features were tested in the field trial: a fridge/freezer and a consumer entertainment
product (Simon et al., 2004a, b).
The work in this paper grew out of the need of manufacturers of large numbers of
products to predict the future costs of any changes in the patterns of product life – for
example, to estimate the effect on maintenance costs of a certain reliability, or to
estimate the numbers of spare parts needed – or indeed, the number of failed parts that
might be remanufactured as spares.
The life cycle of a product is an uncertain thing – a new model is sold over a period,
used at different intensities and fails randomly, with typical failure rates increasing as
the product nears the end of its life. The demand for spares and the cost of repairs
under guarantee are two typical quantities that producers want to be able to predict.
The research aimed to develop a dynamic model, to enable “what if” modelling of
scenarios of the future use of the ELIMA data management system, predicting flows
of products through their life, the data arising from them and maintenance costs.
With this model, a comparison of the products with and without the new system
features can be made, and the benefits from the system can be quantified and assessed.
In particular, the aim of this research is modelling of component and product
reliability, availability and maintenance to track products during the life cycle period
and generating of an estimate of arisings of spare parts and products at the repair and
end-of life stages, linked to life cycle costing