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whether it's lost sales from not having
product when customers need it
or increase costs from excess inventory
leading to discounting and spoilage
how much are you losing from inaccurate
demand forecasting
mckinsey and company estimate a 10 to 20
improvement in forecasting accuracy
leads to a 5
reduction in inventory costs and a two
to three percent increase in revenues
but while demand forecasting is core to
supply chain operations
forecasting strategies have lagged
today's competitive landscape
fickle customer expectations and macro
volatility
let's take for example a retailer with 4
000 stores
40 distribution centers and 50 000 items
building item level forecasts at the
distribution center level requires 2
million forecast iterations
if we went to the store level that
number would jump to 200 million
iterations
so where ideally you would run these
forecasts per item per store per day
provisioning the resources to generate
these many forecasts was cost
prohibitive
instead you would aggregate to the item
category level per region per week
or even month while this approach
delivers forecasts in a timely manner
it comes at the cost of accuracy and
across industries whether it was
retailers and manufacturers estimating
consumer demand
or hotel chains and hospitals predicting
room and bed availability
this was the trade-off nearly every
enterprise had to make due to three main
technical limitations
first legacy tools built on data
warehouse architectures use serialized
processes to calculate forecasts
if you just calculate promotion groups
not individual items at a weekly level
for 40 distribution centers
that is 40 000 iterations of a
statistical model running
every week at a day store and item level
that's now 1.4 billion iterations of
that forecasting model every week
or 200 million iterations per day second
traditional forecasting tools are not
built to bring in additional data like
foot traffic
and weather to further localize models
they have fairly rigid approaches
and custom tools require a lot of data
engineering work to extract this data
before integrating it into the model
and finally in order to deliver
forecasts in time for supply
chain operations legacy approaches based
on data warehouse architectures had to
trade granularity
for timeliness with a serialized
methodology you just can't deliver a
large volume of iterations in a
cost-effective and timely manner
even 40 000 iterations within the
service window meant running forecasts
for only the critical categories once a
week or once a month
and looking ahead at a longer time
horizon further degrading accuracy
as a result product allocation happened
in a manner ignoring
product to store specific patterns we
hope to capture
introducing the data bricks demand
forecasting solution accelerator
a deep dive into this accelerator is
provided in the blog linked in the
description below while the accelerator
is relevant for a range of industries
from
health care to manufacturing to
hospitality the blog includes code built
off publicly available data sets to work
through these patterns on your own
before attempting to apply them to your
organization's forecasting challenges
this accelerator uses sales transaction
history at the day
location and item level to build the
model that scales to provide granular
forecasts down to the store and item
level
and by parallelizing the process in the
cloud it gives you more accurate and
granular forecasts much faster than
traditional forecasting methods
databricks customers have been able to
go from regional to store and item level
forecasting while cutting the amount of
time required to run the forecasts by 50
or more for example consumer goods giant
rb
now runs forecasts twice as fast while
serving 10 times more stores using data
bricks
and when it comes to speed it's not just
in terms of running simulations faster
but also
speed to market with this solution
accelerator a single data scientist can
stand up a poc
in two weeks these pre-built notebooks
are based on best practices from
similar methods in production your
developers just work on plugging in your
own data into the engine and then
feeding it to the downstream business
teams for reporting
analytics and data science what's
important to note is that this
accelerator does not replace your
existing erp
point of sales inventory crm
supply chain management or warehouse
management systems and processes
rather we take those same systems and
processes and make them much
smarter by moving from hundreds of
thousands to billions of iterations
increasing the level of detail and
accuracy of demand forecast
the beauty of the approach in our demand
forecasting solution accelerator
is that we can leverage the same
forecasting techniques as before but
scale
it to get our work done in a timely
manner without compromising on accuracy
that is we are able to scale our
iterations 100 or 1000
x in a tenth of the time generating more
accurate forecasts
faster than before we have a highly
performant architecture using delta lake
to optimize the data for querying and
the distributed power of apache spark to
scale these jobs out
as you increase scale and granularity
while moving
faster to respond to real-time
conditions at the local level
you can optimize profitability and
capital allocation by stocking the right
items your customers want
when they want without wasting space for
items that need to be discounted or
disposed to stay up to date on our
latest solution accelerators
visit our solution accelerator hub