the history of retail forecasting

Entire contents © 2008, Quantum Retail Technology, Inc. 1Entire contents © 2008, Quantum Retail Technology, Inc.

The history of retail forecasting…

Entire contents © 2008, Quantum Retail Technology, Inc.

In the early days…Highly skilled mathematicians created complex models for different forecasting problems Most were based around time series forecasting

Models:Box-Jenkins, Holt-Winters, Croston

Algorithms could help develop:• Seasonal profiling • linear regressions• pattern recognition


But these were only theories

for the problems, they weren’t yet useable on any practical scale.


The 70s: The start of retail technology…

Computing and processing become somewhat affordableINFOREM was born

Pros:• Used basic time series forecasting • Used “profiles” to govern forecasts• Good for predictable environments:• Grocery, fast-moving consumer goods

Cons• Very Manual and User Intense• Profiles difficult to get right• Required lots of processing power


It was a lot of work,

but it was better.


The 80s: Retail technology became faster…

New models started to go outside of time series forecasting

Retailers built automation components around INFOREM

More robust tools became available:E3: The next generation of INFOREM was releasedSAS: Enterprise time series (ETS)

Retailers still working with hierarchy levels, product groups and averaging


Automation made forecasting

easier, and it was good.


The 90s: The computing revolution…

Client-server technology allowed for more computing power to be availableTechnology became affordable

Pros:• Scalability and performance was scrutinized• Bench-marking became common• Pick best / best fit• Retek: RDF, SAS: HPF, Teradata/Sterling

• Automation and optimization components were enhanced•Profiling and clustering were used


The 90s: The computing revolution…

But the solutions only slapped band-aids on INFOREMMany problems still remained

Cons:• Forecasting was still very manual

• Retailers were:• Constantly redoing seasonal profiles• Manually managing lead times• Cheating by manually imputing parameters

• And it still took a lot of computing power to: • Crunch algorithms• Churn data before time ran out


Despite the problems, technology still made retail forecasting faster,

and there was much rejoicing.


Present day: The unsolved problem

Item behavior is always changingScientists still have not progressed their understanding of the data to forecast accurately

80-90% of products are slow movers

• Sparsity of data causes sub-optimal accuracy• Profiles miss changing behavior• Aggregating data• Very expensive• Not learning from mistakes• User intense


Traditional forecasting technology is inaccurate and time consuming,

it’s not good enough.


The next chapter: Quantum Retail Technology

Introducing: Q Now you can have the ability to create several types of profiles for every item at every location• Understand multiple dimensions of item behavior• Manage slow selling inventory• Maximize your profitability • Optimize inventory • Forecast accurately without the manual work• Collect and react to data automatically in real time


And then one day there was no more lumping and no more smoothing,

and retail forecasting was always clear.


Yay!Yay! Yay!


Q, the solution for your happily ever after.

the history of retail forecasting

Technology

quantum retail technology

complex models

different forecasting

time series forecastingmodels

seasonal profiling

linear regressions

pattern recognition