June 20, 2024 • 32 mins
Addressing supply chain inefficiencies continues to be a problem for manufacturers. Legacy systems and information silos cause inventory shortages and production delays.
This podcast explores how digitizing the manufacturing supply chain, from raw materials to delivery, can revolutionize your operations. We discuss how AI, real-time data analysis, and other technologies can optimize performance, unlock valuable insights, and shape the future of supply chain management.
Join us as we explore these ideas with:
Stefano Linari, CEO, iProd
Kemal Levi, Founder and CEO, Relimetrics
Christina Cardoza, Editorial Director, insight.tech
Our guests this episode are Stefano Linari, CEO of iProd, a manufacturing optimization platform provider; and Kemal Levi, Founder and CEO of Relimetrics, machine vision solution provider.
Stefano and Kemal answer our questions about
- 2:41 – Manufacturing supply chain pain points
- 4:47 – Supply chain areas ripe for digitization
- 7:49 – Technologies optimizing supply chain efficiency
- 11:14 – AI’s role in modernizing the supply chain
- 12:59 – Real-world manufacturing supply chain efforts
- 23:00 – The value of leveraging technology partnerships
- 27:57 – The future of the supply chain from end to end
- 30:18 – How AI is going to continue to evolve this space
Related Content
To learn more about the manufacturing supply chain, read AI Boosts Supply Chain Efficiency and Profits and Unified Data Infrastructure = Smart Factory Solutions. For the latest innovations from iProd, follow them on LinkedIn. For the latest from Relimetrics, follow them on Twitter/X at @relimetrics and on LinkedIn.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
(upbeat music)
- Hello and welcome to"insight.tech Talk",
formerly known as IoT Chat
but with the same highquality conversations
around IoT technology trendsand the latest innovations.
(00:22):
I'm your host, Christina Cardoza,
Editorial Director of insight.tech
and today we're goingto explore digitizing
the manufacturing supply chainwith experts from Relimetrics
and iProd but as always,
before we get started,
let's get to know our guests.
We'll start with Kemalfrom Relimetrics first.
Please tell us aboutyourself and your company.
- Hi, I am Kemal Levi.
(00:43):
Founder and CEO for Relimetrics.
We enable customers with
a proven industrial-gradeproduct suite they can easily use
to control and automatequality assurance processes
across use cases with no code
and using our product,
our customers are able tobuild, deploy and maintain
(01:03):
mission critical AIapplications on their own
in conjunction with any hardware.
This can be done bothon prem or in the cloud
and a key industry challenge
that our product repeatedlysucceeds in tackling
is our ability to adapt tohigh production variability
which is commonly experiencedin today's manufacturing.
(01:25):
- Great, looking forwardto getting into that
and how that is going toimpact the supply chain
or bring benefits to the supply chain
but before we get there,Stefano Linari from iProd.
Please tell us aboutyourself and the company.
- Hello, I am Stefano, Stefano Linari.
I am the Founder and CEO of iProd.
(01:46):
iProd is an Italianstartup founded in 2019
to create the first holistic tool
designed for manufacturingcompanies of each size,
accessible for free and asa software as a service.
Our user can leave tons ofpurely integrated software
like ERP, Amira, CRREM, IoT platform
(02:09):
and use just one moderncloud platform, our platform.
- Awesome, so I wanted tostart off the conversation
just getting the stateof things right now.
Obviously a couple of years ago,
the supply chain washeadlining in the news,
almost every day for weeks on end.
Just the challenges and the obstacles,
(02:30):
but I feel like there'sbeen a lot of integration
and advancements in the technology space
that those pain points
we were feeling a couple of years ago,
we have been able to get over a little bit
but I'm curious,
what challenges still remain
or where are the pain points today Stefano
if you want to talk a littlebit about what's going on
at the manufacturingand supply chain level.
(02:52):
- Yeah, this supply chain unfortunately
is still purely integrated.
Especially for its morethan medium enterprises
where digital tools are not updated
and easy to be integrated
because they are legacy technology.
We are far away from the concept
of this so-calledmanufacturing as a service
(03:15):
where the manufacturingcapabilities are accessible
in a fluid way.
This part of the, askfor a highly integrated,
multi-tier supply chain
able to digitally orchestrate
and provide a custom made piece,
optimizing cost, impactand user resources.
(03:37):
Unfortunately, even on the other side
of this supply chain,
if you look at the OEM,
we face other issues.
And the companies are not ableto serve the new part of this
for their industry thatis the machine customer.
(03:57):
Where a product, digital product
is able to purchase autonomously
spare parts, accessories and accessory
from the OEM itself andeven from third parties.
For example, a turning machine
that after digitalization can work a belt
(04:18):
or a gear after severalnumber of working hour.
This is still far away from the reality.
- Yeah, you make somegreat points there Stefano
and one thing I want todiscuss a little further
is you mentioned a lot of problems
is that there's stilllegacy systems in place
and I'm sure that'screating a lot of silos
(04:39):
that these machinescan't talk to each other.
Data is not end to end.
So Kemal, I'm curiousfrom your perspective,
where are some areas that manufacturers
can start digitizingaspects of the supply chain
and how that's going to helpaddress some of the pain points
Stefano just mentioned?
- First of all, digitizingaspects of manufacturing
(05:01):
helps to trace qualityacross the supply chain.
As parts move along the supply chain,
quality automation helpsto identify anomalies
before they get to thecustomer and risk downtime.
