As supply chain optimization involves multiple systems acting together with multiple
input and outputs, it is fundamental to understand how the systems interact. With
new technologies that enable the parallel
processing of enormous data sets through
pattern recognition engines, we are beginning to address these challenges by enabling
modeling of broader digital twin ecosystems, examining the relationships and interactions between agents across supply chain
networks including customers, factories,
warehouses, suppliers, logistics, etc. We call
this a “multiverse” approach, and we believe
it allows us to evaluate potential opportunities that would be missed by logic-based
decision platforms.
Due to improvements in computing,
including in quantum computing, we are
increasingly able to construct and imagine digital twin modeling environments in
which we can effectively compute multiple
internally consistent versions of the future
– a “multiverse” of realities3. Using this
approach, we can help choose what future
to move towards versus being locked into
decisions imposed by deterministic predictions that are rarely correct. Because multiverse modeling at its core allows infinite
scenarios, we are starting to enable a new
form of supply chain modeling where we
can consider potential systems of systems
effects across the extended network that
heretofore could not effectively be represented or
perhaps even imagined.
These types of models
not only break new ground
for understanding and op-
timizing supply chains, they
also will improve the modular digital twin
applications described above by incorpo-
rating systems effects into the applications.
Think, for example, of the systems effects of
supply disruption or exchange-rate volatility
on overall systems performance.
As time progresses and updates are
received, we continuously compute the
new physical realities encapsulated in our
models and can rapidly identify new supply chain optimization opportunities and
strategies, ranging from basic risk mitigation at the edge of the network to broad
strategic pursuits of new cost positions or
business models.
We are already deploying hundreds of
thousands of digital twins in our supply
chains and expect the performance impact
to continue to improve as our self-learning
algorithms in the digital twins are trained
and refined through real world experience.
We believe that, ultimately, autonomous
systems will naturally emerge in the near
future as digital twin technology is paired
with a robust industrial internet and global
digital trust fabric, enabling concurrent information exchange across dispersed global supply chain networks.
The brave new worlds of possibilities in
global supply chain optimization through
digital twin technology may be frightening
to some, but it is also exciting and will eventually become a cornerstone in how GE and
other companies start thinking about and
planning their transitions from traditional
industrial companies to digital-industrial
companies. While it is a long-term vision that
will effectively play out over the next five to
10 years for most manufacturers, there are
many individual steps that manufacturers
can pursue today quite profitably, depending
on their most critical cost, revenue, and busi-nessmodeldrivers. M
MANUFACTURING
LEADERSHIP JOURNAL
..........................................................................................................................................................................................................................................
www.ManufacturingLeadershipCommunity.com
................... ................... ................... ................... ................... ................... ...................
"We are
starting to
see a journey
toward brave
new worlds of
global manu-
facturing and
supply chain
management
where digital
t win tech-
nologies and
strategies are
enabling new
approaches to
monitoring,
managing,
and optimiz-
ing increas-
ingly complex
global supply
chains."
(;) See, e.g., Tom
Simonite, “Google’s
new chip is a
stepping stone to
quantum computing
supremacy,” MI T
Technology Review,
April 21, 2017.