Managing the supply-demand of fresh food products at Retail stores can be possible using Machine Learning. Paper details the effects of social events, natural conditions on supply, demand and pricing of fresh food products in the market.

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I. Introduction
Online retailers like
Bigbasket.com, Grofers.com,
Amazon.in and Godrej’s Nature
Basket are popular due to
availability of wide range of food
products to choose from,
unmatched customer service, fast
delivery of fresh food produce and
most importantly the ability to
reduce the hassles of grocery
shopping. However, traditional
grocery outlets outnumber the
modern retail outlets as they cater
to rural, semi-urban and urban
areas. Furthermore, these outlets
offer products at comparatively
lower prices than most of the
modern retail outlet and provide an
option of credit to their consumers,
unlike the other channels.
Consumers are becoming more
aware of benefits of healthy living
in a hectic world, and the
importance of consuming fresh,
unadulterated produce that
supplement their overall growth
This has increased the demand for
fresh foods– especially fruits,
vegetables, meat, eggs and pulses.
Interestingly, this has increased
competition among retailers to
provide variety of fresh food
products as consumers are
themselves trying out new
channels.
The Government of India has
launched several initiatives to
support production of fresh food
and increasing consumer
awareness by educating consumers
about fresh food. This is expected
to support the forecast
performance for fresh food in India.
So it can be understood that
expansion in retail channels
continues to play vital role in
growth of fresh food. Also the
salubrious trend among people
coupled with government initiatives
drive the need for fresh food.
Among the daily activities
that these outlets perform,
problems like when to order?
How much to order? Is still
difficult to master. This is
mainly because of short shelf
lives of the perishable
products and variability in
demand.
Increase in
Demand
of Fresh
Foods
Multiple
retail
channels
Health
concious
people
Supporting
Govt.
initiatives

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Hence, mismanagement in
maintain stock levels is common.
II. Supply Chain Pain Points
Traditional systems or manual
informed decisions relied heavily
on common sense phenomena to
place orders. In this conventional
approach, when a person makes a
mistake of ordering too many
goods, he tends to overcompensate
the next time by ordering too few.
Quantities based on seasonality are
ordered once the stock level falls
below a certain threshold.
Many a times the cost of
disposing mis-ordered goods can
have a direct impact on business
performance and since sales of
these items is influenced by
multiple factors, the
implementation of such a system is
difficult.
III. Solution Characteristics
There’s a new methodology to
this process that can revolutionize
the demand forecasting and
planning: machine learning. This
model takes into account
uncertainties such as market
outliers, supply chain disruptions,
etc. as well as deviations or
exceptions that may occur.
Business volatility and the
complexity of factors influencing
demand are making it hard to
design a reliable system.
However, machine learning
allows retailers to automate
formerly manual processes as
the system trains itself to
adapt for the upcoming
variations.
Unlike other systems where the
forecasts are obsolete when new
data comes, machine learning is a
type of Artificial Intelligence (AI)
that gives computers the ability to
learn without being explicitly
programmed. These computer
programs teach themselves to
grow and change when exposed to
new data.
The impact of this automation
has the direct impact on revenues-
for instance, reductions in up to 80
percent out-of-stock rates, declines
of more than 10 percent in write-
offs and days of inventory on hand,
and gross-margin increases of up
to 9 percent.

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As a result, the self-learning
technique is driving the force to
implement advanced new systems.
IV. Challenges
The factors that affect supply
chain efficiency include:
 Trade Promotions
It is commonly observed
that retailers fail to guarantee
customer product availability
during promotions. On the
contrary, at times surplus of
food products get spoiled.
 New Products
Predicting demand for a vast
array of new products becomes
reasonably difficult to handle
for demand planner. With
budget constraint addition of
more items makes decision
making complicated.
 Extreme Seasonality
A significant amount of
variability is unseen having
unsatisfied consumers and this
impacts the business in the
long run. In fact, the shelf life
of the fresh food product adds
to the extra care that needs to
be taken to avoid spoilage.
 Too Much Data
Not all systems have the
knack of analyzing and
interpreting huge data banks
and thus only a limited factors
are considered to design
traditional supply chain
systems.
 Media Event Effect
Media event effects are likely
to boost sales in modern retail
stores and similarly affect the
traditional grocery stores.
 Web indicators
Social media monitoring and
data mining can help reveal
consumers sentiments,
shopping experiences and
attached feeling for products.
Machine learning helps identify
trends in advance which might
impact future demand of a
product.
V. Value Creation
Machine Learning does not
rewrite logic, it is evolving in
nature and highly adaptable.
As this technique makes use of
disparate data sources,
retailers will gain complete
visibility of supply chain trends,
enabling to react fast to the
business-relevant information
from the digital noise.
For instance, planners spend
too much time manually
evaluating demand forecasts
and are often not able to
deliver them on time. These
kinds of situations not only
hamper employee morale but
also hamper company’s

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growth. Machine learning can
handle more demand variables
and weighs each of them
according to significance to
provide with forecasts backed
by data.
It is also seen that the safety
stock levels for fresh foods are
kept high to ensure consumer
satisfaction. With machine
learning an optimum level of
stocks is calculated for all kinds
of products. Avoiding spoilage
of safety stocks saves cost.
Much of the losses go
unnoticed in the retail
business, so technological
advancement is essential
If such kinds of situations
resonate with a retailer, it is
high time for them to adopt a
business strategy which strives
to improve on a continual
basis.
VI. Conclusion
Retailers should look to
consider options to enhance
performance management
metrics. The first step is to
create a model that
incorporates the various retail
environment factors for the
store. Next step would require
alignment of business strategy
to integrate the new system.
Eventually, the benefits of this
enterprise-wide process would
be realized as the organization
can clearly visualize its
performance post technology
implementation.
Machine
Learning
Adapted to
incorporate
changes
Cross-
correlation
for Pattern
Recognition
Self-learning
Evolving to
deliver
improving
results
Adjusts to
handle new
item
Uses decision
trees to
discover all
possibilities

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info@embionics.com www.embionics.com +918793064756
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