So for the entire supply chain
and particularly for the OEMs,
(05:23):
it is really important to tracethe quality status of parts
or products
from a multitude of suppliers
and also run dataanalytics to see which one
is actually performing better
and read out those vendorswho are not performing well.
(05:43):
Now digitizing aspects of manufacturing
also helps to improve the bottom line.
So as manufacturers shipproducts to their customers,
they must identify issueswith outbound transportation
and logistics.
So a magnifying lens,
looking at differentpoints of the supply chain
(06:04):
gives better visibility to improve margins
and in the case of the sectors
that we typically serve to,
margins are often razor thin.
So maximizing the number of items,
getting to the end ofthe manufacturing line
that meet the required quality standards
has a direct impact on the bottom line.
(06:26):
Another example is that digitizing aspects
of manufacturing,
helping to make bettersupply chain decisions
and correlation of acquired data
across the product life cycle
and this can be all theway from manufacturing
to sales to service,
(06:48):
enables continuous business intelligence
and a company that cantrace quality in real time
and do a better assessment onwhere quality issues originate
can ultimately boost profitability.
- Yeah, absolutely.
I'm glad you mentioned thequality automation aspect
of the supply chain.
I feel like sometimes
(07:09):
when we talk aboutsupply chain challenges,
we are often thinking aboutdeliveries and shipments
and getting manufacturingproduction out the door
but it also starts, it'san end to end issue.
It starts on the factory floor,
it starts as you aredeveloping these products,
making sure thateverything is high quality,
that it can go out the doorand can be delivered on time.
(07:32):
So that's a great point that you made
and then looking at the differentpoints of the supply chain
so that it's really anend to end experience.
Stefano, I'm curious, you know,
as we look at quality automation
and all of the different parts
manufacturers need to be on top of
in order to have this end toend digitized supply chains,
what are the technologiesthat are being used
or how can we startenhancing and optimizing
(07:54):
supply chain efficiency?
From our side,
all these things can startfrom the demand side.
If we start to build intelligent machines
that can be transformedin a machine customer,
we can create a more predictable demand.
(08:14):
We can avoid to rush,
to produce spare parts and install it
in a non-planned way.
Creating a simple conditionto optimize the supply chain.
So from our side in these months,
in the last year,
we are pushing this newpart upgrade inside OEMs.
(08:38):
What we have created
to support the OEM to handlenew generation of machines
that we call machine customer
it's to create a free andself-service interface
in the cloud while eachOEM can create their rules
(08:58):
and their identity, the digital twin
of every machine that'sbuilt in few minutes.
Gartner in their last books name it
"When Machines Become Customer"
recognize our platform as
the first machine customer-enablingplatform in the world.
(09:20):
We are then creating
the condition to digitalizethe supply chain.
Because when you speakabout potential saving,
entrepreneurs are interested.
But they are engaged when you tell them
about increasing revenueand with our technology,
(09:41):
embedding new intelligenceon board of the machine,
we are transforming our production tool
in point of sales.
And this is a remarkableshift in the mindset
of the OEM that can beeasily understandable.
- So I'm curious,
(10:02):
because we were talkingabout the legacy systems
earlier in the conversation.
Is this a software approach
that we can take todigitizing the supply chain
or does there have to beinvestments in new hardware?
Or can we leverageexisting infrastructure?
- We have to combine both
because for sure, softwareplatform can make the interface
(10:24):
and user experience simple
but we can't forgetthat manufacturing tools
and equipment, automaticwarehouse and production machines
are not yet intelligent enough
to analyze their needs andtry to simply find the life
(10:46):
to the end user and to the OEM.
So we need a combinedapproach at the moment.
- Great and of course,
when we are talkingabout adding intelligence
and doing things like quality automation,
AI comes to mind.
AI seems to be everywhere these days.
Kemal, you mentioned you were you know,
you have an AI approach
to being able to providethat quality automation
(11:10):
and look at differentparts of the supply chain.
So I'm curious from your perspective,
what is the role that AI should be playing
in these you know, supply chain processes?
- Well, AI in supply chains
can deliver powerfuloptimization capabilities,
required for more accurate
supply chain inventory management.
(11:33):
Can also help to improvedemand forecasting,
reduce supply chain costs
and this can all happen
all while fosteringsafer working conditions
across the entire supply chain.
Traditionally, the supply chain
has relied on manualinspections and sorting.
(11:56):
So I would like to give an example
that centers around smartinventory management.
So this,
this process, the inventorymanagement process
can be labor-intensive and prone to error,
adding costs to the loss.
So today, AI-drivenquality automation tools
(12:18):
like ReliVision can be deployed
without requiring any programming skills
or prior machine learning knowledge
and they can offer accessto real-time information
that can improveefficiency and visibility.
Now similarly, AI can also be used
in conjunction with computer vision
(12:39):
and surveyance cameras tomonitor work efficiency
and safety objectively
and provide data-driveninsights for businesses
to optimize workflows andimprove their productivity.
- So do you have any customer examples?
I know you just providedthe inventory use case,
but I'm curious if youhave any customer examples
(13:00):
that you can share with us,
how, what problems they were facing
and how Relimetrics came in
and was able to help themand what the results were.
- A good example isrenewable energy leaders
which engaged with us
to help them inspecttheir wind turbine blades
before they're released to customers.
So using our AI-based quality automation
(13:23):
and non-destructive inspectiondigitization platform,
our customer is today ableto automate the inspection
of phased array ultrasonic data
and assess the condition of blades
before they are placed in the field.
