STEPHAN DE BARSE: Hi, everyone.
This is Stephan De Barse with o9 Solutions.
At o9, we are digitally transforming how businesses
plan and operate today.
I'm based out of Amsterdam.
I'm running o9 in Europe, as well as
our global go-to-market practice.
Today, we're going to talk about AI-powered integrated business
spending on Google Cloud.
What is the agenda for today?
We'll start with a quick introduction into o9 Solutions,
followed by the relevance of planning in today's world,
where we'll share a couple of learnings from the pandemic.
Then we will talk about the architecture
of what we call the Future Ready Enterprise, followed
by a couple of use cases in retail and manufacturing.
Then we'll close out with the key contacts,
so that you can engage with us going forward.
What is o9 all about?
o9 is a premier AI platform for integrated business planning
and digital operations.
We support companies in getting to a more accurate forecast,
a faster response, and end-to-end visibility.
The o9 platform is designed for Fortune 500 companies.
We're working with leaders in every single industry.
In retail, think about Walmart and Starbucks,
where we help with forecasting replenishment and integrated
flow planning.
In consumer products companies like Nestlé and AB InBev,
as well as Estée Lauder.
We support those companies in demand forecasting,
supply planning, and integrated business planning.
Over the last few years, the o9 platform
has been adopted by many other leaders
in different industries, like Exxon Mobil in oil and gas,
and T-Mobile in telecom.
Last but not least, Google is an o9 client.
We are supporting Google consumer hardware
in better predicting demand, as well
as doing the supply planning and integrated business planning.
Why are so many of those Fortune 500 companies selecting o9?
That is because we have developed this next-generation
planning platform that is able to cope
with the complexity of organizations,
as well as using that technology to help them better planning.
Then we have designed a UI and UX that, from an enterprise
point of view, feels like your Google
device or your Google applications,
and hence, it is architected to drive user adoption.
Lastly, time to value is extremely important.
Our implementations are short or fast and full
of very agile implementation methodology.
What I want to do with you next is share
a couple learnings from this pandemic.
What have large retailers and large manufacturers experienced
throughout the past couple of months?
First, demand variability.
It's all about, can I detect better
the risks and opportunities on the demand side?
What we have seen is those products mix shifts, right?
An increase in essential products,
but a decrease in others.
Channel shifts, right?
Overnight, we went from the traditional brick and mortar
to online.
We've also seen regional demand variability,
with lockdown and reopening schedules varying by country,
by city, in some cases by zip code.
So the question is, many organizations,
how do you now predict demand when all of this
happens in a very short amount of time?
That is a challenge for many, and that
is something we support global companies with.
Second is on the supply side.
Again, it's all about early detection.
Many companies today do not have the visibility
or the capacities, material availability, and lead times
of their suppliers, goal [? backers, ?]
or contract manufacturers.
What we have observed is that supplies and suppliers are
slowing down or stopping production,
but some are coming back online.
So how do I know on which one I can rely, and when?
Right now, what we have seen is that inventory and capacity
visibility across a network is challenging.
There are multiple ERPs, multiple systems,
and they don't have that visibility quickly enough.
And then lastly, global companies struggle
which visibility into their supplier network.
So very often, they do not know what suppliers can commit,
and what their capacities and material availabilities are.
Third is integrated planning and response.
How fast is an organization able to run scenarios?
Demand can be 90, demand can be 100, demand can be 110.
Well, what is my supply chain response?
What is my true cost to serve?
And do I understand the different assumptions
that go into those different scenarios?
Companies today lack the ability to run fast scenario planning,
understanding all the different tradeoffs,
including the financial ones.
Again, that is something where we provide support.
The other two key learnings is that enterprise collaboration
goes digital.
What we have seen is that whether companies
have production planners or supply planners
or demand planners or commercial planners, overnight,
all of those functions had to work from home.
And what we've seen is that that boosted
online and digital collaboration,
but also showed the possibilities of looking
into more centralized and more specialized COEs
that companies can deploy.
Lastly, what we've seen is digital operating models
for structural transformation.
CXOs that want structural improvement
involve resilience and cost.
That means that this whole collaboration
doesn't stop within the four walls of the enterprise.
The question is, how can we go beyond that
and start connecting with suppliers and customers
in more real time, use that knowledge to then shape demand,
and better collaborate with suppliers and contract
manufacturers.
So we have observed a couple of key trends, a couple
of key learnings, and we have definitely
seen the need for global companies
to digitally transform how they do and execute integrated
business planning today.
