Digital Twin Strategy To Execution Pyramid Xmpro Webinar

In this webinar on Digital Twins, we'll cover:

• How to create a digital twin strategy that spans across the different levels of your organization

• The timeframes that are typically associated...

Transcript

In this webinar on Digital Twins, we'll cover: - How to create a digital twin strategy that spans across the different levels of your organization - The timeframes that are typically associated... good morning good afternoon good evening

everyone and thank you for joining this

webinar on digital twin strategy

looking at our

strategy to execution

pyramid thank you for joining and let's

jump straight in

so i'm peter vance gothbeck um i'm the

ceo at xmpro and

my background in digital twin started

about six or seven years ago

uh leading the industry the digital twin

interoperability task group inside the

internet consortium

and for the last two years i have also

been involved with the digital twin

consortium where i lead the natural

resources working group

and with um cheyenne from

deloitte previously with ge i also

co-wrote a book on building industrial

digital twins how to design develop

deploy and

create these digital twin solutions um

for real world industries so i have some

background in this and i thought i'd

share some of the learns that we've had

over the last couple of years in terms

of how to connect all of this and create

a coherent strategy for digital twins in

the

organization before we start uh with the

strategy side i thought i'd take just a

few

minutes and make sure that we sort of

have the same understanding around what

we mean

around some of the digital twin

concepts and and definitions we are we

often find a lot of confusion around

what digital twins are this is my view

so

and that will be the context of the

presentation as well so

using the digital twin consortium's

definition it's a virtual representation

of real world entities which could be uh

which could mean a number of things and

a key aspect is that it is synchronized

at the same at this specified

frequency and fidelity

and it's underpinned by three

key

enablers for from a digital twin

perspective the first is that it is

there to support decision making

and understanding and driving effective

actions the second one is that it is

kind of built on real-time data

as well as historical data and the idea

is that it can represent

the past what's happening right now and

also simulate predicted futures

and it is all driven

and motivated motivated by outcomes

which links to our strategy discussion

of today

it is focused on use cases and again

i'll talk around how that fits into the

strategy side of things

and obviously this all needs data that

sits behind it and you need to know what

this thing needs to do so it requires

the main knowledge so those are the

three pillars

that that underpin the definition from a

digital twin perspective to put that

into a picture um

we have the physical environment over

here

the entity of sorts it could be any type

and on the side we have the virtual

representation

and there's data that is synchronized

and sometimes some of the information

can go back

um and we have um the twinning rate

which is that that synchronization and

this is physical to virtual connection

which could

um

go bi-directional or both ways

one of the key concepts of a digital

twin is that it's a model-driven

architecture

if you gonna go back to what uh what

from a software design pattern

it's actually that it's built that

digital twins are built to a model or a

prototype well but

dr greaves

initially turned a digital twin

prototype so

the prototype is the template we

sometimes talk around templates but

there's a certain template for a certain

class of asset

so for example

we have a a template for these pumps

and then

each of the individual pumps are then

instantiated so there's a

each of these use the same model

so those are referred to as the

instances or digital twin instances and

if you combine some of these things in

different way shapes or form either a

number of these together or different

ones of these but into a single thing a

a single higher level twin

these are referred to as the digital

twin aggregates or

quite often as composites as well

how this all fits together from a you

know when is it

or when does it get instantiated when

does it become a twin if you look at a

product life cycle again it's like at

that pump or a motor or

or any um

entity and this could be a supply chain

or something like that as well which

falls under more the process type of

digital twins but if we look at that

pump or motor

when we just have a model

we still don't have a twin we have got a

digital twin template or digital twin

prototype but it's not necessarily a

digital twin when the decision is made

to make that so i can get a serial

number or allocate some unique

identifier to it and then start

populating the the the model with some

real-time information around the either

design manufacturing or whatever it is

um that's when the twin is instantiated

and now i'm starting to create the

physical device

as you can see now we have an instance

and we have the type and as we start

putting this together in a larger either

system or system of systems even

we get to

and we start putting system context to

it

we start building out and start building

these aggregate or composite digital

twins and then when we put them into

operation we get real-time telemetry and

other data to it

this is when

we actually add another dimension

