The history of retail forecasting

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From the beginning of retail forecasting to today, explore the technology as it evolves through retail history.

From the beginning of retail forecasting to today, explore the technology as it evolves through retail history.

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  • 1. The history of retail forecasting…
    1
    Entire contents © 2008, Quantum Retail Technology, Inc.
  • 2. 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
    • 3. linear regressions
    • 4. pattern recognition
  • But these were only theories for the problems, they weren’t yet useable on any practical scale.
  • 5. The 70s: The start of retail technology…
    Computing and processing become somewhat affordable
    INFOREM was born
    Pros:
    • Used basic time series forecasting
    • 6. Used “profiles” to govern forecasts
    • 7. Good for predictable environments:
    • 8. Grocery, fast-moving consumer goods
    Cons
    • Very Manual and User Intense
    • 9. Profiles difficult to get right
    • 10. Required lots of processing power
  • It was a lot of work,
    but it was better.
  • 11. 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 released
    SAS: Enterprise time series (ETS)
    Retailers still working with hierarchy
    levels, product groups and averaging
  • 12. Automation made forecasting easier, and it was good.
  • 13. The 90s: The computing revolution…
    Client-server technology allowed for more computing power to be available
    Technology became affordable
    Pros:
    • Scalability and performance was scrutinized
    • 14. Bench-marking became common
    • 15. Pick best / best fit
    • 16. Retek: RDF, SAS: HPF, Teradata/Sterling
    • 17. Automation and optimization components were enhanced
    • 18. Profiling and clustering were used
  • The 90s: The computing revolution…
    But the solutions only slapped band-aids on INFOREM
    Many problems still remained
    Cons:
    • Forecasting was still very manual
    • 19. Retailers were:
    • 20. Constantly redoing seasonal profiles
    • 21. Manually managing lead times
    • 22. Cheating by manually imputing parameters
    • 23. And it still took a lot of computing power to:
    • 24. Crunch algorithms
    • 25. Churn data before time ran out
  • Despite the problems, technology still made retail forecasting faster, and there was much rejoicing.
  • 26. Present day: The unsolved problem
    Item behavior is always changing
    Scientists 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
    • 27. Profiles miss changing behavior
    • 28. Aggregating data
    • 29. Very expensive
    • 30. Not learning from mistakes
    • 31. User intense
  • Traditional forecasting technology is inaccurate and time consuming, it’s not good enough.
  • 32. 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
    • 33. Manage slow selling inventory
    • 34. Maximize your profitability
    • 35. Optimize inventory
    • 36. Forecast accurately without the manual work
    • 37. 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.
  • 38. Yay!
    Yay!
    Yay!
  • 39. Q, the solution for
    your happily ever after.
  • 40. Q, the solution for
    your happily ever after.