Spares Prediction

The principal reason for carrying spares is to mitigate the consequences of equipment failure. The process of spares stockholding prediction is therefore a logical extension of Reliability-centred Maintenance (RCM) activities. The consequence of a spare part stockout is intrinsically linked with the time taken to replenish that part, which in turn affects the operational availability of the host equipment.

Increasing the number of spare parts improves availability but also increases costs, suggesting that a defensible spares stockholding regime must be based on a balance between cost and asset availability. Spare part cost is a combination of capital cost, logistic and stockholding costs. The required asset availability is based on the value of platform-level operational availability, flowed down via the functional hierarchy to the asset in question.

Any cost-effective spares stockholding regime must also take account of the opportunities for spares rationalisation across a number of equipments (or, indeed, across the complete platform), requiring a knowledge of the hierarchical asset structure and of the asset management plan for the equipment(s). Both aspects are an inherent part of the RCM process.

Rmada has considerable experience with both the RCM process (which identifies the range of spares necessary) and with a number of optimising processes that determine the scale of spares required, taking account of the rationalisation issues above. These scaling processes include Reliability-centred Stockholding (RCS) and a derivative of that process based on genetic algorithm (GA) methods.

Such GA methods have been successfully used to determine the on-board spares stockholding requirements for a number of predominantly electronic ship systems where condition monitoring to forecast failure was not technically feasible. In such cases, comparisons between GA-predicted and actual stockholdings of carried onboard spares suggested significant cost reductions, together with improved availability.

Summary: a review of the functional requirements of the host system followed by an analysis of spares necessary to support these functional requirements, capitalised on system redundancy features (compared with a simple parts count) and provided a more appropriate spares inventory.  This offered improved system availability and at a reduced inventory cost.