Prognostics-driven supply chain optimization in commercial aviation

Abstract

Maintenance providers in commercial aviation are looking into new strategies
to improve maintenance operations and aircraft availability in order to reduce
cost and increase customer satisfaction. Aircraft availability and thus
profitability highly relies on adequate maintenance because an aircraft is only
profitable when flying. One of the strategies that can be used to achieve higher
aircraft availability is Predictive Maintenance (PdM). The new generation of
aircraft produces a flood of data enabling PdM, a maintenance strategy in which
failure scan be predicted and maintenance takes place proactively instead of
reactively. PdM is enabled by prognostics, which is an engineering discipline
that aims to estimate the Remaining Useful Life (RUL) of(aircraft)components.
However, using prognostic information effectively and determining the benefits
and impact on processes in the supply chain of these maintenance providers
still proves to be very difficult in practice. This is amplified by the fact
that this prognostic information is imperfect, not only can prognostic models
produce false alarms or fail to predict coming failures, the exact timing of
these events is subject to uncertainties. On top of that, little research into
the benefits of prognostic information in supply chains is available.
Especially, research considering global supply chains with pool processes such
as in commercial aviation, is very limited. Efficiency of supply chains in
commercial aviation is important in order to increase aircraft availability,
reduce delays and cancellations, and reduce supply chain operating costs. This
study provides a novel approach to using imperfect prognostic information to
optimize a global supply chain considering a pool process and answers there
search question: ’How to optimize a supply chain in commercial aviation using
imperfect prognostic information?’ A discrete simulation model has been
selected and developed for the simulation of a global supply chain of spare
components for Boeing 787 aircraft at KLM Engineering and Maintenance(E&M).
This discrete simulation model provides insight in processes in this supply
chain while simultaneously considering uncertainties and maintaining a superior
computational time. Model verification shows thatthe model is programmed
correctly and model validation shows the programmed model accurately represents
the real supply chain in a case study at KLME&M. Simulation and
optimization shows that enabling the supply chain at KLM E&M with
prognostics can reduce the total cost by 20% per year. Results show that
the prediction horizon, which is the amount of time failures can be predicted
in advance, is the main contributor to this reduction in cost. Not only does
this prediction horizon provide the supply chain department with enough time to
ship components in a cheap manner, replacing components long before the failure
massively reduces damage and thus repair cost. However, a sensitivity analysis
shows there is a clear trade-off between minimizing cost and a reduction of the
Mean Time Between Removals(MTBR). Cost can be minimized by predicting
failures12 days in advance, which simultaneously results in an MTBR reduction
of 2.1%.This effect can be mitigated when the accuracy of prognostic models is
increased. Furthermore, there duction in cost heavily relies on the percentage
of aircraft in the pool for which failures can be predicted.  This
research shows the effects and potential benefits of using imperfect prognostic
information in a supply chain in commercial aviation. Recommendations for
further research are provided in order to use prognostics to optimize the
entire logistics supply chain of aircraft maintenance, including the phase-out,
reuse, and recycling of aircraft and their components. This could have a huge
impact on the environmental impact and sustainability of the global industry
that is commercial aviation.