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David Wiegandon 4 November 2013
Transcript of Lemonade Stand
Rosie opened an organic lemonade stand in 2011. She did really well, but Rosie thought she could do better.
For instance, Sometimes Rosie didn't buy enough ingredients...
Other times, she
bought too much...
The orange line represents
Rosie's lemonade inventory
The blue line represents lemonade sold
Here, we see Rosie produced
a lot more lemonade than she needed
resulting in wasted production
Here we see where Rosie didn't make
enough lemonade resulting in a loss of
So, Rosie asked
TruQua to help
her with the
With this new-found knowledge
Rosie got to work.
Rosie had three lemonade stands to experiment
with in 2012. Her lemonade stands were in three locations in the neighborhood of Sunnyville.
Butter Cup Street
Astor Street would run a very simple strategy: inventory would be reduced everyday to prevent over production, and an extra 2 gallons of lemonade would be produced whenever a promotion would be run.
On Clover Street Rosie employed predictive analytics in real time. On top of the strategies used by Butter Cup, Clover would leverage SAP HANA to incorporate the following variables in real time:
Daily sales data would be included in the forecast to increase sales accuracy
Current weather forecast would be incorporated, instead of average temperatures
The predictive model's training set would be updated every day to get gradually better estimates
Here we have Rosie's data from 2011 showing over production as well as missed sales opportunities.
Here we have 2012 data from Astor Street, where Rosie employed her simple strategy of reducing production overall except when she ran promotions.
With these plots side by side we can see an increase in accuracy between production and sales as different strategies are employed. Rosie's production became more accurate as she moved from a lemonade stand employing simple inventory reduction strategy, to employing a predictive strategy, and finally a predictive in real-time strategy which yielded the most accurate results.
Here we have Butter Cup Street where Rosie is using predictive. Here we see even closer matching.
Last we have Clover Street, which employed real-time analytic powered by HANA. Here we see Sales and Inventory even more closely matched.
Better accuracy means:
Rosie is happy,
her customers are happy,
Increase in temperature
Increase in sales
and Rosie increases her profit....
Here we see Astor
has the highest earnings,
then Butter Cup, and then
Clover with the lowest.
But this isn't the whole story...
Along with the greatest earnings, Astor has the greatest spend, followed by Butter Cup then Clover.
Which leads to the bottom line.
TruQua first took a look
at Rosie's 2011 data.
On Butter Cup Street Rosie decided to use predictive analytics to reduce the gap between her production and sales. Rosie discovered the following variables influenced sales:
Hotter temperature increased sales
Special events, such as local festivals increased sales
Sales increased on weekends
Promotions increased her sales
Also, Rosie makes her entire inventory fresh each morning and all remaining lemonade is discarded at the end of the day to ensure she has the freshest lemonade in the neighborhood.
Note that each day her entire inventory
needs to be produced, and at the end of each day, it's discarded
Here we have a chart of all three lemonade stand's earnings, costs and net profit in dollars.
This made over-production very costly.
Clover's real-time predictive analytics allowed Clover to spend less and make high-return sales which ultimately lead to Clover having the greatest net profit