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Project Management

Notes changes affect demand, it may be possible to benefit further from this. Understanding how
seasonal factors affect consumers helps businesses position themselves to take advantage.

Notes Data entry errors are one possible source of error that can adversely affect the
demand forecasting efforts.

7.4.2 Forecasting Consumer Demand

Analysis Tools

A wide variety of analysis tools can be used to model consumer demand - from traditional
statistical approaches to neural networks and data mining. Using these demand models enables
estimation of future demand: forecasting. Possibly, a combination of multiple types of modeling
tools may lead to the best forecasts.
Time series analysis is a statistical approach applicable for demand forecasting. This technique
aims to detect patterns in the data and extend those patterns as predictions. The ARIMA model,
or autoregressive integrated moving average, in particular is used both to gain understanding
of the patterns in data and to predict in the series. Different parameters are used to detect linear,
quadratic, and constant trends.
Other approaches for building forecast models are Neural Networks and Data Mining, which
are capable of modeling even very complex relationships in data. Demand forecasting is a very
complex issue for which these methods are well suited. Multilayer Perceptrons and Radial Basis
Function neural networks, Multivariate Adaptive Regression Splines, Machine Learning, and Tree
algorithms can all generate predictive models for this application.

StatSoft has a 35 part video series on data mining that demonstrates many of these approaches
for model building. While the video series mainly uses credit risk data, the series can help with
learning the concepts.
Systematic Patterns vs. Trends

Generally, demand patterns consist of some basic classes of components, seasonality, and trend.
Seasonality refers to the portion of demand fluctuation accounted for by a reoccurring pattern.
The pattern repeats systematically over time. Trend is the portion of behavior that does not
repeat. For example, a trend may show a period of growth followed by a leveling off. In retail
sales, seasonality will likely find patterns that repeat every year. With sufficient data, other
seasonality trends may manifest across multiple years.

Forecasting Techniques

Once adequate predictive models are found, these models can then be used to forecast demand.
A demand forecast model may actually be an ensemble of multiple models working together.
This technique of combining models often results in better predictive accuracy. When one
model gets off track, the ensemble as a whole counteracts.
As more data accumulate about consumer behavior, demand forecast models should be updated.
This will be a continual effort monitoring and modeling demand in order to be constantly
aware of changes. Failing to update forecast models and take advantage of all the information
available will likely prove to be a costly mistake.

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