from a digital twin perspective

if we look at the factory however the

life cycle could look slightly different

and you know the the facility that we

created to do this so in that we may

plan design bolt commission operate

maintain and improve the facility there

are use cases across all of this but

those tend to be quite often project

driven and this is continuous and this

is where different

types of of twins fit in as well what we

refer to as simulation twins and

operational

twins

and i'll touch a little bit on the

difference on on some of those as well

but that's

more from a factory perspective how this

would fit in

another question that we all that we

quite often get is what's the difference

between a digital thread and a um

a digital twin and if we look at those

lifecycle phases depending on how you

represent them

um digital twin use cases typically span

across they address a specific problem

you're hiring the twin to do a specific

thing in that life cycle

and the digital thread is that digital

breadcrumb that builds up data across

all the different life cycles so

i can then track throughout the life

cycle

all the interactions

that the

product or or environment facility had

with different use cases and how that um

is built up to

again just very high level um if

anyone's interested we can we um we can

delve into this in a

in a later

webinar going more into some of these

concepts but that was just at a high

level the one last question that i often

get is so is a simulation model a

digital twin because we've spent a lot

of money on building these simulation

models using computational fluid

dynamics all of this and you know how

does this is this a digital twin

if you go back to the

the definition if it's not synchronized

at a certain frequency and fidelity it

is a digital twin prototype potentially

it is a model if it is synchronized then

it can become a digital to an instance i

can use the same

simulation model

in i can either connect it up

or not

and that would be the difference so

using or creating this digital twin

prototypes they are critical in design

but some of them may be thrown away or a

lot of them might be thrown away and

actually not used

in the

digital or the instantiation of digital

twins when i take that same

uh simulation model and i wire it up

with uh and synchronize it at a certain

frequency and fidelity

that's when it becomes a simulation twin

versus a simulation model so that's just

at a very high level some of the

concepts that we we refer to quite often

when we talk about digital twins but

let's change gears

and talk around the strategy side of

things so

refer to this as the digital twin

strategy to execution pyramid and again

we like to use

frameworks for this now what we've seen

over the last couple of years working

with the number of customers on this in

la in in in various industries and at

various

sizes of enterprises

if it's done right it is an accelerator

for digital transformation which is kind

of the holy grail that a lot of

organizations are trying to achieve with

this the important part is that um

that that

the the value levers um

should be connected almost as golden

threads from the top to the bottom of

the the

organization and that's what drives a

real change so what we

see

seeing is that

digital twins done right

actually focus on pulling the right

value levers

to get quick

time to to return on investment and that

will drive more adoption making sure it

is connected that golden shred

is a key aspect of being able to achieve

that and if you want to do this at scale

you want to do this in a short period of

time it does require a kind of a

composable or reuse of package

capabilities

and this is the topic of another webinar

but for today we'll focus on

how to create that digital twin strategy

that spans across the different levels

of your organization

so

at a strategic level

the digital twin provides a macro view

on on business metrics

it focuses

it differs from from

from kind of loose standing

dashboard kpis that we quite often see

because it's a more integrated view

that you can only achieve if you look at

it from a model driven approach if you

look at your business from a model

driven approach and quite often that

model

is the value chain across uh the

business so

it ideally models

the the business from my a macro view

and again it differs from from just

loose standing um dashboards that

managers use in the sense that uh

there's a thread which i'll which i'll

go into in a bit more detail at a

tactical level

the digital twin support

planning functions so and it is really

focused on removing constraints because

it's one of the challenges that we have

in organizations we have limited

resources

in different way shapes are formed and

what we need to do is make sure that in

order to get our strategic view

executed that we actually manage the

constraints that sit in the middle and

that's quite often a planning view

and then lastly we have

the task view which is at the

operational level we focus on how to get

things done

but again how it's connected now the

quite often the the time frames or the

application for this differ so when we

put a lens on we look at

at a strategic level

quite often it starts off with

annual quarterly or those kind of

reportings

even down to weekly and there are some

metrics which may be at a at a strategic

level important to understand on on a

daily basis

likewise with

tactical because it's so focused on on

um on constraints it looks at um

almost all four levels of time frame so

