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Are you looking for a
forecasting solution that is easy to use and saves
you a lot of time, yet improves forecasting reliability and
accuracy? Are you spending more time manipulating parameters in
forecasting models than actually producing results? Don’t you wish
you could get the results you need more
accurately... inexpensively... and
faster and easier? Now you can with the
PEERForecaster Add-in for
Excel!
If you are spending a
lot of time and money manipulating forecasting models with your
large-scale demand planning systems without getting reliable
feedback and simple answers, then we may be in a position to help
you get those analyses done more quickly and with greater
reliability.
Forecasting
technology has improved so much in recent years
that most demand forecasting software providers, especially the
‘larger players’, may have fallen behind in providing improved
solutions to their clients.
Delphus is offering
PEERForecaster, an Excel Add-in with all the
horsepower of a full-fledged forecast modeling tool without the
overhead commonly associated with many forecasting solutions. The
models include all the well-known techniques from simple smoothing,
Holt trending, Holt-Winters seasonal models, and damped trend
exponential smoothing models to the Box Jenkins ARIMA models. The
algorithms and model interpretations are documented in the book
entitled Forecasting:
Practice and Process for Demand Management coauthored by
Delphus' Hans
Levenbach
A
complimentary copy of this forecasting book is also provided at
no charge when you enroll in the CPDF® certification
curriculum for demand planners. The CPDF (Basic) Workshop is a
hands-on workshop that utilizes Excel Add-ins along with
PEERForecaster to re-enforce the modeling capabilities of
seasonal decomposition, exponential smoothing, ARIMA models and the
State Space modeling environment. You will learn how to use
techniques that have been proven superior to the conventional Holt
and Holt-Winters models.
The international M3
forecasting competition has established that the family of
"damped trend" models generally
outperform the more conventional models used for
forecasting trends and seasonality in historical data. This
information has been published in the peer-reviewed International
Journal of Forecasting. Yet, these models are rarely, if ever,
found in many of the mainstream ERP/SCM demand planning systems
available in the market today! | 
