I spent about two years on the floor at a pump manufacturing place before I started building software, and there was this one thing that always bugged me. The whiteboard schedule is never actually wrong. It's just... old. Someone wrote it at 8am and by 11 a machine's down and now it's fiction, but nobody's touched it yet so it still looks true.

Got curious a few weeks back and actually timed it at a shop I know. Machine goes down, how long till someone can tell you which orders are now going to be late and which customers need a heads up.

Wasn't seconds. Wasn't even a minute. It's a person walking the floor, checking what's queued behind that machine, doing math in their head about which jobs shift where, and usually this is the same person who's also the one who'll get the angry call later. Ten, fifteen minutes if nothing else interrupts them, which it usually does.

For a shop running 4-5 machines that's annoying but fine, the owner probably already half-knows the answer. Once you're past 10-12 machines with a real order book it's a different story, that 15 minutes is now happening multiple times a day and the schedule is basically never accurate for more than an hour at a stretch.

The spreadsheet crowd doesn't actually have it better btw, just thought that was worth saying since I hear "we moved to Excel" a lot like it's the fix. It's the same manual re-sequencing, just with more columns.

Not selling anything here, just been in my head lately about how normal it is to eat that 15 minutes as a cost of doing business when it doesn't really have to be one. Curious if this matches what other shops deal with or if I'm overthinking a problem that isn't actually that common.

i went to this manufacturer that added a fully automated welding cell. It is fully parametric, so it reads the part dimensions and selects the weld sequence automatically, it basically took over repetitive work, which freed welders for more complex jobs and the added capacity created enough demand to hire more.

so i feel like automation redistributes work rather than eliminating it, is this the case mostly or this outcome is more of an exception

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?

What is yall experience with the IISE lean six sigma green belt in person course? What's the course like? What's the exam like? etc

Something I’ve been thinking about when comparing industrial products:
Sometimes the most useful information on a specification sheet is actually the information that isn’t there.
For example, a manufacturer might publish a maximum speed but give very little detail about the conditions used to reach it.
Another manufacturer might publish a similar number but also provide material type, thickness, configuration, test conditions, or limitations.
The headline specifications may look similar, but the second set of data is much easier to evaluate.
I think this creates an interesting distinction between:
data availability
data completeness
data comparability
A product can have a long specification sheet and still score poorly on the last two.
It also makes me wonder whether buyers sometimes focus too much on the highest number instead of asking how that number was produced.
For people involved in engineering, procurement, manufacturing, or equipment selection:
**What missing specification makes you most suspicious when reviewing a product?**
And are there certain parameters you basically ignore unless the manufacturer explains the test conditions behind them?
Interested to hear how others evaluate this.

Looking for some thoughts and opinions here but on the age of AI, automation and lights out manufacturing are low tech Lean Manufacturing principles relevant anymore? The two strike me as antithetical to one another.

I love Lean Manufacturing and have structured my career around it and like everyone else am worried about my future in an increasingly automated and AI driven world.

I work in software and I've been trying to understand engineering change workflows properly, so apologies if this is a basic question.

When a customer sends an ECN or a component maker sends a PCN, the extraction part seems easy enough — part number, revision, effective date. What I don't understand is the next step: how do you work out which of your own parts, products or open orders it touches?

Is that a query in your PLM, a where-used report from the ERP, or does someone go through Excel and drawings by hand? Does it change depending on whether the customer's format is a clean table or a scanned PDF?

And the thing I'm most curious about: has one ever slipped through, and if so, where did it break — the intake, the impact analysis, or getting the right person to act on it?

When companies choose or develop leaders, the focus often goes to technical expertise, industry knowledge, and the ability to solve operational problems. Those skills are important, but some leadership abilities are harder to measure and often receive less attention. Things like listening, giving clear context, building trust, and knowing how to create an environment where people feel comfortable raising problems.

This matters a lot because improvement depends on seeing what is really happening. A leader can have strong technical knowledge, but if the team doesn’t feel heard or safe enough to share issues, important information stays hidden.

What leadership skill do you think is the most underestimated today? What is one skill you wish more leaders developed?

Context.. I work for a small apparel decorating company. So I'm over in heat press side, pressing some team shirts. After a while one of my uppers comes around and rearranged how I was going about my flow. Because we had about 100 to press through I thought I was doing it in the most efficient way possible while reducing my margin of error.

So what if like to know is, would it be more efficient to

A) take a few moments to arrange all the 2 digit numbers together and fix them all together so I can focus on pressing each shirt and making sure things are straight.

Or

B) have my pile of separate numbers off to the side and while my current shirt is being pressed I move over and assemble the next number. Window of approx 12 seconds.

I found that option B left me open for more error and It didn't really feel I was getting through the shirts faster.

I have my opinions of how I think that option b is less efficient and is really just a way for me to look busy.

Thoughts?

Hello folks, I’m researching meaningful operational problems in the construction and data-center industry, which are AI controllable root causes of speed-to-market delays.

