Webinar on call center forecasting: Why a forecast …...Why do we forecast? •AllWFM processesstart with forecasting •Bad FC leads to: •More ad hoc decisions •(Structural)

Webinar on call center forecasting:Why a forecast accuracy of 100%

is sheer luckMarch 20, 2020Ger Koole, PhD

Why do we forecast?

• All WFM processes start withforecasting• Bad FC leads to:• More ad hoc decisions• (Structural) under or overstaffing• Higher costs• Bad service levels• Abandonments• Less sales• …

• Garbage in = garbage out Shor

t-ter

m

lo

ng-te

rm

Forecasting

Capa

city

plan

ning

Agen

t sc

hedu

ling

Real

-tim

e pe

rform

ance

m

anag

emen

t

budget

hiring decisions

What drives actuals?

• To be forecasted: actuals, handling times, sick leave, ….• Actuals are impacted by:• predictable intra-day, intra-week and intra-year fluctuations (seasonality)• (long-term) trend• events (holidays, marketing, etc.)• unpredictable noise

• Impact seasonality & repeating events can be accurately predicted• Short-term trend as well• How about noise? What is it??

Drilling down in granularityfrom intra-year … to intra-week … to intra-day … to intra-hour

Repeats itself: predictable

Repeats itself: predictable

Repeats itself: predictable

Unpredictable?

The origin of noise

• Flip a coin 20 times…• How many times heads?• Excel demo

• Flip a biased coin (success rate 0.0005%) 2M times…• How many times heads?

Binomial distribution Poisson distribution

Poisson noise

• Number of arrivals for many customers behaving independently• Unexplained part of volume• Explainable part depends on seasonality,

trend, events: can be forecasted• “Discovered” by S.D. Poisson• Quantifies noise• Which part of error comes from noise?

SD Poisson (1781-1840)

5% 15% 22% 22% 17% 10% 5% 2% 1% 0.3% 0.1%

Mean = 3

Measuring accuracy

• Error = Forecast – actual, measured over multiple periods• Period can be of different length: quarter, day, week, …

• Goal: reduce errors to a single number• “the overall daily FC error of March was 5%”

• One period should not compensate the other • Sum of errors = 0, accuracy = 100%? NO• Solution: use squares or absolute values • Measures based on squares are hard to interpret:

“the RMSE is 12.9”• Measure needs to be relative to size: “5%” instead of

“12.9”• Solution = WAPE = Weighted Absolute Percentage Error• WAPE = sum of absolute errors / sum of actuals =

(20+0+10) / (80+100+110) = 10.3%

actuals

forecast

error 20 0 -10

Squares 400 0 100

Abs values 20 0 10

Quantifying noise

• WAPE can be split in part due to FC error and noise• Which part is due to noise?• What is the noise of a perfect FC?• Answer: minimal APE = √ (2/(FC π))• E.g., √ (2/(100 π)) = 8%• √ (2/(1000 π)) = 2.5%• Smaller for big volumes

• Minimal WAPE can be estimated using actuals

WAPE = 23%

WAPE = 36%

Minimal WAPE = 25%

Consequences

• Small volumes cannot be predicted accurately• Always FC errors, minWAPE gives minimal error• Most relevant for interval-level forecasts• Smart method needed for intra-day FC = splines

• SL is equally hard to predict• Also SL ”errors” and/or real-time performance

management• But: Erlang C/X* or simulations take Poisson

fluctuations as input

“Strive for five” is often infeasible at interval-level

* see https://www.ccmath.com/online-calculators/

Learning more

• www.wfmfellowship.com• Online trainings for

workforce management• At you own time & place• Videos, exercises,

assignments • Supported by CCmath

professionals• Subscribe online or at

[email protected]

Forecasting in practice

• CCforecast by CCmath• Cloud-based call center

forecasting tool• Own dedicated CCmath

algorithms• Automatic parameter setting• Any volume, any number of lines• Consistently outperforms Excel• Reduces time up to 90%

Next week

• Why is Erlang C unsuitable for call center safety staffing?• Friday March 27, 16:00 AMS time

• Questions? Chat or voice