Roadmap to an Autonomous Aftermarket Blueprint for OEMs
Watch Sushana Taunk, Senior Product Manager at Entytle, as she delves into the transformative strategies for the autonomous aftermarket industry, Sushana offers invaluable insights into navigating the evolving landscape of the industry. Key Takeaways: Understand the critical elements for building a successful autonomous aftermarket using AI-driven solutions. Learn how embracing technological advancements can keep you ahead in a competitive market. Discover how to unlock and leverage the potential of your existing data for future success. Discover how Entytle can help your business transition to an autonomous, intelligent, and efficient aftermarket operation. #industrial #IndustrialOEM #manufacturing
You Might Also Like
Good morning, good afternoon and very good evening to those of you joining in
from various parts of the world.
This is J.W. Simmons, the Ego to Market Manager with Entitle.
And with that, I'll go ahead and introduce our speaker of the day, Sushana Tonk
Sushana works in Entitle as a senior product manager. She has over 10 years of
work experience in the product function
and is a blend of domain knowledge and customer advocacy.
She's passionate about building products that make our customers efficient in
their daily work lives. Sushana, over to you.
Thank you, J.W. Hi everyone. Welcome to the Entitle webinar for autonomous
aftermarket series two.
Let me take you through.
So, if you all, some of you all would have attended our CEO of Aix,
previous webinar about autonomous aftermarket where this guy was pretty
familiar and famous.
Who is he? Well, this is John Doe, a typical manufacturing world persona.
A 40 year company veteran who's the keeper of knowledge,
probably because they've been with the industry for so long.
Unfortunately, they're retiring next month.
Now, this is the real state of the affairs today in the manufacturing industry.
I recall a time when a couple of weeks ago, our CEO had a meeting with a
prospect and the prospect was talking about a situation where they had a
meeting with one of their colleagues.
This colleague has been with their company for over 30 years and they were in a
discussion talking about some information that the colleague had stored in a
document, stored it somewhere in some file back in 2005.
And it became of relevance today.
Interestingly, this colleague of theirs was actually retiring in a couple of
weeks.
And with that, this information would have been lost.
Now, this is the truth. This is the reality that has been happening.
If we look at the data and statistics, we're looking at the average age of a US
manufacturing worker to be above 44 years.
If we want to look at what percentage of aging workforce we have above the age
of 55, that's about 25%.
Now, when people retire, the knowledge that they have goes out with them.
What poses is the big risk here is the new blood of the hires that are coming
in.
They're coming at a much slower rate than the one that's going out, which
brings in shortage of skilled capabilities for everyone.
Now, besides this fact of aging population of workers and employees in the
industry, another big issue that the OEM industry is facing today is that of
the data mess.
I'm not able to see any of you all do this, but I'm sure most of you are in
fact nodding in agreement here.
The data mess is real.
With any data driven decision making, you need to start with clean, good data.
Now, with the diverse kind of sources that we have for data today, CRMs, FSM,
ERBs, and let's not forget a large percentage of the OEM industry is still
living in the actual spreadsheet apocalypse apocalypse.
Now, when that happens, you have information that's sitting in various sources.
It's probably duplicate. It's probably stale by that time.
It's difficult to understand what is a single source of truth here. You don't
have unified trusted data sources.
Due to that, you're not able to get insights, especially at scale, which in
turn leads to not being able to share those valuable insights with the right
people at the right time.
Now, if we had to look at it, what is the single source of truth in all of this
That's the big question.
OEMs really need to be able to see everything in order to sell everything.
This is where the data mess becomes a big limitation besides the retiring
workforce.
Now, this is the present state.
Your workforce is retiring. The data is a mess in all of this. How are we to
automate that generational knowledge and wisdom that we have? How are we to tackle all of that messy, install based data to be able to get
actionable insights from that?
These are two questions that are posing right in front of us today.
Now, before we move forward, how would I ask the audiences on call today?
Take a guess. What are the biggest challenges in automating your aftermarket
business for you today?
What do you think most resonates with you in terms of the issues that you see
in your aftermarket businesses?
There should be a poll sitting in your screens right now.
I'd love to see what your responses are.
As the responses keep coming in, no surprises.
We all agree that yes, aging workforce and data mess.
A large percentage of you are actually voting for data mess more.
These are two big challenges that are present in the autonomous aftermarket
world.
If we can move on from the poll now, thank you everyone for responding. This is
really helpful.
If you think there are any other questions or any other challenges that you
faced, feel free to share them over the chat also.
We just spoke about the present, which is the data mess and an aging workforce.
What is typically the future of aftermarket here?
Well, the future is autonomous aftermarket.
If I had to put it into simpler words and explain that better, we're looking at
a future that is automatic, intelligent and independent.
It's not really unheard of where systems automatically talk to each other. They
're powered by artificial intelligence.
They're operating independently without any human intervention.
That is the future. We've seen all of this happen in movies, but that's just
not the case anymore.