And the main challenge thatour typical customer has
(13:47):
is to digitize inspections
which is time-consumingand prone to errors
and improve traceabilityacross their supply chains.
And with our product,
our customers can rapidly implement
AI-based machine visionalgorithms on their shop floor
and they don't need towrite a single line of code
(14:09):
while doing this and they can share,
train the models across inspection points
and leverage existing camera hardware,
irrespective of image modality.
Whether it's infrared, X-ray or PAUT.
- I love the no-code approachthat you guys are taking
'cause I know a lot of manufacturers,
(14:29):
they see these benefits,they want to achieve them
but there's obviouslylabor shortages happening
in the area, in their space
and can't always have the skills
or be able to deploy these as fast
but they'd like to get these benefits.
So love seeing how we canmake it more accessible.
When you have these no-code solutions,
(14:50):
who are the type of users that are able
to implement some of these in practice?
Do you need those engineers
or is it really an operator
or a manufacturing manager
that's able to take part in this as well?
- Well we would like toenable process engineers
to be able to build AI solutions
(15:14):
and not only build but alsodeploy these AI solutions
and then maintain them.
So what we see is thatmaintenance of AI solutions
can also be quite costly.
So we are making it possiblefor non-AI engineers
to be able to maintain AI solutions.
(15:34):
Now we can of course alsoserve AI engineers as well,
we can help them just prototypetheir AI solutions faster
and deploy them to the field.
The maintenance piece again istypically an important aspect
that AI engineers typically would like
to transition to operators
(15:56):
after they are successful in the field.
And this is exactly what we do.
We make it possible formaintenance of AI models
and training of new AIalgorithms for new products,
new configurations tobe done by non-AI folks.
- Yeah, it's amazing to seehow far technology has come
(16:18):
and how non-AI folks can be involved.
Especially since these people
are the ones on the factory floor
with the domain intelligence.
So they can spot thequality issues or you know,
be able to train some of these models
better than an AI engineer probably would
if they don't have that deepmanufacturing experience.
Stefano, I'm curious from iProd's side.
What are the solutions and products
(16:41):
that you guys have on the market
that you're helping yourcustomers in these different areas
and if you had any customer examples
that you could share with us as well.
- Yeah, we have several usecases of machine customers
spreading from concrete industry,
industrial filtration and manufacturing.
But I want to present youthe most significant case
(17:06):
that was done with Bilia.
Bilia SPA is the thirdlargest turning center builder
and their machines are soldto automotive companies
and manufacturers of consumer goods
and a lot of industry
(17:27):
where metal parts are needed.
Most of those machines,
you can figure out to beinstalled in a shop floor,
even in small and medium enterprises.
You know that in Italybut in Europe in general,
most of the company areunder nine employees.
So you can imagine that no expertise in IT
(17:53):
can be found in thecustomer side especially.
So we have enriched,equipped this turning machine
with external brain so we can go,
it's in a panel PC, technically speaking
but we like to describeit as an IoT tablet
(18:13):
to make them morefriendly for the end user
and with this tablet
we have two connection at the same time.
One with the CNC of the machines
and then we can acquirereal-time data about usage
and consumption of resources
and on the other connections,
(18:34):
usually Wi-Fi or forward dealing,
we are connected to the iProd cloud.
This solution, it's a bundled solution
because we hand to provide security
and trust of the end user
that no sensible data about their process
and their secret sauce tocreate the perfect piece
(18:56):
are not exfiltrated.
Then in the cloud,Bilia, the manufacturer,
with their process engineer,
a maintenance engineersusing a visual approach
as Kemal defined before.
So even in this case, noprogrammer, no coder is needed
(19:18):
but you have a wizard in the cloud
where you can simply drag anddrop spare parts and services
from the Bilia catalog
to conditions that can be simple rules,
every 1000 hours, please changethe filters or fill the oil
or forward looking AI and ML
(19:43):
that can predict more accurately
what must be changed.
The main point when westart this project is okay,
but why the end customer have to accept
that the turning machine will ask him
to buy something?
(20:04):
I have still spent €200,000 for this turn
and every day he ask more money, why?
I have to pay.
And so it was a bit scary
but the customer not onlyaccept the recommendation,
but they ask machine more.
They allocate a dedicatedbudget to the machine itself.
(20:30):
Usually in the order of €200 per months,
no big budget.
But in the most efficient area,
because under this level,
machine can automatically place the order
and you receive anotification on your mobile,
"Hey Stefano,
(20:52):
"in a couple of days youwill receive the new filter."
Or new belt and so on.
For €50, €60 because mostof the spare parts are cheap
but we try to estimate thecost to placing the order
and processing the orderand this is never lower
(21:16):
than €50 for each side.
So the end user know thatif the machine never stopped
and by autonomously, theyneed the spare parts,
consumable, periodic service,
he is saving money andprobably the same items,
(21:38):
purchased in an autonomousway is even cheaper
because on the other side,
I happen to spend time to answer an email,
answer phone, send acontract and blah blah blah.
So what was something
that at the beginningsounds very difficult to do
(21:59):
because the no skill, no very digital guys
it's a real market success.
- Yeah and I'm sure
that is a common scenario in the industry.
Not knowing where to start,
being worrisome of getting started,
how much it's going to cost,
how complicated it's going to be.
(22:20):
If it's going to be wasted effort.
So it's great to see how manufacturers
can partner with companieslike iProd and Relimetrics
to be able to integrate some of this
and really make improvementsin the supply chain.
