Stock levels in stores v AOG
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Stock levels in stores v AOG
As the fleet size increases, so the level of nuts and bolts, spares parts etc held in stores will need to be inceased to ensure that aircraft are returned to service following routine maintenance and when they go tech - not just ADDs.
Whilst it will cost more to hold increased stock levels, that must be balanced against the down time of aircraft not flying where the parts are not in stock.
What are the recognised formulae to ensure adequate stock levels but avoid over stocking with that increased cost, and at the same time attempting to minimise AOG situations.
Whilst it will cost more to hold increased stock levels, that must be balanced against the down time of aircraft not flying where the parts are not in stock.
What are the recognised formulae to ensure adequate stock levels but avoid over stocking with that increased cost, and at the same time attempting to minimise AOG situations.
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This science of inventory control probably takes longer to master than getting a PhD.
So many factors: Order lead time, material cost, bulk pricing, cost of money, cost of storage, utilization rate, ... All balanced against the cost of an AOG.
There are shortcut formulae, but they all have risks and compromises.
So many factors: Order lead time, material cost, bulk pricing, cost of money, cost of storage, utilization rate, ... All balanced against the cost of an AOG.
There are shortcut formulae, but they all have risks and compromises.
Within BA it's called EWS...Engineering Without Spares.
de minimus non curat lex
AOG
I think you can safely add the passenger compensation scheme for EU operators to the airline expenditure.
Additionally, the so called cost savings of not having any, or inadequate, engineering cover down the route can prove to be false economy. I don't think you would need many AOGs down route for the engineers to effectively pay for themselves. Worth their weight in gold every time.
Trying and probably failing to save on the pennies, but having to pay pounds instead. Decisions Decisions.
Additionally, the so called cost savings of not having any, or inadequate, engineering cover down the route can prove to be false economy. I don't think you would need many AOGs down route for the engineers to effectively pay for themselves. Worth their weight in gold every time.
Trying and probably failing to save on the pennies, but having to pay pounds instead. Decisions Decisions.
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The problem is the difficulty of forecasting lumpy demand. There are two broad approaches to spare parts forecasting: the first is based on the operational experience of an enterprise, and the second on the application of forecasting techniques. Only 10% of companies use forecasting models.
Airline operators usually base predictions on their operational experience, on annual budgets, and information from aircraft manufacturers that prepare lists of recommended spare parts. The aim is to evaluate and compare. When new types of aircraft are introduced, the airframe and engine manufacturers normally provide a list of recommended spare parts, which is based on the projected annual flying hours of the new aircraft. The original equipment manufacturers also provide overhaul manuals for aircraft components aimed at supporting assessment of required replacements, i.e. data on the operational life of components. Consequently, the forecast of spare part inventories is usually based on past usage patterns and the experience of company personnel.
Demand forecasting (in the mathematical sense of forecasting) for lumpy items is a complex problem. Previous studies are not very accurate. The very latest work by Regattieri, Gamberi, Gamberini and Manzini, using 6 years of data for an A320 fleet, found the best forecasting approaches to be weighted moving averages (which is also easy to do), the Croston method (exponential smoothing), and exponentially weighted moving average models. All do-able in a spreadsheet or a database, although no doubt some IT salesman can make a strong case for spending millions of dollars on their software.
Airline operators usually base predictions on their operational experience, on annual budgets, and information from aircraft manufacturers that prepare lists of recommended spare parts. The aim is to evaluate and compare. When new types of aircraft are introduced, the airframe and engine manufacturers normally provide a list of recommended spare parts, which is based on the projected annual flying hours of the new aircraft. The original equipment manufacturers also provide overhaul manuals for aircraft components aimed at supporting assessment of required replacements, i.e. data on the operational life of components. Consequently, the forecast of spare part inventories is usually based on past usage patterns and the experience of company personnel.
Demand forecasting (in the mathematical sense of forecasting) for lumpy items is a complex problem. Previous studies are not very accurate. The very latest work by Regattieri, Gamberi, Gamberini and Manzini, using 6 years of data for an A320 fleet, found the best forecasting approaches to be weighted moving averages (which is also easy to do), the Croston method (exponential smoothing), and exponentially weighted moving average models. All do-able in a spreadsheet or a database, although no doubt some IT salesman can make a strong case for spending millions of dollars on their software.
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If were're talking about rotables (those bits that can be repaired), both Airbus and Boeing have provisioning formulae which can be used to calculate the number of spares needed to support a certain sized fleet. The variables used in the calculation typically include: Fleet Size, Annual Utilization of the aircraft, MTBUR of the equipment, Average repair cycle time (no. of days it takes for the removed equipment to be repaired and back serviceable on the shelf, including shipping, etc), plus the number of line stations that you wish to keep stock at (typically only the no-go items)
