vermorel
I run an aviation MRO for jet engines. What techniques do you recommend to compress TAT considering that our budget for engine parts is capped?
lokad
Based on Lokad’s quantitative supply chain approach for MRO, especially for aviation engine repairs, the key is to rethink inventory and part prioritization beyond traditional safety-stock logic. Instead of relying merely on historical averages or fixed service levels, compressing turnaround time (TAT) in an environment with a capped budget demands that you:
1. **Focus on Turnaround Time (TAT) Analysis:**
As explained by Joannes Vermorel in our discussion on [lead time](https://www.lokad.com/lead-time/#:~:text=Turnaround%20time%20(TAT)%20for%20MROs) (dated October 2020), the available inventory must be viewed in terms of its ability to keep the repair process continuous. When repairs are unscheduled and subject to inherent uncertainty, understanding the TAT becomes pivotal. Instead of overstocking “just in case,” you should quantify the delay cost of not having a part on hand.
2. **Adopt Stochastic Optimization – “Stochastic Discrete Descent”:**
In the context of jet engines, a stochastic approach helps you evaluate for every potential part purchase: “Given this budget, how many days of repair delay can I avoid?” This method, detailed in our discussion of [stochastic discrete descent](https://www.lokad.com/stochastic-discrete-descent/), recognizes that:
- *Not every component has the same criticality:* Parts needed at the very beginning of reassembly might be stocked less urgently if they can be sourced in parallel.
- *Dependencies matter:* The repair process involves non-linear cascades, where missing one part might have a disproportionate effect on delay.
The technique takes these interdependencies into account, optimizing which parts investments yield the best reduction in downtime under budget constraints.
3. **Quantitative Decision-Making Over Heuristics:**
Lokad’s philosophy stands firmly against the reliance on naked time-series forecasting or blanket safety stocks. Instead, our quantitative approach rigorously models the stochastic nature of unscheduled repairs (which is especially acute in jet engine maintenance) and the cascading delays modules. With a capped budget, the tools that underpin our methods answer the precise question: “How many days of repair delay have I reduced per dollar spent?” This allows you to strategically prioritize investments in parts that not only are needed but also carry the highest cost of delay if they’re missing.
In practical terms, building a robust TAT compression strategy involves modeling both the repair cycle and the supply lead times. This could involve:
- **Detailed analysis of the repair cycle stages** — identifying which stage is most sensitive to delay.
- **Building a cost-delay function** for every part based on its position in the repair process and how its absence affects the overall engine turnaround.
- **Stochastic optimization algorithms** that simulate various scenarios and converge on budget-optimal stocking strategies.
By applying these principles, you shift the focus from traditional, static inventory measures to dynamic, data-driven decisions that better align with operational realities. This is the essence of the quantitative supply chain vision at Lokad.
If you need a deeper dive into how these principles can be tailored for your operations or further clarification on specific model implementations, please consider reaching out directly to our team or contacting Joannes Vermorel at [j.vermorel@lokad.com](mailto:j.vermorel@lokad.com).
Sources:
- [Lead Time - TAT Analysis for MROs](https://www.lokad.com/lead-time/#:~:text=Turnaround%20time%20(TAT)%20for%20MROs)
- [Stochastic Discrete Descent for Engine Repairs](https://www.lokad.com/stochastic-discrete-descent/)