so that it can provide a holistic view

from a from a planning perspective and

at the task level

um quite often

it is it is predominantly focused on

what what's happening right now um so uh

by the hour

um with a view on daily and quite often

i need to also understand how this fits

or what i'll have you know what's

happening

from a weekly

time frame and how my actions uh impact

that

so that provides just a kind of a high

level

view in terms of the the the different

requirements um for this so

looking at it from a from a um

uh

you know what are you

we often refer to to digital twins are

hired to support different users at

different levels

and and

we typically see that those strategic

digital twins service executives and

senior management likewise with tactical

that supervisors engineers planners

those type of roles especially in asset

intensive industries and then

operators and technicians people who are

really focused on how do we make those

things happen right now at that

operational level

what i forgot to mention on the previous

slide it is focused on four

quite often in asset intensive

industries there's four four main mega

use case buckets

the first one is around business

performance

which

tend to be more strategic

and um and tactical

the the the process optimization is

quite a is

again it could be the physical process

like you have in a mine or it could be

the business process

optimization side esg monitoring

compliance we see so much more of that

emerging where digital tunes have a

significant role in making sure that we

are compliant in terms of

a number of the initiatives going on and

you know where this all started for a

lot of organizations was around asset

performance um use cases so these are

the kind of four main buckets that we

see

um where value levers are

identified from a digital twin

perspective

the uh

the interesting thing with uh

this pyramid or

it almost mimics how we synthesize and

get data so at operational data at the

operational level we have a lot of data

and a lot of data collection

and as we go up we start turning that

data into information

right to the top so

we're seeing that

the strategic side of the business

requires quite often less integration

points

but what we see is that this is that the

impact of the decisions made at that

level

it quite often has a bigger impact on

those levers and um the overall business

impact

and

uh as it goes down the the the impact of

the decisions get smaller doesn't mean

that the decisions are less important

it is just in terms of how it impacts

the overall outcome when you drive

towards

certain outcomes

it's interesting i i recently saw this

from a guard on a gartner report and

it's interesting to see how they

anticipate digital twins grow so

um what you see the light blue is 20 21

this is all where we are right now

discrete or single type iot based uh

digital twins seem to be predominant and

and as we

a digital twin of an organization is

more that business process level the

business level view and then the

composite is the ones that we start

putting together it's really interesting

to see how they anticipate that grow and

if we

align that with the different

use case types i think at the moment

we're seeing a lot more of these single

um let me monitor let me build something

around a specific asset but as this goes

forward um

there's a lot more awareness and

the value will be realized

on digital twins as we start building

out to more higher levels and and and

help drive better decisions

uh from a strategic point of view

so i thought that was just interesting

how that aligned with what we what we're

seeing

in terms of our experience and working

with customers in in different

specifically asset intensive industries

it's interesting to see how the ones who

do it well um how that is approached and

here's an example of on

looking at from a mining perspective so

um you know this

the strategic levers here that drives

the digital twins

a lot of mining organizations are now in

a position due to some the global

challenges

and supply chain problems and things

like that where they can literally sell

as much as they can dig out the ground

so

the value leaders are that obviously

safety and esg

are key focus areas for all of those

industries but inside that

right now a lot of organizations are

they

again they can sell as much as they can

dig out the ground so they need to run

more often as a as a key

value driver when you're running you

need to produce more tons

when you're recovering those stuns

that you've dug out the ground in the

chemical processes

you need to recover more

and in all of this how do we focus also

on making sure that we're not blowing

out our costs as we do this

and that is the the strategic view that

the senior executives want to have and

that's the role that they want to hire a

digital twin to give them decision

support on how we're doing around this

that's then broken down into

different initiatives so you know for

each of those you can identify a number

of different initiatives so in order to

run more we need to reduce the might the

the shutdown the duration that our

shutdowns take um and also

the um

can we get away with um less bi-weekly

or can we extend the periods or things

like that

um

same with running more product oh so

creating more products so how do we make

sure that the conveyors are more

operational how do we from a recovery

point of view from looking at our

chemical processes how can we optimize

that

um

lowering cost if we bring in digital

twins to help us with that