One thing that comes tomind and I should mention,
insight.tech, we are sponsored by Intel
but we're talking aboutartificial intelligence
and the cloud and real-time capabilities
(22:43):
and insights into some ofthese things that you know,
I'm sure that you guys areworking with other partners
to make this all happen end to end,
much like your customers.
Sometimes we need to rely on expertise
from other areas.
So curious about how you'reworking with partners
like Intel and what the value of that
(23:04):
and their technology is.
Kemal, I can start with you on that one.
- In our implementations,
we are taking advantageof Intel processors
and Intel hardware
such as Intel® Movidius™vision processing units
and we are also oftenrelying on Intel software
(23:25):
such as OpenVINO™ tooptimize deep learning models
for real-time inferencing at the edge.
Now in the case of quality automation
or digitizing visual inspections,
customers are very sensitiveabout computing hardware costs
(23:46):
and they really do care quitea bit about smart utilization
of CPU.
So we use the Intel OpenVINOtoolkit to minimize the burden
and also as an Intel marketready solution provider.
We have access to a large community
of potential buyers of our product.
(24:08):
- Great, we always lovehearing about OpenVINO.
That is a big toolkit in the AI space.
You know like you mentioned,
taking some of the burden off of you know,
engineers and just beingable to easily run it once
and deploy it on many different hardware.
So it's great to hear.
Stefano, I'm curious from iProd's end.
How are you guys workingwith partners like Intel
(24:30):
and you know, what are the areas
that their technology reallyhelps the iProd solution
be able to benefit customers?
- At the moment we use
widely Intel-embedded mobile processor
because even if we haven'tdone a heavy workload
(24:52):
on AI and ML,
what our customer want is for sure,
to reduce energy consumption at the edge,
you have to consider that each IoT tablet
is installed on top ofeach production machines
and in a harsh environment.
So we need a fanless processor
with high computing power andlow consumption for standby.
(25:18):
We also used Intel connectivity for Wi-Fi
because we need connectivity
that can be reliable in EMC.
Difficult space whereyou have welding machine
and robots with high power
(25:40):
and this is what we have using now.
OpenVINO and new processorwith the Ultra core,
Ultra is also in our ladder.
We are starting toexperiment these features
to accelerate, especiallyML and AI models.
(26:01):
To predict the usage
because we combine in the tablet,
I don't tell before.
Not only IoT data
that came in from theCMCs but from the cloud,
we receive even a scheduleof the next batch to produce
and what we are trying to do
(26:21):
is to forecast the production
because you have to combinehow many working hours
this model will do ifI will win this deal.
Most of the calculationhave to be done on the edge
because customer don'twant to move outside
(26:43):
their company sensitive information.
For the manufacturer,
for example that producepiece for aerospace industry
or high end machines,super car, like Ferrari,
just to name a brand.
(27:05):
Their technology thatis inside your software
of the CMT machines, it's all,
half of the value of your company
and you don't want to transit
even to iProd this information.
You want to process all theinformation on the edge.
- Yeah, absolutely.
One thing I love about these processors
(27:28):
and toolkits is that this, you know,
technology, it seems to beadvancing super fast every day.
Some things that a month agothat we were interested in
is now becoming reality and manufacturers,
sometimes they have trouble keeping up
with all of the advances andgetting all of the benefits.
But with partners likeIntel and these processors,
(27:48):
they're really makingnew changes every day
to ensure that we can continue to keep up
with the pace of innovation.
I'm curious Stefano,
how else do you think thisspace is going to change?
What do we have to look forward to
for the future of the supply chain?
- I agree with even Kemal told before.
(28:09):
What we see, it's a digital continuum.
From the machines to the OEMto the supplier of the OEM
to create a continuum of information.
Because we don't want to spendtime in the order process.
This is the piece that isconsidered a loss of time
(28:31):
and Amazon and other online store,
is driving the user experience.
Because now B2B request inspired
and drove by B2C experiencein the day by day life.
(28:51):
The second main points thatis pushing the digitalization
and will became mandatoryin the next few years,
at least in Europe willbe the ESG regulation
and the so-called Supply Chain Act.
So a company in 2026 hasto present the ESG report.
(29:20):
So they have to account the emission
that each process in the company generate
and the main focus is on themanufacturing side obviously
and with the Supply Chain Act
you have to provide this information,
not only through theESG report to the public
(29:43):
but you have to share pointand data to your customer
in real time or near real time.
This means that the supplychain must be heavily integrated
in the next few years.
- Great point and you mentionedsustainability earlier
(30:03):
where we were talking abouthow some of these things
can help worker safety.
There are so many differentareas that we can talk about
and we've only scratched thesurface in this conversation.
Unfortunately we are running out of time.
So before we go,
Kemal, I just want to throwit back to you one last time.
If there's any final thoughts
or key takeaways you want to add,
what we can expect from the future
of the supply chain management
(30:24):
or how else AI is going tocontinue to evolve in this space?
- Well I think, as I said before,
there will be a lot of focuson real-time data analytics
and correlate acquired dataacross the product lifecycle
and this goes all the wayfrom manufacturing to sales,
(30:45):
to service,
to overall enable continuousbusiness intelligence
and help to derive bettersupply chain decisions.
And I think looking to the futures,
looking to the future, companies
will strengthen demand planningand inventory management
(31:06):
in tandem with their suppliers.
There will be datavisibility at all levels,
whether it's from in-house manufacturing,
suppliers and logistic partners
or customers and distribution centers.