It's actually happening in the real world and this is where the manufacturing
industry is also heading.
We are heading into a world of automating things. That's an intelligent manner
to make things happen independently, aren't we?
These are the three pillars that will help that will have to be the ultimate
goal.
If we as the OEM industry want to achieve the vision of autonomous aftermarket.
Now, you're asking me how do we achieve this?
Why and how should we get towards autonomous aftermarket continuum?
Now, at entitled, we believe this is a four stage process for you.
Stage one being where all the decisions are made by human beings, where human
led decisions and human led actions.
Tools, no software tools involved. This is a scenario where it's a simple
example of, should I be calling gendo at Acme Manufacturing to check with them
if they need part XYZ in the next three months?
This is the stage where most of us are sitting at the moment.
The next stage is where we're automating all of these road tasks.
It's where this kind of automation of business workflows comes into play.
Why should I even call gendo? Why not just send out an email that email should
have the details about the paths, etc.
If I need to do that, what are the kind of workflows that I need to set it up?
And what kind of data would I need to assemble to make sure these workflows
work well and I'm able to automate these.
The next stage is that of self learning and automating decisions.
This is where your DS models, your data science models would need to better
understand your business behaviors, whether they are your descriptive analytics
, prescriptive analytics, predictive analytics, all of that comes into picture.
And then a lot of the stuff that Entite will build today is really revolving
around all of these supervised learning models here.
That's what Entite will bring them.
If you've reached stage three, the last stage is going to be the next logical
step for you.
You are self optimizing everything here.
This is where your AI models come into picture.
The automated feedback loop comes into picture.
This is where you are training the tools, your algorithms to learn what's
working to repeat what's working more and more so and unlearn what's not
working.
There's a lot of learning, relearning and unlearning going on and that happens
in a loop.
So continuously optimizing things to fine tune your algorithms and truly
achieve the autonomous aftermarket value here.
Now, this is where we stand. This is Entite's vision of how autonomous after
market can become a reality for you.
Let me show you some examples in the form of use cases of how you would be able
to tackle situations if you had autonomous aftermarket as a reality for you.
Now, let's take a look at the simplest, biggest thing because data is of utmost
importance in our industry.
Before and today, what we're looking at is managing data sources from various
systems,
leading to duplicacy, leading to redundant data, leading to especially stale
data.
You are probably accessing the information in a system A, but the action you
need to take upon that information has to happen in another different system.
These two systems, you will need to make sure to talk to each other because of
maintenance of all of these various tools and platforms you're spending more.
Let's say in a scenario where you're definitely having people who go out into
the fields with handouts, printouts or details of the customers of assets that
they're going to look at.
Now, that is a typical scenario in the industry today.
In a world of autonomous aftermarket, what would be the reality for you?
You're looking at data collection happening intelligently and it's getting
automatically cleaned.
You're looking at data points made available automatically to you at different
workflows.
There's cost effectiveness.
You're looking at information that is the latest real-time information at all
times and it's all consolidated.
You're looking at a whole unified 360 degree view of your customer that is
automatically presented to you after the consolidation of information from all
of the various sources that you have.
What's best?
You won't even need people in your field to actually use handouts because they
have their installed based data in their pockets, in their hands on the go, in
the mobile app.
Any changes that they do need to make would directly go in that.
Earlier, they would have to go with handouts, write them in notes, PDFs, come
back, give that detail up, that gets updated, but with the autonomous after
market becoming a reality and single source of truth being present.
All of those redundant activities would be removed.
Here's a sneak peek of how we've been able to give that to our customer that
entitled.
This is a view of something that we call the location 360.
This is the consolidated view of a single customer, if I can say so.
Pulled together from all the various sources of information and removing duplic
acies, ensuring that it has completely clean, dedupped, giving you the latest
and the greatest information.
You're looking at customer information in terms of the equipment, parts,
service, contracts, warranties, even all the transaction history.
Is there any attachment that somebody wanted to report around this particular
location that's available?
Your parts revenue trends, your service revenue, your last transaction details,
your important contacts at this location.
All of those can be drilled down.
You're also able to look at whether this location has a high propensity to buy,
meaning is this location really likely to buy from you in the near future.
All of that comes in because it brings you the single source of truth.
Let's jump into the next use case here now.
In an aftermarket, in an autonomous aftermarket world, how could we achieve
independent and actionable automation?
Previously, all your reporting, numerous dashboards that you have to identify
which customers are drifting based on various factors like revenue age, etc.
All of that would be happening manually.
Your data entry is taking place in isolation.
It's happening probably on spreadsheets.
If you're an organization that has operations in different currencies in
different countries, all of that needs to be consolidated in the company
currency.
Which means there's manual currency conversion also going on.
Once you've done all of that, the data is probably weeks old, months old.
Therefore, these dashboards are probably run monthly or on quarterly basis on
data that is into the latest and the greatest.
And then once you have all of that information, your insights from these dash
boards need to be acted upon.
