Page 5 - Logistics News June 2019

Thought Leadership
making data availability an essential foundation
to a successful AI implementation. Internally,
companies already struggle to maintain
accurate data, even for the most basic of
elements such as product code.
In addition to internal data, a good demand
plan also requires external data in the form of
market intelligence, such as competitor actions,
customer behaviours and trade disruptions like
price changes and sell-out data.
Furthermore, all of this data needs to be
interpreted correctly. For example, in order to build
a correct demand plan, an accurate baseline for
demand must be established. Once-off events,
such as service issues and one-time promotions,
have to be identifi ed and accounted for, otherwise
they may skew the planner’s understanding of the
underlying demand. This initial step of cleansing
the data for statistical treatment is often a critical
source of error as it requires a clean view of the
history of past activity of the product. In order
for an AI application to learn from these once-off
events they would need to be fully understood and
coded.
In addition to these data challenges, many
companies today struggle with their digital
culture and level of savviness. Most face the
same struggle: Their planners prefer to build
demand plans in Excel fi rst, and then upload
them into the expensive, integrated demand
planning tools they must use to propagate their
demand plans. The usual explanation for this
resistance is that the tools don’t have enough
of the internal and external contextual data to
build pertinent statistical plans.
The absence of data, resistance to using the
existing suite of statistical tools and low level
of digital savviness represent non-negligible
challenges to the deployment of AI-enabled
demand planning.
The need for one set of numbers
Demand planning is a critical activity in the
sales and operations planning (S&OP) process.
The objective of S&OP is to obtain alignment
from all actors in the company, ideally ensuring
that operations mobilises its resources to supply
what the business needs to meet its fi nancial
goals, while also ensuring that the fi nancial
goals account for the current operational
constraints.
A fundamental pillar of the S&OP process is
the notion of ‘one set of numbers’, which means
that operations and fi nance are working off a
shared understanding of the forward planned
activity for the business. The primary drivers
for this goal are that no market opportunities
June 2019 | Logistics News
are missed due to a supply/demand imbalance,
and operations is focused on the true business
needs rather than on an infl ated demand that
acts as a supplemental safety stock.
When a company ties its fi nancial plan to
the operational plan, general managers are
driven to involve themselves – along with
their commercial and marketing teams – in
the demand planning process in order to have
the most viable demand plan possible. Their
involvement in the planning process is critical,
as they can provide the demand planners
with the valuable external market intelligence
mentioned earlier. Just as importantly, from
a managerial perspective, having one set of
numbers means that any eff ort by general
managers to manipulate the demand plan would
also change the fi nancial pan, which they are
loath to do as it constitutes their commitment
to executive leadership.
When AI is used to generate a demand plan,
that demand plan becomes part of the ‘one
set of numbers’. Otherwise general managers
would be tempted to return to old refl exes, such
as considering the demand plan outside their
sphere of interest, not being as committed to
providing demand planners with the necessary
external data and market intelligence, and
perhaps once again adjusting the numbers to
their subjective tastes. But maintaining the tie
between the AI-generated demand plan and
the fi nancial plan would require asking general
managers to allow their fi nancial projections to
be generated by the AI application. This would
be a consequential management hurdle for
supply chains to overcome.
That’s because the introduction of AI-
generated demand plans would bring with it
what is termed the ‘explainability problem’ of
AI. This term describes the reluctance managers
have to using AI applications that seem like a
‘black box’, where the reasoning and logic used
to obtain the results are diffi cult to explain, even
if they are of high quality.
The explainability problem doesn’t preclude
the use of AI for demand planning, but it
does suggest that it be considered only for
companies that have achieved very high
S&OP maturity and integration between the
operational and fi nancial planning activities.
This maturity would likely correspond with both
more digitally savvy demand planners and a
higher confi dence of general managers in the
ability of the demand planners to provide an
AI-generated demand plan that represents the
most accurate view of the forward business
activity. •
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