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Drops By Month. Drops By Week. Drops By Day Of Week. Weekends have low volumes. Errors in Daily Forecast by Day of Week. Weekends are hard to forecast. Bad Days. A Bad Day is… When drops exceed forecast by more than 20% (say) Two kinds of Bad Days

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Presentation Transcript


Drops by day of week
Drops By Day Of Week

Weekends have low volumes


Errors in daily forecast by day of week
Errors in Daily Forecast by Day of Week

Weekends are hard to forecast


Bad days
Bad Days

  • A Bad Day is…

    • When drops exceed forecast by more than 20% (say)

  • Two kinds of Bad Days

    • High Days: When Drops exceed Forecast by more than 20%

    • Low Days: When Drops fall short of Forecast by more than 20%


Example
Example

  • Compare the fraction of forecasted drops seen each hour of a “good” day with the fraction seen on “bad” days

  • Question: Can we determine early when a bad day is coming?

  • Next Slide has four charts: (view in Presentation mode)

    • Good Fridays

    • High Fridays

    • Low Fridays

    • Good Fridays again



A test
A Test

  • If By 7 am

    • More than 5 times the volume we forecasted has dropped, anticipate a High Day

    • Less then 10% of the volume we forecasted has dropped, anticipate a Low Day

    • Otherwise, expect a Good Day



Waiting to decide
Waiting to Decide

  • If By 9 am

    • More than 10 times the volume we forecasted has dropped, anticipate a High Day

    • Less then 5% of the volume we forecasted has dropped, anticipate a Low Day

    • Otherwise, expect a Good Day



Waiting to decide1
Waiting to Decide

  • If By 11 am

    • More than 50% higher volume than we forecasted has dropped, anticipate a High Day

    • Less then 50% of the volume we forecasted has dropped, anticipate a Low Day

    • Otherwise, expect a Good Day



Conclusions
Conclusions

  • Forecasts by Month and Week are quite good

  • Daily Forecasts are less reliable, especially on Mondays and Weekends

  • Focus labor flexibility around those days

  • As the day progresses, we can make increasingly good predictions about the rest of the day’s demand

  • There seems to be an opportunity to develop intelligent staffing strategies that use this information


Question
Question

  • As the day progresses, we…

    • Get better information

    • Incur more sunk costs

    • Lose flexibility

  • How much can be gained by quantifying and trading off these factors?


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