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Intermittent Demand Forecasting - Context, methods and applications
Inglese
203 b
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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); and the methods and approaches used in forecasting intermittent demands is relevant to many industries such as the military, aerospace, automotive, and information technology.
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