Page 9 - Logistics News July 2019

Thought Leadership
2. Access to community (multi-party) data
The ability to access data outside of the
enterprise or, more importantly, receive
permission to see the data that is relevant
to your trading community must be made
available to any type of AI, Deep Learning or
Machine Learning algorithms. Unless the AI
tool can see the forward-most demand and
downstream supply, and all relevant constraints
and capacities in the supply chain, the results
will be no better than that of a traditional
planning system. Unfortunately, this lack of
visibility and access to real-time, community
data is the norm in over 99 percent of all
supply chains. Needless to say, this must
change for an AI tool to be successful.
3. Support for network-wide objective
functions
The objective function, or primary goal, of
the AI engine must be consumer service level
at lowest possible cost. This is because the
end consumer is the only consumer of true
fi nished goods products. If we ignore this fact,
trading partners will not get the full value
that comes from optimising service levels and
cost to serve, which is obviously important as
increased consumer sell-through drives value
for everyone.
A further enrichment of the decision
algorithm should support enterprise level
cross-customer allocation to address product
scarcity issues and individual enterprise
business policies. Thus, AI solutions must
support global consumer-driven objectives
even when faced with constraints within the
supply chain.
4. Decision process must be incremental
and consider the cost of change
Replanning and changing execution plans
across a networked community in real time can
create nervousness in the community. Constant
change without weighing the cost of the
change creates more costs than savings and
reduces the ability to eff ectively execute. An AI
tool must consider trade-off s in terms of cost
of change against incremental benefi ts when
making decisions.
July 2019 | Logistics News
5. Decision process must be continuous,
self-learning and self-monitoring
Data in a multi-party, real-time network is
always changing. Variability and latency are
recurring problems, and execution effi ciency
varies constantly. The AI system must be
looking at the problem continuously, not just
periodically, and should learn as it goes how
to best set its own policies to fi ne-tune its
abilities. Part of the learning process is to
measure the eff ectiveness ‘analytics’, then
apply what it has learned.
6. AI engines must be autonomous
decision-making engines
Signifi cant value can only be achieved if
the algorithm can not only make intelligent
decisions, but can also execute them.
Furthermore, they need to execute not just
within the enterprise, but, where appropriate,
across trading partners. This requires your AI
system and the underlying execution system to
support multi-party execution workfl ows.
7. AI engines must be highly scalable
For the supply chain to be optimised across
an entire networked community of consumers
to suppliers, the system must be able to
process huge volumes of data very quickly.
Large community supply chains can have
millions, if not hundreds of millions, of
stocking locations. AI solutions must be able to
make smart decisions, fast, and on a massive
scale.
8. Must have a way for users to engage
with the system
AI should not operate in a ‘black box’. The
user interface (UI) must give users visibility
to decision criteria, propagation impact and
enable them to understand issues that the AI
system cannot solve. The users, regardless of
type, must to be able to monitor and provide
additional input to override AI decisions when
necessary. However, the AI system must drive
the system itself and only engage the user on
an exception basis, or allow the user to add
new information the AI may not know at the
request of the user. •
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