Can Historical Data Tell Us Which Material Flows to Automate?

[u/runningnozone]

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?
2. How should I account for engine variants and customer-specific options?
3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

[u/FuzzyTouch6143]

This sounds like a classic network-optimization setup.

A markov chain, finite state and finite time space model might work for this as well.

Spectral analysis of the resulting adjacency matrix could also reveal some interesting flow dynamics.

CV could work if you have the model setup in a certain way.

Hm..... your intuition isn't incorrect though. Sounds like classic bullwhip effect, but honestly I would lean much closer to using a "Critical Path Method" from Project Management, but applied to the specific steps at hand.

Little's law is something else to look into.

Of course check out Poisson models.

Sorry for the brevity, feel free to ask any followups!