we all we should always look at

how does the thread for each of those go

from the top to the bottom so how do we

connect that runoff and strategic to the

tactical application and then the

operational side of it so that is

really the what i would call the magic

source

how this

digital

how you get from the top to the bottom

that it is always focused on driving

the key value levers

otherwise you are building or a digital

twins that one that won't drive roi

and

make sure that

to ask the question what are the

decision support or decision automation

requirements at each level of the

triangle for each of these initiatives

to make sure that we establish this

this

golden thread between the strategic and

the tactical side of it sometimes

and we've seen organizations just start

with at the operational level i want to

build this one thing

if you don't ask the question how does

it support a tactical of you in

organization how does it support the

strategy

those things don't tend to go anywhere

but practically how do you do this how

do you connect

so when you link this

this

garden thread through all of this what

we find very useful is if you use

diagrams like this in that where

you really start with for a specific use

case that specific initiative that we

identified so right at the top it is run

more often

and the the um specific um

uh objective that we're going to going

to try and do is reduce the shutdown

times on our plant

those metrics then go in the top and for

each of these you draw them down and

describe what it would look like what

the twin would be or what the

operational requirement would be

from a key metric perspective what are

the key metrics we're going to deliver

so if we understand what the metric is

we can then look at what analytic and

provided and then we can look at what

data do we need to drive that analytic

so again

if each of these we know what the

outcome is that we want to achieve what

is the thing we're going to measure at

each level

for that initiative

if we know that

that key metric we can then

understand what analytic will give that

to us what do we have to do to calculate

or work it out and then we will know

what data to so don't start with i've

got all this data what can i do with it

which is one of the biggest

bug bears that i have

with

how people often look at digital twins

so this framework is very simple but

very very effective to put on a screen

and work with the team to make sure now

what does it look like when you put some

of this into action so

again those metrics um

at each level so whether you're going

from the overall site to the to

a plant level right down to equipment

level how does it break

down and how does it fit

together to to provide that gold that

golden thread view going through

the best way to describe this is just to

also show you a few examples of this so

if we look at that strategic event board

and here's an example of a value chain

the the

data and values have been anonymized so

that

we don't

show any any

customers real data but

this is a whole value chain for a senior

executive team that and the senior

executives

objective of this is i have a lot to do

when i get into the office um today

let me have a look at this and

understand where i need to apply my

energy now how does this differ from a

normal dashboard that you would have

well

there's real-time monitoring on these

kpis

and as soon as one of those kpis

fall out of a certain range or business

rule that you've defined which in this

instance we present as an executive

recommendation

then it would immediately

notify them and it could be an esg event

it could be a

breach on there could be a safety event

or anything like that but

from an operational perspective based on

those drivers that we've defined when

one of those things go out of spec i

need to know that i need to do something

right now

and again this is a holistic picture

this is a business this is a model of my

overall business it mimics my business

model as well

in terms of

how we want to sell and what we want to

drive so

key difference seems very simple

but i can see key metrics

updated in real time but also we can

make also make sure that we have

consistent

um responses and right and that we

respond to

to certain events that happen so that

when one mind has a problem or

that the other one

or if two minds have the same problem

that we don't have two different

responses in terms of the senior

executive how that is done we can set up

these executive recommendations review

them and make sure that we've got

consistent responses to these

so so that's just one another one would

be

um and this is in a gold mining

operation again i can see some of some

of the metrics that i'm looking at from

a monthly quarterly

and that kind of view but i can also see

what's happening right now with for

example my crushes

so um

and i know that if the crashes are out

i'm gonna have a bottleneck downstream

um i'll have to throttle back production

that's gonna have an impact i need to

intervene that's kind of

as a as a mind manager the only thing i

need to know today otherwise i'm going

to address some some of the other

challenges that i might have and again

you could see things like tailings and

some of the other key esg metrics on

there

likewise with

greenhouse emissions there's a lot more

pressure on executives to start

monitoring that in real time so how do i

make this part of

my business model or my model that i

need to look at

so some of it again is around quarter to

date here to date important things but

