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
request form giving the course(s) of interest and the expected number of
delegates. Alternatively, feel free to email us at
Knowledge of a wide range of demand forecasting
methods for short term and medium term
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:
Forecast accuracy measurement
and the most effective measures to use in accuracy reporting. A worked example is
given to illustrate the most usual methods.
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.
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.
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
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.
If the company operates in a consumer goods
market, the availability of EPOS data and its
potential use in forecasting and planning.
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.
The six steps in a typical demand forecasting
process and consideration of best practice in forecasting for
the specific company.
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