Intermittent Demand Forecasting - Context, methods and applications

Intermittent Demand Forecasting - Context, methods and applications covers the entire breadth of work in intermittent demand forecasting and the very latest research findings in the following topics:

• time series (parametric) methods
• bootstrapping, both parametric and non-parametric
• causal models
• neural networks

In order to enable a full understanding of how these methods may be implemented and how they may complement existing operational functions, other chapters include:

• demand classification (what exactly constitutes intermittence and how it may be distinguished from other patterns in operational terms)
• measures for assessing intermittent demand forecasting accuracy
• interactions between forecasting and stock control
• qualitative considerations (soft managerial issues that should be taken into account)

The methods and approaches used in forecasting intermittent demands is relevant to many industries such as the military, aerospace, automotive, and information technology.