this is just a quick introduction to
Smart facilities management using
intelligent digital Twins and it will
give you a common operating picture that
puts you in control
when we talk to
asset managers and Facility Owners we
hear some of the challenges that they
see is really around asset monitoring
prioritizing repairs budget constraints
emergencies and unplanned events
trying to coordinate with other
departments vandalism public misuse
Regulatory Compliance technology
integration vendor and contract
management
public engagement and and feedback staff
training and management and also
environmental concerns so quite a
complex set of challenges that
facilities managers are trying to
address and digital twins help in some
way to address some of this so some of
the typical applications or use cases
and these are this is just a few
examples
that digital twins are being applied to
in facilities management is really
around predictive maintenance for
infrastructure helping with asset
monitoring and prioritizing repairs
having sensors there it can predict when
assets May Fail and it can use all sorts
of algorithms and things like that we'll
we'll dive a little bit deeper into it
and I'll show you some examples of how
it does it
another application area or use cases
around resource optimization and
budgeting really helping with budget
constraints which quite often is one of
the bigger challenges for facilities
compared to other Industries
we also deal quite often with things
like Disaster Response and recovery from
that so knowing when certain certain
events and emergencies unplanned events
happen and what can be done
even with that operations intelligence
real-time intelligence getting that
common operating picture knowing what to
do next
the other key challenge that we that we
address with digital twins is
integration to other external systems
and quite often there's a whole bunch of
departments in different silos they're
done really well work well together and
it's one of the benefits that a digital
twin can bring is being able to
integrate that so for example of a
rainwater main breaks always predicted
to to to break then that information can
be relayed to things like traffic and
traffic Management systems and other
applications and and systems that are in
the organization
and quite often the influence of the
public and trying to keep them involved
as well as updated is one of the key use
cases that again is slightly different
in Facilities Management to what we see
in some of the other Industries now
this is all around very specific use
cases and if we come from a controller
automation engineering background
you should be familiar with this ISO 95
model which is kind of the Purdue model
or how we control have controllers and
things in the plant and throughout our
our facilities and how that rolls up
into skyde systems and the different
Management Systems so those are
typically referred to as
distributed control systems and
Facilities Management they vary
depending on what the type of
infrastructure it is that you are trying
to manage so if you go to pump water
filtration plants pumping stations more
sophisticated than what you find in some
of the
recreational Services areas like parks
and some of the others but what we've
done is we've taken that same picture
and kind of flipped it over and said so
what what does a digital twin provide
for you if you've got a distributed
um control system on the one hand this
gives you a distributed intelligence
system by using all of these different
use cases and that's the main difference
between having a digital twin and just
have for those previous few use cases
that I showed just have some point
solutions that that work in isolation
what you can now do is at your
facilities level at the at the plant
level you can have multiple different
use cases
and again we'll touch on on some of them
I'll show you examples of that but we
can start rolling them up integrating a
more compa
composite one that gives us a better
tactical view so this might be
operational at the lower level getting
to a more of a tactical view at the next
level
and then being able to bring in business
logic business rules
and models at a
at a higher level which will give us the
ability to create this control tower now
if we combine the two we actually get a
new level a strategic level and that's
the common operating picture or this
executive control tower that gives us
the opportunity to to make better
decisions even though at level one where
the digital twin operate its use case is
still very specific like we saw in the
previous example where or the the
previous slide where we it's
specifically around certain asset
maintenance or event response or those
but it's the combination of being able
to bring all of that together it's a
real benefit of the digital twin
and now you also have some levers that
you can pull depending on budget States
or some other uh
operational or strategic levers that you
may have you can change some of the
business rules and that will influence
how these digital Twins then behave so
can we spend more money on predictive
maintenance
what is the situation with our staffing
in terms of resources and and that kind
of thing
so this is the real opportunity with
digital twins being able to create this
common operating picture by stringing
together or building it up from multiple
different use cases so the core Focus
for Us is around the use case and the
way that we like to do this in practice
how do we actually build this is to
create this common operating picture
you've got the assets on the left hand
side and then we have this capability
which we call data stream designer that
can suck information from multiple
different systems
and it can transform it it can apply
some analytics to it and we can then
create some actions coming out of that
we can also bring in things like AI into
this and I'll be showing you some
examples of what this looks like in an
actual application
um for predictive maintenance
you don't always have to have the AI
side of it sometimes we just have
engineering calculations thresholds
those kind of things more condition
monitoring but as we move to predictive
maintenance and predictive use cases
predictive operations
we start bringing in more AI
the XM Pro app designer is the third
element so this is the visualization and
this visualization is not there to
replace existing 3D GIS models or Point
scans and things that we've done or
Bim models or that it is really taking
those and actually using some of those
user interfaces like Gia systems and
putting the contextual information
around it with the recommendations which
is the next step that's actually what
you really want to in certain events
occur or likely to occur
you want to create a recommendation for
someone to do something it might be to
investigate it it might be to repair it
it might be to
discard it it doesn't matter what it
what the what the outcome is different
scenarios will have different outcomes
but how can we make sure that we have
consistency around what the recommended
action is so that we can Empower people
processes and provide some automation
around this as we go forward
around certain areas or outcomes in our
operations
a little bit more sophisticated version
of the same common number writing
pictures I've got all of these things on
the left hand side senses I've got all
of these systems inside my organization
and I'm trying to get the response done
with people processes and automation
and what we do we have this
user interface where we can in a visual
way build the data streams again get
data from multiple different systems
apply analytics to it provide context to
it so not only do I can I bring in
sensor data but I can bring in
maintenance records I can bring in
weather data I can bring in all sorts of
data and build a for my specific problem