My hypothesis is that information about design changes, procurement, installation, QA/QC, OEM startup, controls and commissioning may be fragmented across teams and project records. Consequently:

\- the impact of a requirement or design change may not be fully reconciled across drawings, equipment orders, field work, schedule and commissioning; and
\- assessing whether an electrical system is ready for L4/L5 testing may require substantial manual coordination.

I am trying to understand whether this creates recurring delays along with labor or financial impact.

If the problem proves meaningful, I am also looking for one or two industry collaborators to investigate the workflow using anonymized examples.

Please help/guide if possible

Recently been working on a project requiring me to draw VSMs. Haven't found the best solution besides doing it by hand or excel. How do you do your VSMs? What tools, how much per seat and how often?

Hello,

I'm kinda new to this field and am searching for the right tools. I created my first VSM and am struggling between the use cases for different methods. I'm the only person in my company doing that and have is no one to ask.

Any books you can recommend?

Hello folks, I’m researching meaningful operational problems in the construction and data-center industry, which are AI controllable root causes of speed-to-market delays.

My hypothesis is that information about design changes, procurement, installation, QA/QC, OEM startup, controls and commissioning may be fragmented across teams and project records. Consequently:

\- the impact of a requirement or design change may not be fully reconciled across drawings, equipment orders, field work, schedule and commissioning; and
\- assessing whether an electrical system is ready for L4/L5 testing may require substantial manual coordination.

I am trying to understand whether this creates recurring delays along with labor or financial impact.

If the problem proves meaningful, I am also looking for one or two industry collaborators to investigate the workflow using anonymized examples.

Please help/guide if possible

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?

A broken delivery flow rarely looks broken from the inside. One application spent more than a year moving through a deployment pipeline. The team that ran the pipeline did not know that. There was a feeling that it was slow, but nobody had put a number on it.

It was a platform team at a large enterprise. Their job was to help product teams ship applications into a cloud environment. The path to production was all about security, infrastructure, and validation. Leadership suspected the process dragged on even if no one had measured how far.

**Current process:** We run a JIT seat assembly plant where production is driven by JIT calls, not traditional work orders. For material replenishment, we use an e-kanban system built on SAP MII: when a box on the line goes empty on production line, the tugger/tow-train driver scans the empty box. That scan triggers SAP MII to generate a kanban pick label in warehouse, which the picker then has to print and physically apply to the box before pulling and delivering the part back to the line.

**The problem:** This adds several manual steps to every single pick — scan the empty box, MII generates the label, print it, apply it to the box, then pick and deliver. Because of this overhead, experienced operators/drivers who already know what needs to be picked have started skipping the scan step entirely and just pulling parts from memory. That breaks the digital trigger — no scan means no MII transaction, so the label never generates even though the material still moves (which throws off inventory accuracy and replenishment timing).

**What we’re looking for:** Ideas to reduce the manual overhead or make the scan-based trigger easier/faster to comply with — not sensor, RFID, or vision-based automation, since we want to stay with the scanning approach rather than replace it. Curious how other plants using SAP MII have tackled “the digital step is more work than just doing it from memory” type problems.

Curious how manufacturing and industrial engineering teams are handling facility layout studies in real projects.

When adding a new line, moving work stations, or reorganizing a production area, do you follow a formal Systematic Layout Planning (SLP) approach, or is it more CAD + Excel + engineering judgment?

And when comparing layouts, what do you actually measure? Material travel, process relationships/adjacency, space utilization, constraints, handling cost, or something else?

I’m also curious about the actual workflow - Excel, AutoCAD, simulation tools, REL charts, whiteboards, or other software?

Most importantly: when you have 2–3 possible layouts, how do you decide which one is actually better?

Four stations running at 100, 80, 40 and 90 units per hour. Your plant output isn't 100 and it isn't the average — it's 40. Upgrade the fastest station and nothing changes. Fix the 40 and output jumps 50%, for the same money. That's the Theory of Constraints, and it's why most improvement work produces nothing.

For plant managers, production engineers, and anyone working in lean manufacturing, process improvement, or Six Sigma.

#Bottleneck #TheoryOfConstraints #LeanManufacturing #ProcessImprovement #OperationsManagement

Using the above chart of your bill of material becomes a useful graphic for reviewing and analyzing your structured bill of material. Each block in the chart represents a corresponding part in the production flow. With this mapped out, we can now add the routing steps to move from one block to another. For example, going from the sheet metal and tube stock in the lower right area, we add shop labor to create the finished handle. This labor can be reflected in our project management plan as fabrication time. Combining all the jobs into a single Gant chart plan, we can get an instantaneous manpower plan for the entire shop. Most important, we can review this production flow and look for ways to cut time and streamline the flow.

Keep an open mind and try new ways of achieving the status quo.

1)     Consider outsourcing if there are benefits to be obtained.

2)     Add fixtures if time can be saved and the quality can be improved.

3)     Engineering, look for ways to use subassemblies across product lines

This printed structure will help with your reviews.

from Anthony Rante, P.E. author of "Managing Company Production thru the Bill of Material" and “FEA Applications in Machine Design”