And then you would probably take the one size fits all approach for your
promotions and campaigns.
In an autonomous aftermarket reality, you would be able to just have systems
that identify your customers at risk intelligently.
You're able to get details about your customers in the currency you want to see
in.
Probably you're the US head.
You're looking at information in dollars, whereas your counterpart sitting in
Europe is looking at it in euros.
But you're both looking at the same dashboard, real time information.
This is how things could go across to make sure that transactional data is
visible to you real time.
You are tracking your risk factors real time automatically.
And when all of these dashboards are made available to you real time,
you're able to independently offer personalized promotions or deals to each of
those customers so that you're able to retain them, promote them.
Instead of just approaching with the one size fits all.
Here's a sneak peek of how entitled does it.
This is something that we call entitled proprietary customer loyalty manager or
CLM.
We're able to give you a view of your entire locations customers installed base
Segmented in sections where you can identify which of your customers need
attention right now,
which of those are actually healthy, which of those are working average.
And you may may not need to immediately focus on them.
CLM gives you a view of not just those that really need attention and you're
going to scramble through to work towards them,
but you can also leverage these analytics, AI tools and capabilities of
entitled to identify which are your healthy customers that you really need to
focus on.
So your campaigns promotions can be tailored to the kind of audience you're
looking at.
More so CLM also gives you a view of the territory breakdown for your customer
loyalty.
So you could also focus on which territory has the most healthy customers at
the moment.
Your campaigns around those territories are centered around those.
Your territory that has at risk customers that really need attention at the
moment. So that detailing is also available with entitled proprietary models here.
Now, let me take you to another use case where how could you realize
intelligence selling in an aftermarket world where it autonomy is present.
I mean, we've all seen it.
You have cold calling going on. We're shooting in the arrows with limited
knowledge because you don't have enough information about your serviceable
parts,
your replacement parts.
You're managing your codes manually, definitely, and especially because they're
coming in via inbound inquiry.
Now, in all of that, you finally reached a stage where negotiations have taken
place and then you would go to check your inventory.
And unfortunately, if the inventory isn't enough, you will then schedule
production.
Imagine the amount of time that has gone through that and your customer is
waiting for you on the other side for weeks, if not months.
Now, if you were living in an autonomous aftermarket world, intelligence
selling would look like this.
You are able to pursue high propensity opportunities, those that were generated
automatically because of the analysis to identify your customer loyalty.
Remember, CLM.
And then you were able to just identify predictive opportunities by continuous
monitoring of the past transaction data.
So you can surface these opportunities upfront, not wait for inbound inquiries,
especially after negotiations and approvals.
Your path's just dispatched independently without having the need for a human
being to take a look at all of these details.
Your inventory management is happening automatically because there's continuous
analysis of data and production batches are scheduled accordingly.
You're able to just identify lead gen very intelligently, not just within one
view, but across views for that same customer.
So you're able to service all your customers, not just based on the limited
information you have for that view, but across views.
Remember when I said we give you the entire consolidated unified 360 degree
view of a customer?
This is what I'm talking about.
An example of this in our application is this is a sneak peek of how the
install base visibility comes to you in the entire application.
It's not just a list with all the details.
It's also a representation of their propensity to buy.
If you see bubbles here, these bubbles or clusters are groups of customers that
are grouped together based on their likelihood.
To buy from you in the future, what we call the propensity to buy.
Now, if you look at the green ones, they're the ones that have the highest
propensity to buy.
They're most likely to buy from you in the near future.
The ones that are amber and green amber and red, but then be the medium and low
propensity.
So you could design your campaigns accordingly.
Your pipeline could target a particular region where you've got customers with
high propensity to buy.
So your leads opportunities would yield higher revenue if you pursued those.
Similarly, you've got at risk customers identified because you've got red
bubbles out there.
Those are the ones that you need to really focus on with a different kind of
strategy.
So it's definitely not going to be a one size fits all here.
So this is just some examples of how we at entitled believe autonomous after
market can be achieved.
The title is an AI powered purpose build solution.
It's for the industry because we understand the industry and we understand that
the issues of the generational wisdom that is about to get lost.
The data mess that we're all in and struggling to overcome.
These are the things that we really need to work towards to make sure that we
're all sitting in a place where there's autonomous aftermarket.
Now that brings me towards the end of my prepared presentation or back to you J
W.
Thank you, Sushana.
Thank you so much for the content and walking everyone through that.
For those of you here, we have a pre assessment on how to get started with
streamlining your aftermarket process.
You can go ahead and scan this QR code to access that.
We'll leave this up here on the screen for just a few more seconds.
This will give you a good understanding of where you're at in your aftermarket
journey as well as help you think about maybe where you want to go.
If you want to move and thank you everyone again for your time.
Thank you everyone.
This is a test comment.
This is a longer test comment to see how this looks if the person decides to ramble a bit. So they're rambling and rambling and then they even lorem ipsum.