Supermarket Perimeter - September 2022 - 71

TECHNOLOGY
preventing food waste and unsold inventory puts
significant pressure on the bottom line in an
increasingly competitive marketplace where margins
are already squeezed. "
Matt Frost, Impulse Logic
Consumers care more than ever before about
food waste in the retail sector.
Artificial Intelligence (AI) and
Machine Learning (ML) are
arming retailers with actionable
insights that enable them
to better match supply with
demand, make efficiency gains,
effectively manage markdowns,
and reduce overstocking.
A good example can be
seen in the UK's grocery tech
giant Ocado. Through the
innovative use of data analytics,
Machine Learning and Artificial
Intelligence, the retailer has
slashed food wastage rates
to just 1 in 6,000 It also uses
advanced forecasting engines
to predict demand to avoid
over-ordering from suppliers.
Meanwhile, ML is also helping
optimize and manage real-time
routing of delivery drivers, with
9.5 million routing calculations
per second.
Retailers like Ocado recognize
the importance of precise
forecasts to help improve
inventory handling and in turn,
reduce waste. Unlike the data
collected in the past - which
often resulted in overwhelming
amounts of data that was not
only too cumbersome to manage
but often the wrong data needed
to make accurate predictions
- today's machine learning
algorithms improve over time
and help retailers understand
the true picture of supply and
demand at a local store level.
Transforming inventory
handling with
machine learning
Forecasting and stock
optimization are among the key
benefits afforded by ML. Here,
smart algorithms determine
the products customers want,
allowing for accurate forecasting
and planning.
This data can be used to
increase efficiencies by ensuring
no excess merchandise is
ordered - or wasted. It also
enables retailers to get a
fully accurate picture of their
inventory and where it is held,
whether on the shop floor or
in the stock room. By matching
supply with demand, retailers
can reduce overstocking and
make informed decisions further
out to accurately fulfil stock
requirements. Additionally, it
allows for an alignment between
ordering and delivery cycles
by understanding the reality of
demand fluctuations.
Accurate inventory handling
can also help reduce the need for
costly markdowns. Unfortunately,
these already form part of a
tried and tested strategy in the
grocery retail tool kit, where
large amounts of perishable
goods account for high levels of
wastage. When markdowns are
unavoidable, ML can offer the
capabilities to identify the optimal
price, helping to protect the
product's margin as it nears its
expiration date. Instead of simply
reducing products by percentage
increments, the technology
is helping retailers determine
the best price based on several
key factors and existing retail
data points.
Moreover, accurate machine
learning is also drastically
reducing the risk retailers take
with promotional offers. A more
accurate outlook on the value
of selected promotions requires
business owners to take a more
granular look at the effects of a
promotion to control spending
and increase profits. Machine
learning is assisting in predicting
the best promotions while
ensuring their activation has an
overall positive impact on the
bottom line of the business. By
predicting the likely outcome
of a promotional offer, store
managers can make informed
decisions on both the promotion
and product price.
ML models are also being
used to study the behavior
of individual shoppers by
recommending promotions for
buyers with similar purchasing
patterns. This is an important
incentive that today sees retailers
supermarketperimeter.com
offering customers the choice of
promotional discounts, based on
the most purchased items within
a set timeframe.
Understanding trends
and making accurate
predictions
When grocery retailers opt to use
ML to improve inventory handling
- the benefits are two-fold.
Product availability is improved,
meaning customers find what
they want and when they need
it, and waste is reduced. This in
turn protects margins and profits.
The uptake is enabling retailers to
create highly accurate forecasts
previously unseen in the sector.
While the retail industry may
never entirely remove waste - an
element of spoilage in fresh and
perishable sectors is inevitable -
it can dramatically reduce it.
As retailers embrace an
intelligent future, powered by AI
and ML, further advancements
can be made. From embracing
accurate sales forecasts to
increasing the focus on customer
service to the technology is here
to help to build a retail revolution
where high levels of waste are a
thing of the past.
For more information on how
Machine Learning can transform
your inventory handling, visit
the Impulse Logic website:
impulselogic.com.
SEPTEMBER 2022 - 71
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Supermarket Perimeter - September 2022

Table of Contents for the Digital Edition of Supermarket Perimeter - September 2022

Supermarket Perimeter - September 2022 - Intro
Supermarket Perimeter - September 2022 - 1
Supermarket Perimeter - September 2022 - 2
Supermarket Perimeter - September 2022 - 3
Supermarket Perimeter - September 2022 - 4
Supermarket Perimeter - September 2022 - 5
Supermarket Perimeter - September 2022 - 6
Supermarket Perimeter - September 2022 - 7
Supermarket Perimeter - September 2022 - 8
Supermarket Perimeter - September 2022 - 9
Supermarket Perimeter - September 2022 - 10
Supermarket Perimeter - September 2022 - 11
Supermarket Perimeter - September 2022 - 12
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