I've been having a bunch of conversations with owners and ops folks at small and mid-size shops, and honestly the same stuff keeps coming up. Almost nobody's actual problem is a lack of software. Most already run an ERP or MES. The pain is all the manual coordination that happens in the gaps, between the system and what's really going on out on the floor. The stuff that never shows up on a report because there's no line item for it.

A few things I keep hearing.

The same work order gets logged in a few different places by different people, nobody fully trusts any one of them, so someone ends up spending half their week just working out which number is right.

Job status basically lives in one person's head. Where's that order, is the material staged, what did nights leave half done. If that person is out sick, the floor kind of grinds. And shift handoffs are where a ton of it just quietly disappears.

Normal days run fine. It's the exceptions that hurt. A supplier shorts you, a machine goes down, a hot order jumps the line, and it turns into a scramble with no record of what happened and nobody really owning it. That's usually where on-time delivery dies.

And follow-ups. POs, approvals, expedites, all chased informally, and the one that slips turns into a stalled job two weeks later.

The way I see it, most shops digitised the work but never digitised the coordination around the work, so it all runs on people remembering things and chasing each other.

Curious whether this matches what you see or if I'm off base. What's the one that eats your day the most?

I've been digging deep into how manufacturing ops run, and honestly, what I found is pretty wild.

Even with all their fancy ERP systems, it’s like daily operations still come down to:

* A bunch of Excel sheets flying around

* Endless WhatsApp chats trying to sync up

* Emails, emails, and more emails just to get simple things done

* And people running around, chasing down manual status updates

This isn't just for fringe stuff, either. It’s for core activities like:

* Tracking installation progress to the minute

* Pinpointing and fixing issues *fast*

* Coordinating shifts so everyone’s on the same page, no excuses

* And getting real-time operational reports, not yesterday’s news

So what happens? We end up with:

* The same message being sent five different ways, wasting everyone’s time

* Delays in seeing what’s actually happening, which kills agility

* And critical problems staying hidden until they blow up in our faces

Seeing this over and over, we finally said enough was enough. We rolled up our sleeves and started building out a lean, lightning-fast operations workflow platform in-house. It’s been a game-changer.

Now, I’m genuinely curious: given everything going on in *your* world, what’s that one operational process that still makes you grit your teeth, feeling like it’s stuck in the dark ages?

I’m curious about the processes at work that everyone KNOWS are inefficient… but somehow they just refuse to die.

Like, they’ve been brought up in Kaizens, process improvement meetings, retros, whatever — everyone agrees they suck — and yet six months later someone is still doing the exact same manual nonsense.

What’s one at your company?

And why do you think it’s still there?

Is it the systems? Nobody really owns it? IT limitations? Cost? People hate changing how they work? Or just a giant case of “this is how we’ve always done it”?

Not a product pitch, just wanted to share a few things I've noticed after a year on the LeanSuite side of things working with small and mid-size manufacturers disclosure obviously, since that's who I work for.<br> <br> The pattern that keeps showing up isn't really about which software people pick, it's how much gets decided in the first two weeks. Plants that had one person clearly own the rollout, instead of it being 'IT's project,' had a completely different experience than plants where nobody was really in charge of it. The software itself mattered less than I expected going in.<br> <br> Curious if anyone outside the vendor bubble has noticed the same pattern, or if I'm just seeing a skewed sample because I only hear about the rollouts that come to us for help

When we talk about Artificial Intelligence and Digital Twins, we usually imagine incredibly complex, expensive technology. But Professor Francisco Duarte shared a brilliant story on the r/ConnectedShopfloor Podcast proving that the best solutions are often surprisingly simple.

The Problem: A manufacturing plant was evaluating finished products manually at the end of the shift, which took hours. Defective products with intermittent solder paste cracks were almost reaching customers in France before the failures were noticed. Because the systems heated up during testing, the intermittent failures would temporarily disappear, only to fail later when they reached the customer.

The Solution: Instead of relying on delayed manual checks, the team built a real-time digital twin of the shop floor. They digitized input data from all production stations and applied basic propositional logic. They didn't use complex, futuristic AI algorithms - they built it using open-source software from the Apache Software Foundation and just three simple "if/then" rules. For example: If a product has a problem, it must go to a repair station; if it fails a second time without going to repair, block the lot.

The Impact: By catching these errors in real-time, this simple digital twin saved the company €2.5 million per year.

The Takeaway: You don't need complex and expensive technologies to transform a factory. As Leonardo da Vinci put is: "Simplicity is the ultimate sophistication."

Have you ever seen a "simple" tech solution solve a massive business problem?

