Uncertainty modelling for extended product lifecycles: Application of a biological analogy to product lifecycle management

Abstract

Product lifecycles are determined at a point in the planning process where there is great uncertainty in future market conditions and drivers for change. Particularly for products with high investment costs and long lifecycles, the period of production may be considerably longer than the change cycle for new technical developments, legislation changes, market conditions, etc. Using internal combustion (IC) engines as an exemplar of products with long planned lifecycles (10- 20 years) and heavy investments (~£200M), a model has been developed to help predict probable, but uncertain, geometry changes in product architecture over expected lifecycles. The model draws on a biological analogy to apply adaptive landscapes to product architecture choices, building in robustness to requirements variation over the life of the product. The model has been applied to historical examples of the evolution of a family of products from first introduction, through to end of production. In this way, actual lifecycle extension, modification and change can be compared to modelled approaches to validate heuristic values to be used in future product planning. The use of adaptive landscapes allows products to be defined in such a way that they are more robust to ill-defined, but reasonably expected changes in product configurations and requirements. Thus, reducing total lifecycle investment costs and allowing products to be more responsive to changed circumstances. Through this process, the lifecycle of products can be extended for minimized cost of change.