The supply chain
will no longer be drivenby uncertainty in demand
(31:29):
and execution capabilities and overall
it will be characterizedby continuous collaboration
and flow of information.
- Well I can't wait to see
how that all starts to shape out
over the next couple of years
and how Relimetrics andiProd, how the advancements
and innovations
you guys continue to make in this space.
(31:49):
So I invite all of our listeners
to visit iProd and Relimetrics' websites,
see how they can help youdigitize the supply chain
from end to end and really getthat continuum of information
in all aspects of your business
and also visit insight.tech
where we will continue to keepup with iProd and Relimetrics
and highlight the innovations
that are happening in this space.
Until next time, this hasbeen "insight.tech Talk".
(32:12):
Thanks for joining us.
(upbeat music)
(upbeat music)
- Hello and welcome to"insight.tech Talk",
formerly known as IoT Chat
but with the same highquality conversations
around IoT technology trendsand the latest innovations.
(00:22):
I'm your host, Christina Cardoza,
Editorial Director of insight.tech
and today we're goingto explore digitizing
the manufacturing supply chainwith experts from Relimetrics
and iProd but as always,
before we get started,
let's get to know our guests.
We'll start with Kemalfrom Relimetrics first.
Please tell us aboutyourself and your company.
- Hi, I am Kemal Levi.
(00:43):
Founder and CEO for Relimetrics.
We enable customers with
a proven industrial-gradeproduct suite they can easily use
to control and automatequality assurance processes
across use cases with no code
and using our product,
our customers are able tobuild, deploy and maintain
(01:03):
mission critical AIapplications on their own
in conjunction with any hardware.
This can be done bothon prem or in the cloud
and a key industry challenge
that our product repeatedlysucceeds in tackling
is our ability to adapt tohigh production variability
which is commonly experiencedin today's manufacturing.
(01:25):
- Great, looking forwardto getting into that
and how that is going toimpact the supply chain
or bring benefits to the supply chain
but before we get there,Stefano Linari from iProd.
Please tell us aboutyourself and the company.
- Hello, I am Stefano, Stefano Linari.
I am the Founder and CEO of iProd.
(01:46):
iProd is an Italianstartup founded in 2019
to create the first holistic tool
designed for manufacturingcompanies of each size,
accessible for free and asa software as a service.
Our user can leave tons ofpurely integrated software
like ERP, Amira, CRREM, IoT platform
(02:09):
and use just one moderncloud platform, our platform.
- Awesome, so I wanted tostart off the conversation
just getting the stateof things right now.
Obviously a couple of years ago,
the supply chain washeadlining in the news,
almost every day for weeks on end.
Just the challenges and the obstacles,
(02:30):
but I feel like there'sbeen a lot of integration
and advancements in the technology space
that those pain points
we were feeling a couple of years ago,
we have been able to get over a little bit
but I'm curious,
what challenges still remain
or where are the pain points today Stefano
if you want to talk a littlebit about what's going on
at the manufacturingand supply chain level.
(02:52):
- Yeah, this supply chain unfortunately
is still purely integrated.
Especially for its morethan medium enterprises
where digital tools are not updated
and easy to be integrated
because they are legacy technology.
We are far away from the concept
of this so-calledmanufacturing as a service
(03:15):
where the manufacturingcapabilities are accessible
in a fluid way.
This part of the, askfor a highly integrated,
multi-tier supply chain
able to digitally orchestrate
and provide a custom made piece,
optimizing cost, impactand user resources.
(03:37):
Unfortunately, even on the other side
of this supply chain,
if you look at the OEM,
we face other issues.
And the companies are not ableto serve the new part of this
for their industry thatis the machine customer.
(03:57):
Where a product, digital product
is able to purchase autonomously
spare parts, accessories and accessory
from the OEM itself andeven from third parties.
For example, a turning machine
that after digitalization can work a belt
(04:18):
or a gear after severalnumber of working hour.
This is still far away from the reality.
- Yeah, you make somegreat points there Stefano
and one thing I want todiscuss a little further
is you mentioned a lot of problems
is that there's stilllegacy systems in place
and I'm sure that'screating a lot of silos
(04:39):
that these machinescan't talk to each other.
Data is not end to end.
So Kemal, I'm curiousfrom your perspective,
where are some areas that manufacturers
can start digitizingaspects of the supply chain
and how that's going to helpaddress some of the pain points
Stefano just mentioned?
- First of all, digitizingaspects of manufacturing
(05:01):
helps to trace qualityacross the supply chain.
As parts move along the supply chain,
quality automation helpsto identify anomalies
before they get to thecustomer and risk downtime.
So for the entire supply chain
and particularly for the OEMs,
(05:23):
it is really important to tracethe quality status of parts
or products
from a multitude of suppliers
and also run dataanalytics to see which one
is actually performing better
and read out those vendorswho are not performing well.
(05:43):
Now digitizing aspects of manufacturing
also helps to improve the bottom line.
So as manufacturers shipproducts to their customers,
they must identify issueswith outbound transportation
and logistics.
So a magnifying lens,
looking at differentpoints of the supply chain
(06:04):
gives better visibility to improve margins
and in the case of the sectors
that we typically serve to,
margins are often razor thin.
So maximizing the number of items,
getting to the end ofthe manufacturing line
that meet the required quality standards
has a direct impact on the bottom line.