Now, what I want to do next is introduce you
to the o9 platform, and I would like
to do that by using an example from Google Maps.
In this example, Google is able to tell us, hey, leave by 8:45
to arrive at the airport one and a half hours
before your flight.
Now, we all take this kind of technology for granted,
but to me, it's a beautiful example
of how we have moved from sort of traditional data and data
mining to prescriptive decision making.
Now, let's take this example to the enterprise.
Assume that we could sense that demand is going up.
What do I instantly want to understand?
Can I satisfy that demand, yes or no?
If not, then I want to understand why not.
Where are my constraints?
Is it a factory constraint, a material constraint,
a logistics constraint?
But it doesn't need to stop there.
What I truly want is a prescriptive recommendation,
like Google Maps is giving us here, and saying,
hey, I found out that we have this logistics
constraint, but no worries--
we can set up an expedited lane so you
can fulfill the demand on time.
Oh, and by the way, the additional cost
of that expedite are X. And that is the notion
of digital transformation in the planning
space in the enterprise.
Now, what is actually behind this example
that Google Maps is using?
Three core capabilities that I will link to the enterprise
and to the o9 platform in a second.
In the middle, you find something
that we call the AI-powered knowledge model.
So in the Google Maps example, there is network master data.
Think about the map with the roads
and the traffic lights and the speed limits.
That is obviously connected with the big data store.
What do we find in the big data store?
Location tracking, calendar information,
and the flight status.
All those data elements help us spotting risks
and opportunities much earlier.
So now, when you combine network master data with big data
and you run smart algorithms, now on your mobile device,
you can now receive prescriptive actions with predictive alerts.
And again, that is where we need to take enterprise decision
making, so that that becomes as easy and as prescriptive
as this example with Google Maps.
So at o9, we developed the following platform
that allows us in the transformation
to prescriptive decision making.
What you can see in the middle is
what we call an enterprise knowledge
graph, the digital brain of the enterprise.
That is allowing us to create a full digital representation
of the enterprise.
From the supply chain, the supplier nodes, the material
hubs, the factories to the shippers,
connecting that with the different demand
nodes, the products, the initiatives,
the sales accounts-- all the way to the go-to-market channels,
whether it's selling through retail, business
to business, or to stores or online,
ultimately all the way down to the end consumer,
where we can also bring in data from the competitor.
So that entire knowledge brain is then
connected with various data sensors.
What kind of data sensors?
Think about your ERP, where we connect with master
data and transactional data.
But more importantly, we want to connect
with data that gives us insights in risks and opportunities,
so we can connect with external data sources,
both structured and unstructured from nature.
Can I give a couple of examples?
Absolutely.
Think about a company like AB InBev, the largest beer
company in the world.
They're selling through retail, they're
selling online, and through distribution.
What is driving their demand?
Think about weather.
Think about competitive pricing.
Think about competitive promotions, their own trade
promotions, their marketing initiatives.
So what if we could collect all the data
and use that more intelligently to forecast the demand?
Other example-- Starbucks.
What is driving their demand?
Think about local events--
a football game, or maybe a graduation event,
when a Starbucks store is located on a university campus.
Again, weather is also a big influencer
in terms of what's driving demand at Starbucks.
So again, it's about capturing that information
and bringing that in.
Is that only happening with consumer products?
Not really.
Think about companies like Pirelli Tires or Bridgestone
Tires.
They're selling tires to dealers and OEMs.
What is driving their demand?
That could be, you know, the number of cars
being produced by the OEMs.
Can we incorporate that kind of data?
And again, the answer is yes.
So the entire notion is, we are looking out for all those data
sources that can either predict a demand risk
or opportunity or a supply risk and opportunity,
and integrate that with the digital brain.
Then on top of the digital brain is
where we expose different planning workflows.
On the far left hand side, it's all about joint customer
collaboration, as well as better connecting with my account
sales teams, category management, and product
portfolio management.
Then when I jump into the enterprise again,
that is where my commercial teams are connected
with my customers, and that is where we need to support them
with annual operating planning, initiative planning, demand
planning, gap closure planning, and all
that combined with analytics, because planning
and analytics go hand in hand.
On the same platform, that is where you
can do supply chain planning.
We are creating a full digital twin of the supply chain,
providing end-to-end visibility and allowing
for smart and intelligent demand supply matching, control tower
capabilities, and scenario planning.
Obviously we want to connect that
with suppliers and contract manufacturers,
as well as with all your factories and logistics
operators.
