GOOGLE CLOUD NEXT '20: ONAIR
MANUFACTURING
ADDRESSING THE "NEW NORMAL" IN MANUFACTURING
Hi, I'm Dominik Wee
and I lead Google Cloud's Industry Solutions Team for Manufacturing and Industrial.
We are proud to support a broad range of manufacturing companies
on their digital transformation journeys.
Those companies range from small, highly specialized players, to global conglomerates.
At Google Cloud, we have built a team of experts
that deeply understands the nuances of manufacturing companies.
We are building industry-specific solutions
leveraging Google's unique AI and infrastructure capabilities.
Our customers work with these industry-specific solutions
as well as with a variety of capabilities that we have in Google and Alphabet.
Those range from our suite of collaboration tools, G Suite
to Cloud infrastructure and AI to specialized hardware such as Google Glass
and our dedicated edge machine learning accelerator, Coral.
In addition to these efforts
we partner with leading technology providers in the manufacturing space.
Today, I would like to discuss Google Cloud's perspective on manufacturing
how the COVID-19 pandemic impacts the industry both short and long term
and how we see technology helping the industry respond, recover, and re-imagine.
As an industry with a lot of history
this isn't the first time manufacturing has experienced disruptive changes.
What previous adversities have shown is that while some effects may have been temporary,
each of these global challenges has accelerated emerging trends
and brought lasting changes to the industry.
World War II massively accelerated the share of women in the workforce.
In fact, between 1940 and 1945
the US female labor workforce grew by 50%, to a height of 20 million.
The 9/11 attacks demanded hardening of physical assets
including factories and refining operations.
And the recent financial crisis accelerated the transition
to low cost countries for manufacturing.
What lasting changes should we expect coming out of the current disruption?
The immediate change currently is increased health and safety requirements.
Remote work has become a default for many jobs.
Particularly in manufacturing
we are seeing a dramatic uptake of our virtual collaboration tools
including companies that are using these technologies for the first time at scale.
Many customers tell us that their way of working
will fundamentally change beyond the current situation.
However, we believe the deeper, more profound changes are yet to come.
While the past decades have been characterized by
manufacturing moving more and more to lower cost locations
we believe we will see a reversal of this trend.
This will have a profound impact on the entire manufacturing ecosystem.
Most of all, the shift to higher cost locations
will lead to an acceleration of automation and digitalization.
This will be needed to prevent overall manufacturing costs from increasing.
Those geographic shifts will also lead to a fundamental restructuring of supply chains
to enable more diversification and localization.
In mastering those changes, one needs to consider the industry's current situation.
Transforming a manufacturing company is more involved than simply changing a website.
Many competitively differentiating assets are physical in nature.
These physical assets bring along their own operational technology stacks.
Often we encounter a patchwork of legacy systems
that are both critical to business continuity
as well as very costly to maintain and modify.
You can see the challenge manufacturers have to quickly adopt change.
One company that has persisted through times of hardship
ever since its founding in 1899 is our partner, Renault.
During this most recent crisis,
Renault used its 3D printing and injection molding capabilities
to produce more than 100,000 face shields for hospitals
and retirement centers in communities near its global manufacturing facilities.
Renault also partnered with startups
the French government, and the maker community
to pivot from dream to medical certification
for much-needed parts and ventilators in just six weeks.
I'm very excited to have Eric Marchiol
Renault's leader for Industry 4.0 with us today.
Hey Eric, thanks so much for joining us today
and taking the time to have this conversation with us.
Hi, Dominik.
Thank you for giving me the opportunity to explain our strategy.
Renault is a very large and complex organization.
What role does technology play in helping you scale?
So for sure, we have operations in nearly all countries across the planet,
and many, many, many plants.
And in all our plants, we are extracting data from our equipment.
Bringing this data to life with standardization
and real-time access is a really big challenge.
And the quantity of data is huge.
We have a half a billion events that we record each day in all our plants.
And we need to use this data on a real-time basis
to improve our operation for energy
for predictive maintenance, or for cycle-time optimization.
How are you using data outside the four walls of the factory
for instance, in your supply chain?
The supply chain is very extensive in automotive, and across the planet.
So being able to track and trace all our parts from all our suppliers
not only Tier 1 suppliers, but also Tier 2 and 3 suppliers
and bringing this information in real time
not like we've done in the past
at the end of the week or at the end of the month with the inventory.
Now, for us, bringing the data in real time a game changer.
So a decision that was taken by a plant manager or an additional director
is now taken directly by a technician or an engineer in the field
before the problem is too big.
What's also important is the mindset change that data gathering in real time is giving us.
What role does artificial intelligence and machine learning play?
We are using machine learning for two or three basic activities, I would say.
The first one is for quality control with cameras.
For a long time, we have been using vision cameras.
But it cost quite a lot of money
because we do it in the traditional way of implementing those stations.
And now with machine learning and a simple device
we are able to divide the cost by 10 for implementation of those quality controls.
What is also important is that the time
to implement those devices is completely different.
In one case, you have a traditional industrial project
and you will need about one year from the decision to implementation.
And with machine learning and on-the-shelf technology
we can target first implementation in less than one month
and a generation in two or three months for a multiple controller area.
Another topic about machine learning
is energy optimization in our paint shops.
So how we can gather the data we have from our equipment, but also from outside
from the humidity and all that information
to process in real time the best parameters for energy savings in our paint shops.
And we believe that we can have savings of between 10% and 20%
in energy consumption in our plants with machine learning technologies.
Thank you, Eric.
That has been incredibly insightful.
