The document discusses how machine learning in manufacturing has focused on optimizing individual machines, but now needs to take the next step of analyzing data across entire production networks. Analyzing inventory, costs, machine capabilities, and more across all plants unlock opportunities to: 1) Determine optimal workflows and allow machines to run at slower rates when downstream processes are delayed 2) Enable a "gig economy" to deploy specialized labor more precisely as needed 3) Allow facilities to produce different product types more efficiently through a multi-modal model informed by cross-network data 4) Better share excess capacity or workload across all plants to optimize the entire supply network.

1. | |May 2016
1CIOReview
CIOREVIEW.COMMAY 31, 2016
CIOReviewT h e N a v i g a t o r f o r E n t e r p r i s e S o l u t i o n s
CIOReview
MANUFACTURING SPECIAL
CXO INSIGHTS
IN MY OPINION
Paul Boris,
CIO-Advanced
Manufacturing, GE
Keith Moore,
Senior Product Manager,
SparkCognition
The ERP and Cloud
Matchmaker
LeanSwift:
Johan Axelsson,
CEO

2. | |May 2016
8CIOReview
In My
Opinion
Machine Learning in Manufacturing:
Moving to Network-
Wide Approach
By Paul Boris, CIO - Advanced Manufacturing, GE
he challenge with
machine learning in
manufacturing isn’t
always the machines;
it’s often the people
as well. For nearly
30 years, the industry
has talked about the
coming of one big
interconnected network of plants, supply chains,
enterprises and technology that creates a digital-
lean-manufacturing nirvana. While we’re well
on our way to reaching that mountain-top of
just-in-time delivery and zero waste, a risk-
adverse culture has slowed the implementation of
machine learning.
Up until this point, machine learning in the
Industrial Internet has focused on optimizing at
the machine level. We have access to a ton of data
about machine function and productivity that we
have used to run our machines at full capacity
for as long as possible and predict many
maintenance issues.
But now it’s time to take the next
step and start looking at network-
wide efficiency. By moving beyond
the nodes of machine data and
analyzing the bigger pic-
ture, manufacturers can
unlock the true poten-
tial of machine learn-
ing. Network-focused
machine learning al-
gorithms will include
data sets like inventory, material cost and labor
cost, machine capability and performance – fac-
tors that have been considered on a plant-by-plant
basis already. However, by opening up the entire
network’s worth of data to these network-based
algorithms we can unlock an endless amount of
previously unattainable opportunities.
OptimalWorkflow
With the move to network-based machine learning
algorithms, engineers will have the ability to
determine the optimal workflow based on the next
stage of the manufacturing process. We already
have the ability to run machines at extremely high
productivity rates, but what’s the point of stressing
a machine if the next piece has been delayed for
two weeks? Machine learning algorithms will give
plant engineers the knowledge that they can run
certain machine at a slower to reduce the wear on
the equipment, while still completing
its output in time for the next
stage in the manufacturing
process. The engineer
needs the authority and
the ability to move in
and amongst the data,
letting the algorithms
understand the impact of
the current performance
on the next action and
recommend a course to
the operator that most
effectively meets the
business objectives.
T

3. The Gig Economy
Looking beyond the machines themselves,
machine-learning algorithms can reduce
labor costs and improve the work-life
balance of plant employees. By utilizing
more data from across the network of
plants and incorporating seemingly
disparate systems, we can better enable
the “gig” economy in the manufacturing
industry. For example, you might employ a
very specific skillset based on the products
you build or machines you run. Using
advanced data and machine-learning
algorithms you may have identified that
the likelihood of mechanical issues or
production disruption is imminent. Instead
of having the specialized labor arrive
either too early to be fully productive or
too late to avert the issue, an organization
can be more prescriptive as to when and
where they deploy key resources if at all.
And while many companies do this now
with seasonal or surge labor, we’ve seen
that this model can be utilized effectively
in many of the new consumer-based
business models that are emerging. A
shorter work day that provides the same
amount of productivity for both the worker
and the plant is a win-win, it’s the theory
of working smarter not harder.
Multi-Modal Facilities
Today, large manufacturers often have
plants set-up based on industry or product
set. For example, they have one plant
focused on healthcare products and one
focused on aviation. By enabling machine
learning to look across the entire network,
manufacturers will be able to more
effectively move to a multi-modal facility
production model. What part, machine or
skill profiles are similar across gas turbines
and jet engines, for example, and how
could one site be tuned to fulfill demand
for multiple businesses? Enabling the
seamless flow of data (the Digital Thread)
is critical in this case, but machine-
learning algorithms can determine that the
most cost effective production strategy is
to make 1,000 parts in Kansas and ship
300 to the healthcare plant in Wyoming
and 700 to the aviation plant in California.
By moving to a multi-modal production
model and analyzing a broader, real-time
data set, the capacity of each plant is
optimized to increase the efficiency of the
entire network.
OptimizingCapacity
Across a diverse manufacturing operation, at
any point in time there will always be some
plants that have excess capacity, while others
are struggling to deal with spikes in demand.
Today,manymanufacturingplantsaresiloed,
and are forced to determine how to maximize
their own operational effectiveness even if
that includes planned downtime or overtime.
By sharing data across the network, a plant
could better share any excess capacity or
shed workload to better optimize the supply
network, as opposed to a single operation.
While this is done in a macro sense today,
the window of opportunities will continue
to shrink as we approach a real-time supply
chain for the most complex, engineered-to-
order products.
Schedule for Purpose
When producing extremely large or
complex products, scheduling production
to optimize cost and delivery can be
difficult on both the manufacturer and
the customer side. Every manufacturing
supply-chain executive can share horror
stories about customers requesting to
move up or push back their delivery
dates and the chaos that can ensue.
In the future, the algorithms will be
able to provide the ability to schedule
for purpose. When one customer says
that they want to move their order
back from March to May because their
facility won’t be ready, the algorithm
will determine whether the production
schedule can be adjusted to incorporate
another customer’s request for an
expedite, or if the delay might be of
other benefit to the facility like shedding
overtime planned to meet the original
demand, maximizing both employee and
machine productivity.
These are all opportunities that can,
and at some level, are being realized
right now – just not in the most complex
manufacturing operations. The technology
exists, but industry must first move from
applying machine learning to haphazard
array of node-based data on machine or
cell performance to looking at the network
as a whole. The real value in machine
learning is in the algorithms that tell us
where we should be investing in people,
tools, techniques and technology across the
entire manufacturing network as informed
by real operational data, rather than what
to do with any single machine.
Paul Boris
Looking beyond the machines themselves,
machine-learning algorithms can reduce
labor costs and improve the work-life
balance of plant employees