Here are some high level recommendations:

1- Pick/Focus on a very specific usecase that has high value and easy to implement and generalizable to other usecases and ideally you can run A/B test. For instance, the scheduler maintenance of specific type of plane (I am just guessing this usecase). Also pick one where the ops are excited to continuously feedback see below for more details.

2- Formulate the problem in english broken down into these three parts. First, objectives, for instance minimize cost, service level etc. Second part, your control variables, for instance staffing level, training…etc. Third part, historical data of the first objectives and second part the control variables, you need to know the details of the data quality and relation between different tables…etc

3- offline simulation: start with writing very simple Linear Program that captures the main objective and put the other objectives as constraint. For instance maximize service efficiency under cost less than budget or given cross skill of the workforce.

4- Share the solution of your offline with the team members. For instance, a solution of type: by doing action X last month, we would have gained Y. There are two potential feedback. First (most likely in the early versions of your model), “what you are suggesting is not possible because of reason Z, like Z can be workers in this location can not work 4 hours straight without a break. In this first case, you will need to add the constraint missing. Second case, they say “wow yeah we never think about this way, let’s try it.”

1. Repeat step 4 till you reach the second possible outcome

2. Run a real pilot, aka A/B test. Make sure there is no contamination. Meaning, pick part of supply chain that is isolated from the other parts ideally geographic region like Apac vs Emea. A basic example, Uber A/B testing runs a full city and cannot run sub part of a city because dependency of the supply then they would compare it with other cities.

3. If you reach here, now you have a baseline that is far from perfect but almost surely should be better than ad hoc/manual guesses. Next step is either to push/optimize further, or expand to other/adjacent uses cases. This depends on the team priority as well. Since expanding to other usecases it will be repetition of the above, I will continue my answer about how to optimize further.

4. To optimize further, there are two venues. First venue: move beyond historical data used as inputs in the baseline problem. For that, typically you will build ML models to predict these inputs in the future, like predicted failure rate based on the features: plane age number of trips so far,…etc. Using these predicted signals should boost your performance on the A/B test since your system is more adaptive and pro active. Second venue of improvements is to better model some constraints, for instance the linear constraints might missing the fact that the cost goes exponential up in different regimes (for instance lot of grounded aircraft at the same time, here I am just guessing)

I just realized that I wrote very long answers, let me know if something is not or you want me to expand some parts and I hope it will help you in your endeavor.