also what's happening right now where

are we with some safety

incidents that were reported over the

last um because i've got a couple of

thousand people um need to monitor this

and report on this so these are all key

things and it doesn't always have to be

that high fidelity um you know when you

look at this and again this is around

what's my real-time site kpis i know how

good are we doing around

the four things that i get compensated

on as or

incentivized on us the gm of a mine

and what does it break down into the

detail and again it's all about these

executive recommendations that when

things go then when we have a challenge

that we know exactly how um to respond

when we look at it from a tactical uh

perspective and and the connection

between all of this so there's a user

story for for the executives here

likewise with at a tactical level um

and i'll start with kind of a user story

at a tactical level so bruce dundee good

australian

um

is um

his role as a maintenance planner so he

slots into that tactical level

you can see the things that really

challenge him is that we understaffed um

we have if we make the wrong decisions

it's got a really big impact

i want to get promoted so these are the

personal drivers of that person which we

have to take in consideration when we

look at digital twins

and so based on his background there are

certain user stories and if you take the

crusher example that we saw you know

there was a

a problem with that red dot there was a

a crusher problem

i want to know when um

you know what the remaining use for life

is i've got six crushes if two of them

are out for maintenance at the same time

i've got a really big problem we have to

shut we have to throttle back production

so as a maintenance planner i want to

know when multiple crashes who

need over uh overlapping maintenance so

that i can tell the control room

actually to run these things faster

and i want to be able to simulate so

you'll see some of this so when we get

to the example but this is

the

important aspects of connecting um

the different levels so again

uber for his role around crashes there

are 12 different

things that need to be addressed or

questions that he's got to know now what

is the frequency of recent liner

changeouts so that you know how can we

improve the lifespan of of liners here's

the digital twin that represents that so

you could see you wanted to know if

there are two crashes out at the same

time um i need to know and so we need to

predict the line aware and there might

be some predictive analytics involved in

that in an actual fact there is in this

instance there's some predictive

analytics to predict the way on the

liner and if two of them overlap we need

to know in advance so we can tell the

control room to do something so again

this is giving them recommendations on

how to do that some of them are

overlapping some of them are doing other

things

and for each crusher you can then see

what is remaining in the predicted end

of life six days so if i go into that

crash and this is all driving lower cost

you know as a value lever

getting into that crusher here's the

actual crusher i can see how it's

running

i can see all the work orders and

everything associated this is what i

need from a planning perspective so that

i can do a next level

tell the operations how to operate this

um so

the interesting thing is i can also see

how this compares to

the previous liners that we've had in

this same crusher

so that's all important information from

a decision support perspective and again

this is all driving towards that

lowering the cost

um one of the other areas um you know

that will give you a health of how

my operational planning work not in my

maintenance planning but my operational

planning is

quite often if you've got multiple

control rooms and we look at the

the percentage time in normal ptin

how that looks and i can i compare

across multiple control rooms

and see how we are doing and so that we

know again from a planning perspective

and intervention perspective so this is

going down into a specific control room

looking at

the alarms

and percentage of time that that things

are running autonomously versus we have

to have manual intervention again that

gives us an understanding of the health

and

how i can plan around that what i need

to do and which

which areas are better than others in

terms of the performance of an

organization so that gives you kind of

an overview of the control room

side of things and then if i drill down

again on a specific area inside that you

know how how how good are we again at

certain of these so

um from a planning perspective

understanding which control loops and

things um are giving us the most issues

so we can plan those so that's kind of

the view the role

and i can do this across

all my different

control rooms on exactly the same model

so my digital twin is the model i then

instantiate it for the different control

rooms

at the operational level um

and again just going back to

the biggest challenge that we start

seeing here is how do we connect all of

this data this is where there's quite

often a lot of data complex

uh connections multiple systems cleaning

up the data wrangling doing all of that

and that

it's a key

most characteristic is

the

the data that goes to the top

quite often needs to go through these

processes in order to clean that up so

that i can drive the decisions that i'm

making right now

so this is looking at my operations view

process optimization i'm looking at

the different circuits here

