Purpose – To provide an overview of how a number of frequently used smoothing-based forecasting techniques can be modelled for use in dynamic analysis of production-inventory systems.

Design/methodology/approach – The smoothing techniques are modelled using transfer functions and state space representation. Basic control theory is used for analysing the dynamic properties.

Findings – A set of expressions are derived for the smoothing techniques and dynamic properties are identified.

Practical implications – Dynamic properties are important in many applications. It is shown that the different smoothing techniques can have very different influences on the dynamic behaviour and therefore should be considered as a factor when smoothing parameters are decided on.

Originality/value – Dynamic behaviour of production-inventory systems can be analysed using control theory based on, e.g. transfer functions or state space models. In this paper a set of models for five common smoothing techniques are analysed and their respective dynamic properties are highlighted.