mations that come pre-packaged or easily assembled in desktop applications have led to
many of the productivity improvements we
see in the office today.
We expect to see digital threads operating
in much the same way, automating the inter-operation of product development and production processes and data.
Another approach to automating these
data and processes we call the digital hub
data model. Think of it as something analogous to the Internet. Data exists in many
different places and in many different formats, but we can type into a search engine
and quickly get to just the data we need in a
format we can consume, usually a web page
or digital model in a common data format.
The digital design-to-manufacturing data
hub, in fact, presents even greater opportunities to improve the overall workflow. In the
typical design-to-manufacturing workflow,
there are many data formats and data types,
and we end up passing data from one to another through standard files forwarded to
the next application. In some cases, we even
pass paper from one station to the next.
While the digital thread simply automates
this downstream data flow, a digital hub type
of model makes upstream or downstream
data available to the entire enterprise. Also,
relevant data is available throughout the supply chain, and we can eliminate the need for
all vendors to have the same software stack.
Leveraging the Data
Digital hubs as we envision them will make data accessible to ev- ery entity in the supply chain that
needs access to the data. If the relevant data
is accessible to the supply chain, we can see
the same level of productivity gains that we
have all experienced with the Internet and
the accessibility of data that was often difficult to find even just a decade ago.
If we have all product data collected in a
digital hub, it is now possible for us to apply machine learning or artificial intelligence
to the data to gain greater insights into the
ideation, design, engineering, manufacturing, and production processes. In addition,
we can better identify bottlenecks in our processes. We can understand how each discipline relates to the other disciplines and, in
the end, how to create better products at a
lower cost and with less waste.
With data aggregated in digital hubs, we
can also apply generative algorithms that allow the product development process--from
design to manufacturing--to evolve and leverage mathematics along with past data to
drive improvement and agility.
Machine learning, AI, and generative methods are not intended to automate everything.
Rather they will free up human creativity and
ingenuity. Humans are very good at making
tradeoffs and creating new ideas. Computers
are very good at processing large amounts of
data and doing complicated calculations.
Data at the center, coupled with ways to
process and expose that data to us, will free
us to make tradeoffs between competing
ideas quickly, much in the way we make tradeoffs when making travel choices with the aid
of a travel website. By utilizing data across
the entire product development process, we
will begin to see patterns and generate ideas
that can further drive productivity gains and
create better and more customized products.
It truly is an exciting time in which we live
with access to data and information we need in
our personal lives available at the tap of a button. In our companies, by connecting the data
from ideation of products to the production
and distribution of those products, we will see
similar productivity gains. And with machine
learning, AI, and generative techniques, we will
gain greater insights and make better tradeoffs
in manufacturing the products that we use. M
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