(06:26):
Another example is that digitizing aspects
of manufacturing,
helping to make bettersupply chain decisions
and correlation of acquired data
across the product life cycle
and this can be all theway from manufacturing
to sales to service,
(06:48):
enables continuous business intelligence
and a company that cantrace quality in real time
and do a better assessment onwhere quality issues originate
can ultimately boost profitability.
- Yeah, absolutely.
I'm glad you mentioned thequality automation aspect
of the supply chain.
I feel like sometimes
(07:09):
when we talk aboutsupply chain challenges,
we are often thinking aboutdeliveries and shipments
and getting manufacturingproduction out the door
but it also starts, it'san end to end issue.
It starts on the factory floor,
it starts as you aredeveloping these products,
making sure thateverything is high quality,
that it can go out the doorand can be delivered on time.
(07:32):
So that's a great point that you made
and then looking at the differentpoints of the supply chain
so that it's really anend to end experience.
Stefano, I'm curious, you know,
as we look at quality automation
and all of the different parts
manufacturers need to be on top of
in order to have this end toend digitized supply chains,
what are the technologiesthat are being used
or how can we startenhancing and optimizing
(07:54):
supply chain efficiency?
From our side,
all these things can startfrom the demand side.
If we start to build intelligent machines
that can be transformedin a machine customer,
we can create a more predictable demand.
(08:14):
We can avoid to rush,
to produce spare parts and install it
in a non-planned way.
Creating a simple conditionto optimize the supply chain.
So from our side in these months,
in the last year,
we are pushing this newpart upgrade inside OEMs.
(08:38):
What we have created
to support the OEM to handlenew generation of machines
that we call machine customer
it's to create a free andself-service interface
in the cloud while eachOEM can create their rules
(08:58):
and their identity, the digital twin
of every machine that'sbuilt in few minutes.
Gartner in their last books name it
"When Machines Become Customer"
recognize our platform as
the first machine customer-enablingplatform in the world.
(09:20):
We are then creating
the condition to digitalizethe supply chain.
Because when you speakabout potential saving,
entrepreneurs are interested.
But they are engaged when you tell them
about increasing revenueand with our technology,
(09:41):
embedding new intelligenceon board of the machine,
we are transforming our production tool
in point of sales.
And this is a remarkableshift in the mindset
of the OEM that can beeasily understandable.
- So I'm curious,
(10:02):
because we were talkingabout the legacy systems
earlier in the conversation.
Is this a software approach
that we can take todigitizing the supply chain
or does there have to beinvestments in new hardware?
Or can we leverageexisting infrastructure?
- We have to combine both
because for sure, softwareplatform can make the interface
(10:24):
and user experience simple
but we can't forgetthat manufacturing tools
and equipment, automaticwarehouse and production machines
are not yet intelligent enough
to analyze their needs andtry to simply find the life
(10:46):
to the end user and to the OEM.
So we need a combinedapproach at the moment.
- Great and of course,
when we are talkingabout adding intelligence
and doing things like quality automation,
AI comes to mind.
AI seems to be everywhere these days.
Kemal, you mentioned you were you know,
you have an AI approach
to being able to providethat quality automation
(11:10):
and look at differentparts of the supply chain.
So I'm curious from your perspective,
what is the role that AI should be playing
in these you know, supply chain processes?
- Well, AI in supply chains
can deliver powerfuloptimization capabilities,
required for more accurate
supply chain inventory management.
(11:33):
Can also help to improvedemand forecasting,
reduce supply chain costs
and this can all happen
all while fosteringsafer working conditions
across the entire supply chain.
Traditionally, the supply chain
has relied on manualinspections and sorting.
(11:56):
So I would like to give an example
that centers around smartinventory management.
So this,
this process, the inventorymanagement process
can be labor-intensive and prone to error,
adding costs to the loss.
So today, AI-drivenquality automation tools
(12:18):
like ReliVision can be deployed
without requiring any programming skills
or prior machine learning knowledge
and they can offer accessto real-time information
that can improveefficiency and visibility.
Now similarly, AI can also be used
in conjunction with computer vision
(12:39):
and surveyance cameras tomonitor work efficiency
and safety objectively
and provide data-driveninsights for businesses
to optimize workflows andimprove their productivity.
- So do you have any customer examples?
I know you just providedthe inventory use case,
but I'm curious if youhave any customer examples
(13:00):
that you can share with us,
how, what problems they were facing
and how Relimetrics came in
and was able to help themand what the results were.
- A good example isrenewable energy leaders
which engaged with us
to help them inspecttheir wind turbine blades
before they're released to customers.
So using our AI-based quality automation
(13:23):
and non-destructive inspectiondigitization platform,
our customer is today ableto automate the inspection
of phased array ultrasonic data
and assess the condition of blades
before they are placed in the field.
And the main challenge thatour typical customer has
(13:47):
is to digitize inspections
which is time-consumingand prone to errors
and improve traceabilityacross their supply chains.
And with our product,
our customers can rapidly implement
AI-based machine visionalgorithms on their shop floor
and they don't need towrite a single line of code
(14:09):
while doing this and they can share,
train the models across inspection points
and leverage existing camera hardware,
irrespective of image modality.
Whether it's infrared, X-ray or PAUT.
- I love the no-code approachthat you guys are taking
'cause I know a lot of manufacturers,
(14:29):
they see these benefits,they want to achieve them
but there's obviouslylabor shortages happening
in the area, in their space
and can't always have the skills
or be able to deploy these as fast
but they'd like to get these benefits.
So love seeing how we canmake it more accessible.
