This leads us to the so-called Poisson distribution. This is crucial for the understanding of many parts of WFM, like forecasting or for safety staffing calculations.
The Poisson distribution is an important assumption underlying the famous Erlang C formula.

In this topic we see where fluctuations in arrivals come from. Please watch the clip and repeat the demo with 100 customers, each having a 0.01 probability of calling. Then submit the resulting Excel sheet.

In this topic we continue the Excel demo and analyse the resulting distribution. Please repeat again the demo and submit the Excel sheet.

In this topic we discuss the practical implications of the theory presented. Please watch the video and then make the final assignment.

•  forecast error = FCE = forecast - actual 
  
•  percentage error = PE = FCE / actual 
  
•  absolute percentage error = APE = absolute value of PE (i.e., without the minus sign)
  
•  mean average percentage error = MAPE = average APE over all forecasted periods
  

Calculate the APE for the overall call volume and the MAPE for every line separate and all lines aggregated.
Additionally, which lines have a forecasting error based on the following rule of thumb: 

• A line has a forecasting error if there are two consecutive periods with an error larger than twice the square root of the forecast
  

• A line has a forecasting error if there are three consecutive periods with an error larger than the square root of the forecast in the same direction.
  

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To obtain the Certificate of Completion your average grade of all topics should be higher than 80%. You can retry every single topic as much as you would like. There is no limit in time or number of tries.