The purpose of the Multi-Location Inventory Optimisation function is to determine the most cost-effective mid-term plan, i.e. required quantities and default allocation of spare part units to all stockholding nodes where demand can realistically arise.
Requirements arising from different demand types (i.e. regular maintenance and unscheduled demand) are combined to generate a forecast based on to realistic demand signal. Static and dynamic cost factors are considered to minimise total lifecycle (inventory + AOG delivery & risk re-balancing logistics + late/no-fill) cost. The systems enables inventory optimisation across entire part number groups, allowing trade-offs between keeping the number of expensive units low versus storing more units of cheaper part numbers.
Pools across multiple locations are also considered in order to quantify re-usability of spares at nearby airports and maximise overall asset utilization. Airline schedules can be taken into consideration to portray logistics connectivity and to enable the assessment of the chance of being able to bring in AOG parts from other locations versus keeping them locally. Dynamic redistribution of serviceable spares can also be portrayed to determine most cost-effective policies and make savings maintainable right from the Initial Provisioning stage onwards.
By comparing with the current spare parts inventory ownership, purchase recommendations can be derived. The purpose is to reduce the overall risk of running into trouble situations such as inventory outage and urgent deliveries from one location to another.