on the crushing side for example right

now what's happening

on my

plant right now

and where are the potential bottlenecks

or where do we need to intervene

from that perspective

and again

just a

a similar view to that but focusing on

what's happening right now

and what can we do

to address some of those again all

recommendations or you can call them

alerts it could be recommendations

but that's how we drive it

again at the operational level i need to

know which bumps are giving me issues

right now you know what's happening on

my plant if there's nothing wrong with

this then no dot if there's something um

based and this could be

every second every millisecond even that

this analytic needs to that the digital

tool needs to analyze based on an

engineering model it could be that

simulation

model or

machine learning or just a rule and then

based on that great recommendations for

me so i can go into that piece of

equipment look at it and analyze it

further in terms of what i need to do

right now potentially from a maintenance

perspective

so those are the kind of three levels

and just to recap from a summary

perspective it is important to

strategically align your digital twins

with those value levers and establish

that golden thread so also make sure

that it drives what the business

outcomes are

and set the kpis at each level

and connect them through through them a

diagram as i showed earlier

that you make sure that you've got the

that each of those threads have their

kpis and how it would be measured um and

how it actually

delivers value so how do i connect all

this data and drive the better decisions

from the top

so

that is how we see the strategy pyramid

when we present this

it gives organizations kind of a clear

way of thinking about how do we do how

do we

um

not just have all these loose standing

digital twins that emerge almost like

spreadsheets in a business but how do we

make sure that we've got a strategy to

connect all of this together and drive

the business outcomes that we have

just uh

very briefly so what is xmpro and how do

we build digital twins we see ourselves

as an application composition platform

that provides a no code environment for

industrial business technologists aka

engineers and people like that

to create real-time event intelligence

apps composable digital twins and

industrial iot analytics solutions and

our focus is how can we help you with

business

capabilities to be able to create safer

greener and more responsible

industrial operations do it faster

and with higher efficiencies that make

it worthwhile for everyone that drives

value levers for everyone in the

ecosystem

quite often asked how do we fit into

this whole industrial ecosystem and

there are a number of players we see

ourselves as that composition platform

bringing things together connecting them

up composing

digital twins to be able to drive um

outcomes from from this

and then how do we do this well

um

we handle the data integration at that

at the bottom level

being able to aggregate it up

we

provide the

the user front-end so to enable that

decision support and also how do we set

up that recommendation structure and how

about let our digital twins become our

trusted advisor with recommended actions

when we have certain things at all

levels of the organization

so that's me

if you need to get if you want to get

hold of me

there's my email address

and what i'll do is open it up for

questions to see if there are any

questions

that people may have so

um

and uh so there's one from

antonia

i'm just going to break this out so that

i can read it better

apologies

excellent so if anyone's got any

questions just post them into the chat

they will go with questions for a while

and then

we'll wrap it up so the question is

given the supply chain challenges

companies are facing today is it

possible to build an operational digital

twin um of the end-to-end supply chain

yes in actual fact it's one of the areas

that we see

almost at that composite level one of

the biggest growth areas especially if

you've got multiple nodes so

we've as exxonpro we've been involved

with quite a large scale

digital twin of a supply chain for a

industrial manufacturer

who support i think

i have to check it's either

120 000 skus across

including in customers i think there are

almost a million nodes in the supply

chain

and it's all about short-term inventory

planning which is the biggest challenge

that they have

in terms of

operating across a large geographic

region

in europe for example and this is also

running in different areas but if they

take europe as a as a

uh example creating a digital twin

and it's all about in the end

the operational how do we move

how do we make decisions right now in

terms of where to make certain products

in a certain facility they've got a lot

of distributed facilities

almost micro factories

across a large area of europe and making

decisions around where certain orders

are made for fold how do we change

that that continuous mrp

is driven by the supply chain and having

that as a model driven based approach is

the only way that you can manage that at

scale

and again we know the biggest challenges

that they've had is a manager in a

certain region at a certain many factory

would make a certain decision on the

let me rephrase that slightly based on

the same data

they would get different outcomes

in different regions by different people

on the decision to be made around you

know do we make it here do we send it

across

um

to someone else do we do we change out a