Then, similar to the commercial side, planning and analytics
go hand in hand, which means I can run analytics on my cost
to serve, inventory, different planning policies,
and so forth.
So all of this is then coming together
in the sales and operations planning.
No more meetings and PowerPoint.
It's fact-based decisions live in the platform
where, again, the system will prescribe
what actions to take based on running
the different scenarios.
Now, all of this is obviously across time horizons,
so from the daily planning--
you know, the next four weeks out--
all the way to the long range planning, the next five years
out.
And that is a true differentiator
of the o9 platform.
On top of that, the entire architecture
has been built to be open source, which
means that we can integrate with R, Python, Gurobi,
and the likes so that any development that you have
made in data science already can be leveraged in o9 platform.
What I want to do next is summarize
what we have discussed.
So in o9, it all comes together in this enterprise knowledge
graph.
You should truly see this as the intelligent brain
of your enterprise that is connected, on the left hand
side, with your transactional backbone.
We connect with your [? UP ?] system, your CRM systems,
your PLM systems, and so forth.
But also, we have this integration framework
that I just mentioned with R, Python, Gurobi, and the links.
And then obviously, at large scale organizations like
Walmart or like Nestlé or like AB InBev,
there is a need for being future proof in terms of having big
data capabilities, and that is where we are integrated with
Hadoop and Hive.
On the right hand side, you see that is where all
those different data sources--
internal and external, structured and unstructured
data--
are coming into the brain, where o9 takes care of data cleansing
and data harmonization.
Ultimately, we offer [INAUDIBLE] best in class
UI to tablet browser mobile to the different users
in the enterprise.
Now, obviously, o9 9 is born in the cloud
and natively deployed on Google Cloud,
and that is also how we can scale
for and with our global clients.
Next are a couple of examples.
Where has o9 been implemented, and what is
the value that we are logging?
Now, first and foremost, Google consumer hardware.
What happened, actually, prior to o9?
Actually, a legacy system in Excel.
What we identified was the supply chain cost and inventory
were quite high, as well as there
were complexities in managing the end-to-end supply chain.
Now, with the o9 platform, Google consumer hardware
is able to make better decisions to reduce inventory
and to reduce non-standard supply chain
costs, such as flex capacity expedites and so forth.
At Estée Lauder, we are replacing SAP APO at a global
scale for demand planning, for supply planning,
for integrated business planning.
One of the unique issues at Estée is that their product
portfolio changes very rapidly, and hence,
there is a need to better plan for new product introductions,
but also to link that to the supply chain to minimize
solvence risks for products that you're going to take out with
the shelves.
Two other examples would be in the retail space--
Starbucks and Walmart.
What are we replacing at Starbucks?
JDA plus lot of spreadsheets.
What was really the issue at Starbucks
is that the barista in the store is spending way too much time
on inventory, on admin work, on ordering,
and Starbucks wants that barista to spend time with the client.
The other issue is that there is a lot
of demand variability driven by weather, local events,
and so forth.
So what we did, we actually built an AI-powered forecasting
model, taking into consideration weather data, promotional data,
external events data, and predicting the demand
at [? SKU-store ?] level, while at the same time linking that
in an automated fashion to the replenishment so that
the barista can spend time on serving your and me,
rather than doing ordering and inventory and so forth.
At Walmart, we are deploying a solution for integrated flow
planning.
In other words, how do I take my products from my inbound DCs
to the shelf in the most efficient and optimal way,
taking into consideration all the different constraints,
the cost, and the complexities of a network that consists
of a million SKUs, four and a half thousand stores,
and 200-plus DCs.
Prior to o9, Walmart was not able to run a full constraint
plan, which with o9, they are, as well as they
can run different scenarios to understand tradeoffs
between improving on-shelf availability and cost.
So what is the impact of o9?
Let me give you two examples.
At a large retailer where we implemented the o9 platform,
we were able to improve the forecast accuracy,
improve the service level, and in reality,
reduce expediting cost for both labor and transportation.
Another example would be a large industrial manufacturing
company, where we implement demand, supply, and inventory.
And we've seen tremendous improvements
in forecast accuracy, inventory reduction,
as well as a significant improvement in service level
and a reduction in expediting and transportation costs.
So what we believe is that for every billion dollars in sales,
there is a $10 to 20 million value opportunity
when companies decide to implement o9.
Now, if you want to hear more about o9,
about some use cases, or about anything
else around our platform you can contact me directly
or our Google client account lead, Simon Baksys.
Thank you very much, and wishing you a wonderful day.