When you have these no-code solutions,
(14:50):
who are the type of users that are able
to implement some of these in practice?
Do you need those engineers
or is it really an operator
or a manufacturing manager
that's able to take part in this as well?
- Well we would like toenable process engineers
to be able to build AI solutions
(15:14):
and not only build but alsodeploy these AI solutions
and then maintain them.
So what we see is thatmaintenance of AI solutions
can also be quite costly.
So we are making it possiblefor non-AI engineers
to be able to maintain AI solutions.
(15:34):
Now we can of course alsoserve AI engineers as well,
we can help them just prototypetheir AI solutions faster
and deploy them to the field.
The maintenance piece again istypically an important aspect
that AI engineers typically would like
to transition to operators
(15:56):
after they are successful in the field.
And this is exactly what we do.
We make it possible formaintenance of AI models
and training of new AIalgorithms for new products,
new configurations tobe done by non-AI folks.
- Yeah, it's amazing to seehow far technology has come
(16:18):
and how non-AI folks can be involved.
Especially since these people
are the ones on the factory floor
with the domain intelligence.
So they can spot thequality issues or you know,
be able to train some of these models
better than an AI engineer probably would
if they don't have that deepmanufacturing experience.
Stefano, I'm curious from iProd's side.
What are the solutions and products
(16:41):
that you guys have on the market
that you're helping yourcustomers in these different areas
and if you had any customer examples
that you could share with us as well.
- Yeah, we have several usecases of machine customers
spreading from concrete industry,
industrial filtration and manufacturing.
But I want to present youthe most significant case
(17:06):
that was done with Bilia.
Bilia SPA is the thirdlargest turning center builder
and their machines are soldto automotive companies
and manufacturers of consumer goods
and a lot of industry
(17:27):
where metal parts are needed.
Most of those machines,
you can figure out to beinstalled in a shop floor,
even in small and medium enterprises.
You know that in Italybut in Europe in general,
most of the company areunder nine employees.
So you can imagine that no expertise in IT
(17:53):
can be found in thecustomer side especially.
So we have enriched,equipped this turning machine
with external brain so we can go,
it's in a panel PC, technically speaking
but we like to describeit as an IoT tablet
(18:13):
to make them morefriendly for the end user
and with this tablet
we have two connection at the same time.
One with the CNC of the machines
and then we can acquirereal-time data about usage
and consumption of resources
and on the other connections,
(18:34):
usually Wi-Fi or forward dealing,
we are connected to the iProd cloud.
This solution, it's a bundled solution
because we hand to provide security
and trust of the end user
that no sensible data about their process
and their secret sauce tocreate the perfect piece
(18:56):
are not exfiltrated.
Then in the cloud,Bilia, the manufacturer,
with their process engineer,
a maintenance engineersusing a visual approach
as Kemal defined before.
So even in this case, noprogrammer, no coder is needed
(19:18):
but you have a wizard in the cloud
where you can simply drag anddrop spare parts and services
from the Bilia catalog
to conditions that can be simple rules,
every 1000 hours, please changethe filters or fill the oil
or forward looking AI and ML
(19:43):
that can predict more accurately
what must be changed.
The main point when westart this project is okay,
but why the end customer have to accept
that the turning machine will ask him
to buy something?
(20:04):
I have still spent €200,000 for this turn
and every day he ask more money, why?
I have to pay.
And so it was a bit scary
but the customer not onlyaccept the recommendation,
but they ask machine more.
They allocate a dedicatedbudget to the machine itself.
(20:30):
Usually in the order of €200 per months,
no big budget.
But in the most efficient area,
because under this level,
machine can automatically place the order
and you receive anotification on your mobile,
"Hey Stefano,
(20:52):
"in a couple of days youwill receive the new filter."
Or new belt and so on.
For €50, €60 because mostof the spare parts are cheap
but we try to estimate thecost to placing the order
and processing the orderand this is never lower
(21:16):
than €50 for each side.
So the end user know thatif the machine never stopped
and by autonomously, theyneed the spare parts,
consumable, periodic service,
he is saving money andprobably the same items,
(21:38):
purchased in an autonomousway is even cheaper
because on the other side,
I happen to spend time to answer an email,
answer phone, send acontract and blah blah blah.
So what was something
that at the beginningsounds very difficult to do
(21:59):
because the no skill, no very digital guys
it's a real market success.
- Yeah and I'm sure
that is a common scenario in the industry.
Not knowing where to start,
being worrisome of getting started,
how much it's going to cost,
how complicated it's going to be.
(22:20):
If it's going to be wasted effort.
So it's great to see how manufacturers
can partner with companieslike iProd and Relimetrics
to be able to integrate some of this
and really make improvementsin the supply chain.
One thing that comes tomind and I should mention,
insight.tech, we are sponsored by Intel
but we're talking aboutartificial intelligence
and the cloud and real-time capabilities
(22:43):
and insights into some ofthese things that you know,
I'm sure that you guys areworking with other partners
to make this all happen end to end,
much like your customers.
Sometimes we need to rely on expertise
from other areas.
So curious about how you'reworking with partners
like Intel and what the value of that
(23:04):
and their technology is.
Kemal, I can start with you on that one.
- In our implementations,
we are taking advantageof Intel processors
and Intel hardware
such as Intel® Movidius™vision processing units
and we are also oftenrelying on Intel software
(23:25):
such as OpenVINO™ tooptimize deep learning models
for real-time inferencing at the edge.