product do we offer a customer something

else

that kind of thing

using this recommendation getting the

digital twin to provide that consistency

it's had a significant impact

for them

and in terms of the scale this is the

only way

they could scale that out and the

benefits of what we're seeing right now

with supply chains

being able to run that optimization

logic every 15 minutes

so that the digital twin can come up

with

new

new

suggestions

uh has proved invaluable from that

perspective

and then there's a follow-up

um so in these cases

thanks antonio follow-up question do

these cases install sensors on the

supplier facilities

yes

so what's interesting is there's

actually a mix so on there on their

larger customers they actually have

sensors

on let's call them tanks just for lack

of a better i don't want to go into

exactly

what

what the products are that they are

selling but

let's say there are facilities or like

tanks in

at the end customer

organizations

they well let me explain it like this

they actually supply

industrial gases

so it's all about filling cylinders and

doing

all of that and

for some critical infrastructure like

hospitals and others like that

they would have sensors installed at the

at the customer facility so that would

give them some real-time intelligence

the other part of the intelligence are

orders that are in the system that the

customers are placing like i need these

replenished or whatever but

um they are driving for large critical

infrastructure customers that they

actually have direct insight and connect

connectivity into those facilities

and know what the levels are um so that

i can supply them better and that's all

in the

um

supply chain

um there's a question from young on do

you have a inexperience of applying this

in health care in

personal care

also in personalized personalization of

care one of the things i didn't mention

on here

is that

before um

that diagram from gartner with the

discrete iot organization there's

actually digital to another person in

there i didn't include it in here

because of the focus was a little bit

more around some of the industrial

examples

but digital twin of a person um which

are

which

and a lot of the use cases are around um

personal health care the biggest

challenge with that right now

is yes we can do this

the privacy challenges right now is

still being worked through

um there's interesting use cases where

the same analogy applies so you have

certain

value drivers and those value drivers

might come from institutions like

governments local governments uh things

like that

in terms of what they want to drive from

from outcomes or delivering better

service how that needs to go into

planning and how that then gets executed

the challenge with healthcare

specifically

is um

[Music]

at the moment is there's still a lot of

concern from a privacy so if there's a

digital twin of me where is it going to

end up you know who's going to have

access but in terms of the use cases and

the application it is exactly the same

process and

and model area where we are seeing some

of the um

personalizer or the digital twin of the

of the person being used are in

crm and retail and you'd be

it's actually

really interesting to see what's

happening in the retail space around

creating digital twins

of

or taking the first the

the end consumer um digital twin and

overlaying that with

infrastructure and different so looking

at a shopping mall how people move where

the feet are and how can we sell

more retail space to larger retailers by

showing them what the

some of the behavior and characteristic

of people underneath that is so those

are some of the again the value drivers

are we want to sell

um we want to charge premium for for for

certain areas in our

shopping malls and then how does that

relate down all the way to the

operational site where we collect data

but also give people things back

i'm just mindful of time but i'll

quickly answer

there's another question can you

simulate a condition of a digital to an

instance with corresponding

so yes so

the question is can you simulate a

condition of a digital twin instance

while corresponding physical entity has

never experienced such conditions it's

one of the capabilities that we refer to

as synthetic data so how can you create

synthetic data that you could

potentially use so it could be modeled

based on

on existing data plus some exceptions

that you want to introduce to that um so

there's a whole area around synthetic

data and yes um

it is a it is one of the capabilities

which we'll touch on in another webinar

um of that you could potentially build

into a digital twin um absolutely so

thank you for for that question uh

and then last question

um opc

uh integra opc ua integration yes we've

got standard connectors for opc um in

actual fact we work pretty closely with

the microsoft team

who's out of germany who's very involved

eric barnstein and the team there

um on the opc side of things with yes we

have got opc connector like we have

mqtt connector like we have uh osisoft

or adx or

azure data explorer so

that's one of the things from my

integration point of view is we've got a

large library of integrations at that

lower operational level but yeah

and

so yes for ot factories um

opc and we see that as a

and

and

uh

did if i see that you know um eric so

say hi to him when when you're next in

excellent

thank you very much really appreciate

your time great questions

and looking forward to seeing you on one

of our next future ones thank you