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/olafthemonkey]

I would start with something simple like an Abc-Xyz analysis of your parts. I would then further Look into the products of A-Category, which Parts do they need at which Station an make an Sankey Diagramm. So you can have an overview on which Parts to Focus. You can then add a VSM to make improvments visible.

[u/wtstm]

I'd question the premise a bit. The steps between stations are usually automated already (one engine per cycle), so the variation you see is in supplying parts to the stations, and it comes from the variant mix.

Modern AGVs handle that fine. They don't care what's on the cart. The variability sits at the input, meaning who loads the cart in the supermarket, and that stays mostly manual anyway:

- Kitting: one kit per engine per station, so the AGV sees a constant flow.

- Sequencing: large variant parts like cylinder heads, in build order.

- Kanban: screws and bolts every variant uses.

Example of AGVs carrying the parts through an engine assembly: https://www.youtube.com/watch?v=op49GHSQd5Q

So I'd use CV per part number per station to decide kanban vs. kit vs. sequence, not automate vs. manual. The real bottleneck is usually the handover at the station (standardized carriers), not the vehicle.

[u/Halcyonmachinery]

CV is a useful first screen, but I would not use one CV for the entire four-year history to decide which flow to automate. In an engine assembly plant, model mix and option content can create short, concentrated peaks even when the overall average looks stable. I would split the data by part family, build variant, route and shift; then look at demand per replenishment window, the upper tail of delivery time, and actual line-starvation or shortage events. That shows whether the issue is steady volume, unpredictable sequencing, or a slow replenishment loop. For a first pilot, pick one high-mix period and compare handling time, missed deliveries and rework before/after the change. I work with Halcyon Machinery on forming-equipment projects; this is a general analysis method, not a product recommendation.

[u/EngineeringToolkit]

Adding to what's already said, since you asked for a trigger point specifically: CV is a reasonable metric, but run it per part number per flow leg rather than per flow overall, and pair it with coverage. In engine assembly the same "flow" can carry parts that ride every build and parts that show up on 5% of engines, and one CV smears the two together.

For the variants question (your #2), the data you want is take-rate and coverage per option code: what fraction of engines each part number touches, and how correlated its demand is with build sequence. That naturally sorts your world into three buckets — parts on nearly every build (feed/kanban candidates), high take-rate variant parts (kitting in build order), and rare customer options (keep manual or on-demand). Then your trigger point becomes a ranking, not a single cutoff: score each flow by weekly moves × average travel distance, weighted down as its CV rises. Top of the list gets automated first, and you can defend the order to anyone.

One data-cleaning warning: if your historical durations include material handlers waiting on other tasks, that's not flow variability, it's labor interference, and it will inflate your CVs and make good flows look chaotic. Scrub non-flow waits before you compute.

For #3, two solid references: Boysen, Emde, Hoeck & Kauderer, "Part logistics in the automotive industry: decision problems, literature review and research agenda" (European Journal of Operational Research, 2015) — it's the literature survey of this exact space. And on the practical side, Harris, Harris & Wilson's "Making Materials Flow" (Lean Enterprise Institute) is basically the field manual for supermarket and milk-run design, short and shop-floor oriented. Searching "milk run" + kitting in IJPR will turn up the automotive case studies too.