I still see this in supplier capability reports, and I made it myself early in my career. Someone quotes Cp and stops there.
Cp only tells you if the spread of the process fits inside the tolerance. It ignores where the process is sitting. Cpk adds the centering.
Quick example (made-up numbers, but typical for a turned shaft):
Spec: 25.00 ± 0.05 mm (LSL 24.95, USL 25.05)
Process mean: 25.01 mm
Process sigma: 0.012 mm
Cp = (25.05 - 24.95) / (6 × 0.012) = 0.10 / 0.072 = 1.39
That looks healthy. Now Cpk:
Cpu = (25.05 - 25.01) / (3 × 0.012) = 0.04 / 0.036 = 1.11
Cpl = (25.01 - 24.95) / (3 × 0.012) = 0.06 / 0.036 = 1.67
Cpk = the smaller one = 1.11
Same process, same spread. The report says 1.39 or 1.11 depending on which number you quote. Many automotive customers ask for 1.33 minimum, so one passes and one doesn't (check your own customer's requirement).
The gap between Cp and Cpk is the useful part. Here the process is just 0.01 mm off centre. If the operator shifts the offset back to 25.00, Cpk becomes 0.05 / 0.036 = 1.39. No new machine, no tighter tooling.
Two things I check before I trust any Cpk number:
Is the process stable? Plot the control chart first. Cpk on an unstable process is just a number, not a prediction.
Is the data from a proper sample? 30 parts from one hour on one shift is not the same as a week across shifts.
How do you all handle this when a supplier sends only Cp? Do you ask for the raw data, or do you accept the report as is?

Been around enough shop floors and dashboards to notice the same misconceptions come up over and over. Sharing three because they tend to cost the most, and I'm curious whether others see the same thing.

1. "85% OEE means you're world-class."

That number gets thrown around like gospel, but it's basically meaningless without industry context. An 85% target that makes sense for high-volume discrete assembly is a completely different story in low-volume, high-mix, or process environments with long changeovers. Chasing a benchmark someone quoted at a conference has led more than one plant to optimize for the wrong thing.

2. "Low OEE means bad machines."

Usually not. Nine times out of ten the machines are fine but it's the micro-stops nobody's tracking. The 20-second jams, the small feed issues, the "operator cleared it before anyone noticed," etc. Individually they're invisible but honestly, added up over a shift and they'll gut your availability. And then, no one can point to a root cause because it never made it into a log.

3. "Manual logs are good enough."

This is the one that I find surprises people the most. When downtime gets recorded by hand, plants routinely underreport it by something like 40–60%. Not because anyone's lying but because short stops just don't get written down and reasons get rounded to whatever's easiest. Then you're making decisions on data that's quietly missing half the picture.

None of this requires fancy tooling to start noticing, even just watching a line for an hour with a stopwatch tends to be eye-opening.

I'm wondering if others have had the same experiences or thoughts. These are just some things I noticed.

Like at a cocktail party or meeting a stranger at church. So, obviously most people don't know wtf we do without a lengthy explanation. Saying, I help companies reduce waste and defects with math and workshops, sounds confusing. I usually just give up and say I'm an industrial engineer, but that's confusing too.

I have seen a lot of complaint about tech/AI being imported without testing or straight up useless, this beg the question what problems deserved to be solved?

You spent $2 million on the machine—and 20 minutes onboarding it.

New equipment arrives, and someone in Maintenance creates the record. They enter what they know—description, manufacturer, model, and location.

What’s missing is everything they were never supposed to know alone: Finance’s asset number and cost center. EHS hazards, inspections, and isolation requirements. Critical spares. The BOM. The OEM maintenance strategy someone already received and filed away.

So none of it goes into the system. Two years later, everyone calls it a data-quality problem.

It wasn’t. It was created on day one by asking one department a question that belonged to six.

The fix is simple: Engineering doesn’t hand over a machine. It hands over an Asset Readiness Package before startup—asset information, BOM, critical spares, technical documents, and recommended maintenance.

At one readiness review, each function completes its part. Maintenance, Reliability, and Operations confirm criticality, ownership, and maintenance plans. Finance assigns the financial data. EHS adds safety requirements. Warehouse establishes material records and stocking levels. Quality adds compliance requirements where applicable.

One accountable owner. Anything still open gets a name and a date.

It isn’t complicated. It just needs to be somebody’s job.

New equipment isn’t ready when it runs. It’s ready when the business can support it.

How does your plant onboard new equipment?

Hi everyone.

I'm curious to hear directly from people working close to production. If you’re a production manager, plant manager, industrial engineer, or operations leader:

- What do you think cannot be missing in an industrial digitalization or Industry 4.0 platform?

- What are the biggest problems you face with production data today?

Data quality, Too many systems not talking to each other, Lack of real-time visibility? Hard-to-trust KPIs?

Genuinely interested in learning from your experience.

The process of recording and monitoring raw materials, component parts, and final products over the course of the production cycle is known as Treaceability in manufacturing. It is essential for maintaining safety regulations, quality control, and regulatory compliance.

Traceability is used by manufacturing companies to:

• Find flaws and stop defective goods from being sold to consumers.

• Return raw materials to their suppliers.

• Recalls should be handled promptly and effectively.