Thanks so much for taking the time and joining us today.
Thanks a lot, Dominik.
It's inspiring to hear how an iconic manufacturer
like Renault has continuously innovated for more than a century
and will continue to do so.
ENGAGING WITH MANUFACTURING CUSTOMERS IN 3 MAIN AREAS
Let's talk about how Google Cloud is working in the manufacturing industry.
There are three main areas in which we engage with our customers.
Firstly, maintaining business continuity.
This has been a top priority for many of our customers
we expect that we'll continue to see increased volatility in the external environment.
Secondly, operations optimization.
We're helping customers achieve the next level of efficiency using digital tools.
Thirdly, openness and integration across all OT and IT environments.
We want to meet our customers where they are.
We embrace the complexity of existing systems in manufacturing.
And we embrace the desire of our customers
to keep mission-critical data and systems on their own premises.
I'll now dive into each pillar a bit more
starting with business continuity.
All manufacturers are facing increased challenges
in maintaining health and safety in the workplace.
We help customers achieve business continuity
through our virtual collaboration tools.
Viessmann, a German manufacturer of heating, industrial and refrigeration systems
was able to keep up its production with G Suite
helping them convert employees to remote work within a 48-hour span.
Now they're conducting roughly 60,000 Google Meet conferences per month.
The other aspect to consider for business continuity is the impact on planning.
Often, planning involves taking the previous year's plans
as a reference and making incremental adjustments.
Clearly, given the current volatility this approach breaks down in many cases.
Using our state-of-the-art machine learning capabilities
combined with our collaboration tools
we can help manufacturers transition to a more agile planning regime
relying more on external signals.
Our approach leverages experiences we have made in areas such as retail manufacturing.
We are helping California Design Den
a manufacturer of high-quality bedding products
better sense customer demand
harmonize the customer experience, and optimize forecasting accuracy.
In doing so, they achieved an increase of 95% forecast accuracy
and reduced inventory carryovers by 50%.
Secondly, turning to operations optimization.
Continuous improvement is the lifeblood of manufacturing.
For more than a century, manufacturing companies have driven innovation
such as the introduction of the assembly line in 1913
mass production, and more recently, LEAN.
However, returns on investment for those innovations have been decreasing over time.
The question is, what can be the next accelerator for optimization in manufacturing?
We believe that artificial intelligence holds the potential to play this role.
Artificial intelligence, or AI, has reached a level of maturity
where it has become an essential ingredient in many of our consumer products
for instance, in how we improve search results.
We are seeing many promising applications of these capabilities in manufacturing.
And, we believe that
the increased push on automation and digitalization mentioned before will accelerate
the application of AI at scale in manufacturing.
A great example of AI in use is ABInBev.
ABInBev makes about 630 beer brands in 150 countries.
They use our technology to reduce costs and improve quality
by optimizing the beer filtration process with much greater accuracy.
Another great area that illustrates this particularly well is quality control.
Today, the manufacture of many products, from cookies to windmills,
requires manual quality control processes, or ones with very expensive automation.
With visual inspection, we can offer a simple and cost-effective answer.
We've applied this, for instance, at LG CNS
one of the world's most recognizable electronics brands.
Our machine learning model reaches an accuracy of 99.9%
for defect detection on display panels.
The machine learning model runs on the edge in this case
since 200 images need to be inspected in 0.8 seconds for each and every display.
What's special about our approach to visual inspection
is that it's particularly easy to deploy and use.
Set-up is quick, and thanks to pre-trained models
the number of images required to train a new model is reduced.
It's fully integrated with our AI stack
making it easy to scale across a multitude of adjacent use cases.
Forrester notes in their recent report
"The New Wave for Computer Vision Platforms"
that Google Cloud is the platform to pick that can do the most for the most.
From business users, to developers and data scientists
Google has powerful computer vision tools that all of them can use.
Lastly, coming to openness and integration across OT and IT environments.
A common issue in making technology work
in a manufacturing environment is the ability to scale up from an initial pilot.
Underlying this challenge is the complexity of the existing IT landscape
particularly when a company runs dozens or even hundreds of manufacturing locations.
With Anthos, we offer a solution based on Kubernetes
to integrate IT across on-premise environments
edge devices, our own cloud, as well as other clouds.
It's a software-only stack
enabling manufacturers to leverage existing investments in IT infrastructure.
Moreover, with our serverless data warehouse BigQuery
we help customers harmonize their data across a myriad of sources.
For instance, Kaeser
a leading manufacturer of compressed air and vacuum products
uses Anthos to combine business data with machine data
and drive unique insights across the enterprise
as well as streamlining customer data in real time to optimize service.
Additionally, with our support for OPC UA,
a machine-to-machine communication protocol for industrial automation
we can utilize factory machine data in Google Cloud
to facilitate advanced manufacturing insights
as well as to control and monitor production processes.
Our support for OPC UA is aligned with our commitment to open standards
and interoperability between machines and systems to enable advanced analytics and AI.
Renault, for instance, has adopted OPC UA
as their company standard for machine-to-machine communication.
Renault will both retrofit existing machines
as well as purchase new OPC UA-capable machines
to establish an industrial data management platform based on Google Cloud.
This will also allow Renault to standardize the data model describing operations globally.
To recap, we are helping manufacturing companies around the world
big and small, in three main ways
maintaining business continuity, optimizing operations
and facilitating openness and integration across OT and IT environments.
Stay tuned and explore the Next OnAir
scheduled for deeper dives into the solutions we've outlined and much more.
Many thanks for your attention.