Forecast Solutions

Advanced Forecasting Course - 1 day forecasting training

This 1 day course is an optional follow-up to the Introduction to Sales Forecasting.  It includes forecast accuracy measurement, causal modelling, additional time series forecasting methods and forecasting process.  The full range of exponential forecasting models is explained including the forecast.ets function in Excel.  The course explores forecast model selection and optimisation and covers the alternative ways that specialist software may function in this regard.  

Who should attend:

The advanced course is suitable for those looking to gain an in-depth understanding of demand forecasting methods and process.  There is a good amount of detail on a number of forecasting methods with examples in Excel, so a good familiarity with Excel is beneficial.  Delegates come from many different industry sectors spanning consumer goods, pharmaceuticals, high tech, financial sevices and industrial sectors.


For prices please complete a quotation request form giving the course(s) of interest and the expected number of delegates.  Alternatively, feel free to email us at

Learning Outcomes:

  • Knowledge of a wide range of demand forecasting methods for short term and medium term horizons.

  • Understanding of how a suitable demand forecasting model can be chosen and it's parameters calibrated.

  • Sufficient insight to support consideration of potential improvements to the company's existing demand planning methods and procedures.

Typical Course Content:

  1. Forecast accuracy measurement and the most effective measures to use in accuracy reporting.  A worked example is given to illustrate the most usual methods.

  2. Causal modelling to quantify the effects of drive factors such as pricing, weather and economic indices.  Discussion around software for carrying out causal analysis and the possibility of implementation in Excel.

  3. Further examples of time series forecasting with exponential smoothing including some worked examples and explanation of the full range of possibilities in this family of methods.  The forecast.ets function in Excel.

  4. The need to cleanse abnormal historical data that may be due to promotions, out-of-stock or other events.  Different ways of dealing with outliers and discussion on facilities that may exist in specialist forecasting software.

  5. Selection and optimisation of demand forecasting models with a worked example on an exponential smoothing method using the Solver add-in in Excel.  Alternative ways in which specialist forecasting software may deal with model selection.

  6. If the company operates in a consumer goods market, the availability of EPOS data and its potential use in forecasting and planning.

  7. Forecasting in the context of a sales and operations planning process.  Considerations around demand forecasting for the medium term as opposed to short term horizon.

  8. The six steps in a typical demand forecasting process and consideration of best practice in forecasting for the specific company.

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