Sonum International

Automated Statistical Forecasting

Partner type

Gold

Function

Finance, Sales, Supply chain

Industry

Business Services, Consumer Goods & Retail, Consumer Products, Energy & Resources, Hospitality, Insurance, Life Sciences & Healthcare, Retail, Technology & Software, Telecom

Region

America, Asia Pacific, Europe, Global, Middle East

With today’s S&OP challenges, companies are looking for ways to make the demand forecast numbers as accurate as possible, to be able to provide great service levels at the lowest cost. This can be accomplished with automated statistical forecasting, where the system generates the most optimal forecast numbers without any human input needed. The time you gain from having this system in place opens the door to spending more time on high-value strategic decision-making parts of the process. The model also allows checking the actuals against the previous forecast numbers and analyzing how the forecast numbers can be improved.

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Statistical forecasting methods and features

Several statistical forecast methodologies can be implemented in Anaplan and are currently available in the application. Each forecast methodology contains at least two of the listed features below:

  • Baseline
  • Irregular fluctuations
  • Trend
  • Cyclicity
  • Seasonality

Statistical forecast methodologies:

  • Regression (linear, logarithmic, exponential, power)
  • Historical average/moving average (single, double)
  • Driver-based forecast
  • Exponential smoothing (simple, double, triple)
  • Holt’s linear trend
  • Decomposition (additive, multiplicative/linear/logarithmic, exponential, power)
  • Winter’s additive/mulitplicative

Solution features

  • Features of the automated statistical forecast application
  • Forecast methodologies with associated parameters.
  • Forecast scenarios for comparison.
  • Forecast features adapt to forecast needs.
  • Selecting forecast methods based on maximizing performance and minimizing variance.
  • Visualization of all forecasting techniques with the possible parameters.
  • Visualization of the optimal parameters vs. the actuals.
  • Comparison of loaded actuals vs. previous forecast scenarios (margin %) to anticipate GAPs that can be explained.