Now in the case of quality automation
or digitizing visual inspections,
customers are very sensitiveabout computing hardware costs
(23:46):
and they really do care quitea bit about smart utilization
of CPU.
So we use the Intel OpenVINOtoolkit to minimize the burden
and also as an Intel marketready solution provider.
We have access to a large community
of potential buyers of our product.
(24:08):
- Great, we always lovehearing about OpenVINO.
That is a big toolkit in the AI space.
You know like you mentioned,
taking some of the burden off of you know,
engineers and just beingable to easily run it once
and deploy it on many different hardware.
So it's great to hear.
Stefano, I'm curious from iProd's end.
How are you guys workingwith partners like Intel
(24:30):
and you know, what are the areas
that their technology reallyhelps the iProd solution
be able to benefit customers?
- At the moment we use
widely Intel-embedded mobile processor
because even if we haven'tdone a heavy workload
(24:52):
on AI and ML,
what our customer want is for sure,
to reduce energy consumption at the edge,
you have to consider that each IoT tablet
is installed on top ofeach production machines
and in a harsh environment.
So we need a fanless processor
with high computing power andlow consumption for standby.
(25:18):
We also used Intel connectivity for Wi-Fi
because we need connectivity
that can be reliable in EMC.
Difficult space whereyou have welding machine
and robots with high power
(25:40):
and this is what we have using now.
OpenVINO and new processorwith the Ultra core,
Ultra is also in our ladder.
We are starting toexperiment these features
to accelerate, especiallyML and AI models.
(26:01):
To predict the usage
because we combine in the tablet,
I don't tell before.
Not only IoT data
that came in from theCMCs but from the cloud,
we receive even a scheduleof the next batch to produce
and what we are trying to do
(26:21):
is to forecast the production
because you have to combinehow many working hours
this model will do ifI will win this deal.
Most of the calculationhave to be done on the edge
because customer don'twant to move outside
(26:43):
their company sensitive information.
For the manufacturer,
for example that producepiece for aerospace industry
or high end machines,super car, like Ferrari,
just to name a brand.
(27:05):
Their technology thatis inside your software
of the CMT machines, it's all,
half of the value of your company
and you don't want to transit
even to iProd this information.
You want to process all theinformation on the edge.
- Yeah, absolutely.
One thing I love about these processors
(27:28):
and toolkits is that this, you know,
technology, it seems to beadvancing super fast every day.
Some things that a month agothat we were interested in
is now becoming reality and manufacturers,
sometimes they have trouble keeping up
with all of the advances andgetting all of the benefits.
But with partners likeIntel and these processors,
(27:48):
they're really makingnew changes every day
to ensure that we can continue to keep up
with the pace of innovation.
I'm curious Stefano,
how else do you think thisspace is going to change?
What do we have to look forward to
for the future of the supply chain?
- I agree with even Kemal told before.
(28:09):
What we see, it's a digital continuum.
From the machines to the OEMto the supplier of the OEM
to create a continuum of information.
Because we don't want to spendtime in the order process.
This is the piece that isconsidered a loss of time
(28:31):
and Amazon and other online store,
is driving the user experience.
Because now B2B request inspired
and drove by B2C experiencein the day by day life.
(28:51):
The second main points thatis pushing the digitalization
and will became mandatoryin the next few years,
at least in Europe willbe the ESG regulation
and the so-called Supply Chain Act.
So a company in 2026 hasto present the ESG report.
(29:20):
So they have to account the emission
that each process in the company generate
and the main focus is on themanufacturing side obviously
and with the Supply Chain Act
you have to provide this information,
not only through theESG report to the public
(29:43):
but you have to share pointand data to your customer
in real time or near real time.
This means that the supplychain must be heavily integrated
in the next few years.
- Great point and you mentionedsustainability earlier
(30:03):
where we were talking abouthow some of these things
can help worker safety.
There are so many differentareas that we can talk about
and we've only scratched thesurface in this conversation.
Unfortunately we are running out of time.
So before we go,
Kemal, I just want to throwit back to you one last time.
If there's any final thoughts
or key takeaways you want to add,
what we can expect from the future
of the supply chain management
(30:24):
or how else AI is going tocontinue to evolve in this space?
- Well I think, as I said before,
there will be a lot of focuson real-time data analytics
and correlate acquired dataacross the product lifecycle
and this goes all the wayfrom manufacturing to sales,
(30:45):
to service,
to overall enable continuousbusiness intelligence
and help to derive bettersupply chain decisions.
And I think looking to the futures,
looking to the future, companies
will strengthen demand planningand inventory management
(31:06):
in tandem with their suppliers.
There will be datavisibility at all levels,
whether it's from in-house manufacturing,
suppliers and logistic partners
or customers and distribution centers.
The supply chain
will no longer be drivenby uncertainty in demand
(31:29):
and execution capabilities and overall
it will be characterizedby continuous collaboration
and flow of information.
- Well I can't wait to see
how that all starts to shape out
over the next couple of years
and how Relimetrics andiProd, how the advancements
and innovations
you guys continue to make in this space.
(31:49):
So I invite all of our listeners
to visit iProd and Relimetrics' websites,
see how they can help youdigitize the supply chain
from end to end and really getthat continuum of information
in all aspects of your business
and also visit insight.tech
where we will continue to keepup with iProd and Relimetrics
and highlight the innovations
that are happening in this space.
Until next time, this hasbeen "insight.tech Talk".
(32:12):
Thanks for joining us.
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