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The history of retail forecasting…<br />1<br />Entire contents © 2008, Quantum Retail Technology, Inc. <br />
In the early days…<br />Highly skilled mathematicians created complex models for different forecasting problems <br />Most...
 linear regressions
 pattern recognition </li></li></ul><li>But these were only theories for the problems, they weren’t yet useable on any pra...
The 70s: The start of retail technology…<br />Computing and processing become somewhat affordable<br />INFOREM was born<br...
 Used “profiles” to govern forecasts
 Good for predictable environments:
Grocery, fast-moving consumer goods</li></ul>Cons<br /><ul><li> Very Manual and User Intense
 Profiles difficult to get right
 Required lots of processing power</li></li></ul><li>It was a lot of work, <br />but it was better.<br />
The 80s: Retail technology became faster…<br />New models started to go outside of time series forecasting<br />Retailers ...
Automation made forecasting easier, and it was good.<br />
The 90s: The computing revolution…<br />Client-server technology allowed for more computing power to be available<br />Tec...
 Bench-marking became common
 Pick best / best fit
Retek: RDF, SAS: HPF, Teradata/Sterling
 Automation and optimization components were enhanced
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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.

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Transcript of "The history of retail forecasting"

  1. 1. The history of retail forecasting…<br />1<br />Entire contents © 2008, Quantum Retail Technology, Inc. <br />
  2. 2. In the early days…<br />Highly skilled mathematicians created complex models for different forecasting problems <br />Most were based around time series forecasting<br />Models:<br />Box-Jenkins, Holt-Winters, Croston<br />Algorithms could help develop:<br /><ul><li> Seasonal profiling
  3. 3. linear regressions
  4. 4. pattern recognition </li></li></ul><li>But these were only theories for the problems, they weren’t yet useable on any practical scale.<br />
  5. 5. The 70s: The start of retail technology…<br />Computing and processing become somewhat affordable<br />INFOREM was born<br />Pros:<br /><ul><li> Used basic time series forecasting
  6. 6. Used “profiles” to govern forecasts
  7. 7. Good for predictable environments:
  8. 8. Grocery, fast-moving consumer goods</li></ul>Cons<br /><ul><li> Very Manual and User Intense
  9. 9. Profiles difficult to get right
  10. 10. Required lots of processing power</li></li></ul><li>It was a lot of work, <br />but it was better.<br />
  11. 11. The 80s: Retail technology became faster…<br />New models started to go outside of time series forecasting<br />Retailers built automation components around INFOREM<br />More robust tools became available:<br />E3: The next generation of INFOREM <br />was released<br />SAS: Enterprise time series (ETS)<br />Retailers still working with hierarchy <br />levels, product groups and averaging<br />
  12. 12. Automation made forecasting easier, and it was good.<br />
  13. 13. The 90s: The computing revolution…<br />Client-server technology allowed for more computing power to be available<br />Technology became affordable<br />Pros:<br /><ul><li> Scalability and performance was scrutinized
  14. 14. Bench-marking became common
  15. 15. Pick best / best fit
  16. 16. Retek: RDF, SAS: HPF, Teradata/Sterling
  17. 17. Automation and optimization components were enhanced
  18. 18. Profiling and clustering were used</li></li></ul><li>The 90s: The computing revolution…<br />But the solutions only slapped band-aids on INFOREM<br />Many problems still remained<br />Cons:<br /><ul><li> Forecasting was still very manual
  19. 19. Retailers were:
  20. 20. Constantly redoing seasonal profiles
  21. 21. Manually managing lead times
  22. 22. Cheating by manually imputing parameters
  23. 23. And it still took a lot of computing power to:
  24. 24. Crunch algorithms
  25. 25. Churn data before time ran out</li></li></ul><li>Despite the problems, technology still made retail forecasting faster, and there was much rejoicing.<br />
  26. 26. Present day: The unsolved problem<br />Item behavior is always changing<br />Scientists still have not progressed their understanding of the data to forecast accurately<br />80-90% of products are slow movers<br /><ul><li> Sparsity of data causes sub-optimal accuracy
  27. 27. Profiles miss changing behavior
  28. 28. Aggregating data
  29. 29. Very expensive
  30. 30. Not learning from mistakes
  31. 31. User intense</li></li></ul><li>Traditional forecasting technology is inaccurate and time consuming, it’s not good enough.<br />
  32. 32. The next chapter: Quantum Retail Technology<br />Introducing: Q <br />Now you can have the ability to create several types of profiles for every item at every location<br /><ul><li> Understand multiple dimensions of item behavior
  33. 33. Manage slow selling inventory
  34. 34. Maximize your profitability
  35. 35. Optimize inventory
  36. 36. Forecast accurately without the manual work
  37. 37. Collect and react to data automatically in real time</li></li></ul><li>And then one day there was no more lumping and no more smoothing, and retail forecasting was always clear.<br />
  38. 38. Yay!<br />Yay!<br />Yay!<br />
  39. 39. Q, the solution for<br />your happily ever after.<br />
  40. 40. Q, the solution for<br />your happily ever after.<br />
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