maintenance
Assistant
SMART PRODUCTS

17. 17
From fixed frequency to usage/condition-based interventions
Examples of sensors on technical installations
Input
Pressure of the cooling liquid in HVAC systems
Temperature of the cooling liquid or server room
Cycle counter (# of cycles or hours used)
Filter sensor to detect when a filter needs cleaning or
replacement
Electricity consumed by AC system
Output
Alert
Work order
Change in maintenance plan
Trigger maintenance based on condition or usage
Air handling units
Chillers
Compressors
Pumps
Elevators

18. 18
CO2
Temperature
Humidity
Case 3 : Trigger maintenance based on condition or usage

19. 19
Benefits
Case 3 : Trigger maintenance based on condition or usage
• Better comfort
• Fewer complaintsUsers
• Only maintain when needed
• Be proactive
• Avoid downtime
Maintenance
Manager

20. 20
Feedback Collector: touchpoints
Kiosk
Mobile
Feedback Display
Dashboards

21. 21
It takes a few maturing steps to get there ….
… steps from the data science process one walks
through to reach the final goal of the IoT scenario
Optimisation takes a few steps

22. 22
Prediction
(ML)
The MCS data science process & guidance
Categorize
(ML)
Visualize
aggregated
data
Visualize
raw data Regression
(ML)
1) What is the current situation?
2) Can we derive KPI’s from the data?
5) What will the situation become?
4) What is affecting the situation?
3) How do different groups compare?

23. 23
Where do I start?
What do I need to measure?
Right way to invest in sensors?
Which sensors to invest in ?
Where is our business case for activating IoT scenarios?
Make it ‘consumable’ by users?
How to create internal awareness?
Will it bring me the expected benefit?
How to get started
Challenges of CONNECTED Buildings