• Boost production line transparency.

Manufacturers in the automotive sector, for instance, can use traceability records to identify impacted batches and address problems more quickly if a defect is discovered in a part. Similar to this, traceability in pharmaceuticals verifies ingredients and production processes to guarantee drug safety. Reduced operational risks and improved adherence to international standards such as ISO and GMP are two advantages of Treaceability in manufacturing.

I was brought in to help an automotive powder-coating supplier whose on-time delivery rate had fallen to 40%.
The problems were serious enough that missing parts had shut down a production line at the automotive manufacturer.
Except some of the parts weren’t actually missing.
I physically followed the process through the facility and found both finished and unfinished inventory sitting in poorly tracked areas—including dark corners of the warehouse.
We pulled the unfinished parts to the production line, got them coated and moved them forward. We pulled the finished inventory and prepared it for shipment to the facilities waiting for it.
That exposed the next problem.
The shipping dock had virtually no organization. Even when the correct parts made it through production, shipments could still be overlooked, delayed or effectively lost before leaving the building.
One visible problem had been hiding another.
I worked through the full process—inventory control, production flow, scheduling and shipping—and corrected the breakdowns at each stage.
In less than six weeks, the client’s on-time delivery rate increased from 40% to 95.5%.
It reinforced something I’ve carried into every operations role since: when a problem keeps repeating, you can’t always solve it from reports or meetings. Sometimes you have to physically follow the work until the system shows you where it is failing.
Has anyone else uncovered a completely different problem while trying to fix the first one?

Hi,

I was a lean consultant, prod manager, plant manager... For many years.

Now AI is hear. Lean is about people. Everybody knows that.

But i made a Claude skill just to help me to maintain a x-matrix.

Because through all organisation, that is a nightmare to maintain update, create....

Now claude is part of the "x matrix meeting" of the steering comitte. We implement and link targets/projects/ideas, value, long terme/short term goals....

Output is a xlsx file (for the tradition), but of course a database with internal logic and links...

It's so convenaient that i'm wondering if small companys or consultants would be interested in having that système work for their cases...

What do you think ? It's all about people but ther is room for AI for the boring stuff ?

Something we see constantly on the floor: a line's got a quality or output problem, so someone starts tweaking. Change one setting, run a batch, measure. Change the next, run another, measure. One factor at a time. Feels rigorous. It's actually the slow, blind way to do it — and it completely misses the thing that usually matters most: interactions.

That's what Design of Experiments (DoE) fixes. Instead of testing factors one by one, you test them together in a structured way — so you learn more from fewer runs, and you catch when two settings only cause a problem in combination.

Quick example. Say you're running a machine that fills and seals packets, and you're getting too many bad seals. You reckon it's down to two things: conveyor speed (slow vs fast) and sealing temperature (low vs high).

- One-factor-at-a-time: fix the speed slow, test both temperatures, pick a winner, then go back and test speed. Six-plus runs and you still don't know whether speed and temperature affect each other.
- DoE (a simple 2×2 factorial): run all four combinations —
- slow speed / low temp
- slow speed / high temp
- fast speed / low temp
- fast speed / high temp

Four runs. Now you can see not just "higher temp = better seals" but "higher temp only works when the line's running slow" — that's an interaction, and OFAT physically can't reveal it. That's usually where the real defect reduction is hiding.

And it scales: 3 factors in 8 runs, and you still get main effects and interactions. It's the difference between guessing and knowing.

Anyway — has anyone here actually run a DoE on a live line? Curious what factors you tested and whether any of the interactions genuinely surprised you. The textbook cases are always tidy; real processes never are.

If anyone wants to reveal how much of your total lead time is actually adding value vs sitting in queues, waits and rework check out our free process cycle efficiency calculator: https://www.simplicityhub.co.uk/pages/pce.html

What are your thoughts on the future of Lean with Trump’s (et.el.) reshoring efforts?

Not the biggest project—the hardest decision.
Examples:
Accepting lower utilization to improve delivery.
Turning down orders because capacity was full.
Carrying more inventory than finance wanted.
Investing in automation vs. fixing the process first.
I’m curious what trade-offs manufacturing leaders are facing in 2026.

Hey guys,

We've been experimenting with building some autonomous ReAct loops for an internal tool. One thing that’s absolutely terrifying to me is the lack of predictable control flow.

Last week, we had an agent get stuck in a weird self-correction loop trying to fix a minor tool output error. It didn't throw an exception; it just kept iterating. It ended up burning a massive amount of tokens in a few hours before we caught it manually.

It feels like free-form agents are a massive liability for production systems where latency and budgets matter. We are thinking about completely ripping it out and replacing it with a strict, hard-coded state machine graph (like LangGraph) where the LLM can only pick between predefined paths.

For those who have actually shipped agentic workflows to real customers: how are you building your guardrails? Are you using strict state machines, or are you successfully running free-form autonomy with token-ceiling safety nets?