a a data stream that will feed and give
me the the information that I need
to create a common operating picture and
in this common operating picture you can
see at the operational level at the
bottom the Tactical level which is the
planning level and the Strategic level
everyone's looking at the same data but
through a different lens for different
reasons for different things that I want
to see at the top it's more kpis
at the middle level it's more on
planning when we have when do we have
multiple things
in the workshop at the same time and
we're over capacity so we can't actually
deal with that and at the bottom you
know looking at the actual facility and
while I'm there what's happening
um do it but it's all the same data and
this is what we call event intelligence
and now when certain events occur
based on the real-time data
we can turn that into operations
intelligence for for the decision
support and if we combine that with
recommendations which is a key element
of our solution
through our recommendation manager and
recommendation rules
that can also bring in from other alarms
and alerts and things that you may have
in the organization but at this at the
highest level and at the lowest level it
all works the same and you can combine
all sorts of different systems to create
a set of recommendation rules and I'll
be showing you what that looks like
it enables you to empower the smartest
people in your business to pull the
levers as I mentioned earlier
it will reduce the risk of being
blindsided by key business events that
are happening or likely to happen and it
also improves the accountability because
you now create a feedback loop a closed
loop feedback system
so that you have the visibility around
you know always getting better are we
closing the work orders you know
is the facility running better so you
can create those closed loop monitoring
capability that also improves the
accountability for that
as you can see we've got some elements
in terms of what is in excellent Pro
um so we call that our intelligent
digital twin Suite consists of four
different areas the data stream designer
AI the app designer and the
recommendation manager and before we go
into that I'd like to highlight uh
something that we quite often see when
we talk about digital twins people just
see the visualization part they see this
really nice
digital twin user experience that has
got dashboards it's got bi it's got 3D
it's got IR VR all sorts of really nice
looking visual interaction the challenge
with that is uh
that that is just the visualization
like this Iceberg the real challenge 80
of the work sit below the surface and
that is where you need to make sure that
it's safe secure reliable that it's
trustworthy you need to be able to
integrate all these different systems
and then Wrangle that data provide
analytics over that being able to bring
in things like AI that it's not just
Standalone but it's that it's baked into
the business process and then creating
these recommendations that can run on
top of that that is all the things that
are required to actually build a
successful digital twin at the end of
the day it's not just about the user
interface that sits at the top
and what we're starting to see in some
instances we don't even need a user
interface because we can automate the
whole process
for those tedious things that you
actually don't want a human to
to get involved with so what I'll be
doing next is taking you through an
example we'll just take the predictive
maintenance for infrastructure
and looking at a facility management
um
application at a very high level in
terms of how we how we address that
specific challenge now for us from a
smart Asset Management point of view it
is really how do we connect to the data
how do we provide the recommendations
and alerts for the planning team so be
able to to plan work then actually
create the work orders work requests
into the backend systems that you may
already have
and um
augment that with additional information
so that it's not just this things like
safety information additional context
operational information that you can
actually pass on to that work ordering
request to make it more rich and then
also checking or verifying that the work
has been done so for us that's the
closed loop process that we like to
follow around smartassive management
and in terms of use cases for facilities
management we see applications around
condition monitoring prescriptive
maintenance predictive maintenance
machine intelligence this is where we're
getting to move the real-time
operational side of things or autonomous
operations
and from a talk from a technology aspect
it uses things what we call composable
so we can make reusable blocks so we
don't have to build it every single time
make some really smart integration
automate some of the business process
areas of that and we try to do all of
this in a no code application
development which environment which I'll
show you so that you don't have to code
the stuff
and lastly the real objective of this is
that you can
reduce downtime increase the asset life
and the cost that you have around it but
we also see more and more that there's
emphasis on the ESG side of things the
environmental societal and government uh
the the the the governance requirements
so that is the objective of of using
digital twins to help you with smart
Asset Management now before we go into
the example and also just take a a brief
minute to explain how we often see the
journey happens because
you may have so many different things
that you could potentially do and one of
the approaches is to look at from a
for the predictive maintenance condition
monitoring prescriptive maintenance type
use cases is to go and look at or
analyze who are The Bad actors and what
percentage of impact do they have so you
can see the size of the block they
represent that I can also then look for
a certain bad actor maybe say it's in
the pump stations and certain
centrifugal pumps I can look at the type
of failure modes that they have you know
the bearing failure see all failures
um whatever it may be and then for those
failure modes trying to understand what
the root causes are
and the reason why that why we want to
have the root cause is that we know what
to look for so what are the lead
indicators that tell us something is
causing so which will cause a certain
failure and that results in the bad
actor
so now that we understand what the root
causes are and what and the the this is
really the bread and butter of a lot of
Maintenance organizations that we have
right or the the work that we see being
done right now
so understanding what the root causes
are
and then look for what are the lead
indicators for that root cause so is it
the is it the the
plan maintenance history the tonnage the
motor arms the vibration the flow the
whatever the lead indicators are and
that from what systems do we need to
bring them so that we can create these
rules either rule-based AI based
the person who's been there 30 years and
also applying some engineering
calculations because most of the things
that we do still subscribe to the lower
laws of physics so we can use our
engineering calculations to do that so
that's just some other approach that we
take but to get back to how the software
worked led me jump in and briefly show
you so I'm going to start with with
again with this picture we have the data
stream designer
um and that gives us the ability to
embed AI as well the visualization
aspect is in the app designer and we
have the recommendation manager that
drives it I will start with the app
designer number three the the user
interface so if you recall our
tip of the iceberg I'll start with the
application designer show you what the
what or how we
the the the the the the information the
interaction and then I'll show you
behind the scenes below the water line
how we do this
