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Finding the optimal spares strategy for rail vehicles

Finding the optimal spares strategy for rail vehicles

01/11/2011 | Channel: Infrastructure, Rolling Stock, Business Improvement

What assortment of components and spares will be needed for a train operation when the
low-frequency maintenance starts? How can the choice of maintenance strategy possibly impact on the size of this investment? These are questions that all fleet operators and owners face from time to time, says Systecon’s HÅKAN BORGSTRÖM

The following example, based on a real situation, describes how an operator is revising its current spares management practice with the ambition to develop a new spares strategy. A central part of the new strategy is to implement the leading spares optimisation software OPUS10™ as the standard analysis tool throughout the organisation. OPUS10 is not limited to calculating the optimal assortment and location of spares, it also offers extensive analysis functionality that provides key input to strategic decision makers. In addition to this, OPUS10 will also be used to recommend a consignment stock for 15 new trains that are gradually entering service over the next two years.

The transit operator has a fleet of 65 trains and two depots; one to the north and the other to the south of the city, with the train fleet split 40/25 between these depots.
Major overhauls and repairs are performed at a central workshop in the same region and shipments between the depots and the workshop will arrive the following day. Consumable items are delivered from three different suppliers with lead times varying from one to four weeks, depending on the type of component. One of the suppliers is also responsible for repairing some hi-tech components.

The target for the analysis is a mean waiting time (MWT) for spares of three hours. This performance level is required in order to meet the exceptionally high demands on operational availability.

Criticality & cost driver analysis
The optimisation method used in OPUS10 relies on standard logistic data such as corrective and preventive maintenance activities, lead-times, and turnaround times (TAT) etc. This information is usually readily available within the organisation. The model can also take into account if some components are considered to be extra critical to the operational performance.

With all data entered into the model, the analyse function in OPUS10 will give an initial overview of all the components and highlight potential cost drivers that need closer attention

The cost driver analysis will in this early stage of the process raise questions on the current maintenance practice and possibly bring about immediate actions. This could for instance concern modularization, i.e. the division, from a maintenance perspective, of large systems into smaller subsystems

Finding the optimal spares solution
OPUS 10 is used to calculate the initial optimal spares assortment as well as the optimal replenishment strategy of an existing stock. When calculating the optimal replenishment strategy it is valuable to include the initial optimal solution as a reference in order to have an idea of how well the organisation should have performed if OPUS10 was used already during the initial spares provisioning.

In this case the analysis shows that the €12.1 million invested in the existing stock will give an MWT for spares of 59 hours. Investing the same amount initially using OPUS10 would have provided a spares assortment with an MWT of six hours.

This emphasises the importance of using OPUS10 early in the project. The analysis also shows that the suggested replenishment to meet the target MWT of three hours will require an additional investment in spares of €3.8 million.

Sensitivity analysis
OPUS10 offers extensive possibilities to perform sensitivity analysis on the input data. This could either involve all components or focus on a selected group of items. The sensitivity analysis will reveal which parameters have the largest influence on the final output. This in turn will pinpoint the most valuable improvements initiatives as well as areas to focus on in the negotiation with external suppliers.

In this case it was decided that the sensitivity analysis should evaluate a 25 per cent reduction of price and TAT respectively, since this was assumed to be a realistic goal.

The analysis shows that cutting the turnaround times by 25 per cent is the most effective way to reduce the additional spares investment required to meet the target MWT. This is supposed to be realised through more efficient internal processes and renegotiation of the agreements with some external suppliers.

Implement new spares strategy
The recommended spares assortment at the given target MWT of three hours is presented in a table that for each unique component specifies the number of spares that should be stored at each location in the supply structure. Discardable components will also be associated with a reorder point and reorder size at each location.

The final step is now to implement the new optimal spares strategy that was developed based on the outcome of the cost driver analysis, the recommended replenishment strategy, and the sensitivity analysis.

Conclusions
In conclusion with this analytical approach Systecon can assist spares managers with:
  • Optimal assortment and location of spares for existing fleet and new deliveries
  • Highlight cost drivers and possible improvements of current maintenance practices
  • Sensitivity analysis and focus areas for negotiation with workshops and external suppliers.
For further information, please contact:
Systecon (UK) Limited
Tel: 0871 641 2202
Email: systecon@systecon.co.uk
Web: www.systecon.co.uk