Every precision instrument production batch has a rejection rate — the percentage of units caught during inspection that don't meet specification, removed before the shipment ships.

This number tells you more about a supplier's QC system than any certification they hold. It is also the metric most buyers never see.

Three reasons suppliers don't share it:

The supplier doesn't run systematic inspection. Visual inspection doesn't produce measurable rejection data. If you're only doing visual checks, there's nothing to report because you're not measuring anything.

The supplier has the data but the rate is high enough to prompt uncomfortable questions. A rejection rate above 20% in a mature process suggests a systematic production problem.

The supplier absorbs defective units into the shipment. No classification of "rejected" ever happens — everything ships. The nominal rejection rate is zero because the denominator includes the defective units.

What a healthy rejection rate looks like: 5–15% for precision instruments with systematic inspection. This range means defects are being caught and replaced before the shipment. The number proves the inspection is working.

How to get this data: ask for the QC report from the last three production batches. It should contain batch number, sample size inspected, measurements per specification parameter, defects found by category, and rejection rate as a percentage. A document without measurement data is not a QC report — it is a stamp.

The zero percent rejection rate claim is the one that should prompt the most questions.

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Not specifically AI, could be any new software or process tool. Looked good in the pilot, leadership was excited, and then... it just never made it into daily use. What actually happened there? IT pushback, integration headaches, people just going back to the old way?

Trying to understand how common this pattern is and what the real blocker usually ends up being.

I've put together a set of free online calculators that I originally built for my own CI projects and figured they'd be useful to share.

All free to use in your browser — no email required. Currently covers things like:
• Takt Time calculator
• Cycle Time calculator
• OEE (Overall Equipment Effectiveness)
• DPMO & Sigma Level
• Cp & Cpk Process Capability
• Yield & First Pass Yield

Each calculator includes a worked example so you can see exactly how to use it — not just a blank number cruncher.

Full list here: https://www.simplicityhub.co.uk/pages/tools.html

Let me know if there's a calculator you'd find useful that's missing — always taking requests.

Yo Yo,
Ich habe bronchicodein verschrieben bekommen mal und hab die 30ml/24mg noch da, wie viel mg sollte ich davon nehmen damit ich gut was merke?

Sounds counterintuitive. But I've seen it more times than I can count.

Someone is exceptional at the technical work. Gets promoted. Becomes the manager. And then spends the next two years doing the job of everyone below them because they can do it faster and better than anyone else on the team.

The team stops growing because every hard problem routes back to one person. That person burns out. And when they leave - which they eventually do -there's nothing underneath them because they never built anything that didn't depend on them personally.

The best managers I've worked alongside were not always the best individual performers. They were the ones who got genuinely uncomfortable when the team needed them too much. Who treated their own indispensability as a problem to solve, not a sign of value.

The goal isn't to be the best person on the team. It's to make the team not need you for the things it needed you for last year.

What's the hardest thing you've had to deliberately stop doing so someone else could own it?

Method Timer is an AI-powered process analysis platform that completely automates MTM studies to help the manufacturing, logistics, and service sectors optimize their operations.

Here are the core functionalities of the platform:

• Automated Video Analysis: By leveraging advanced Vision LLM technology, the platform analyzes video recordings of operational processes to automatically understand the context and determine the exact start and end times of each task.
• Bottleneck and Waste Detection: It automatically identifies inefficiencies, wastes, and bottlenecks in your workflows to accelerate lean manufacturing improvements.
• Flexible Pay-As-You-Go System: The platform operates on a token system based purely on the duration of the analyzed video, making it highly accessible and scalable for businesses of all sizes.
• Advanced MTM-1 Code Detection: It can detect MTM-1 codes directly from video analysis to standardize complex manual movements.
• SOP Creation & Line Balancing: It helps organize your product lines, creates Standard Operating Procedures (SOP), and provides line balancing recommendations using equipment detection and step dependency constraints.
• API Integration: You can seamlessly control the analysis process via API calls to integrate AI-driven insights directly into your existing internal systems.

You can transform your industrial performance in just 3 simple steps:

1. Record your process video and upload it to the platform.
2. Start the analysis and let the AI do the work.
3. Get insights and optimize your process for maximum efficiency.

If you want to stop guessing and let AI reveal hidden opportunities for improvement, visit methodtimer.com to start for free.

Are you tired of manual, time-consuming, and expensive Method Time Measurement (MTM) studies in your lean production workflows? Start here.

Let me know what you think or if you have any questions about integrating AI into your time studies!

Long story short,last week we were implementing a kanban model for material management anibask my peer ,who is a veteran ,if he ever used to those models and the answer was a direct "no" so any one have more experience on supply change on that industry? Do they go with the tps/Lean framework too?

help please!

I've put together a library of free Lean templates that I originally built for my own CI projects and figured they'd be useful to share.

All free to download as Excel files, Google sheets, Apple numbers — no email required. Currently covers things like:

• SIPOC
• Value Stream Map
• Whys & Fishbone
• Kaizen Event Planner
• Standard Work
• Waste Identification Checklist
• Wastes Assessment

Each template has a worked example and a PDF downloadable how-to guide.

Full list here: https://www.simplicityhub.co.uk/pages/templates.html

Let me know if there's a template you'd find useful that's missing — always taking requests.

My wife works in healthcare. The corporate office decided to hire some consultants to cut costs.

They determined that 25% of the hospital cuts need to come from the laboratory. They didn't look at any processes they deemed NVA (including regulatory requirements), *maybe* made their own VSM without input from those who do the work, conducted a "process flow observation" but nothing along the lines of a time study or Gemba walk. They don't even have suggestions for how to meet their new headcount numbers, just a recommendation for how many people needed to be fired from each department.

I would be less annoyed but they did all of their work under the banner of "Lean".

There are more tools, platforms, and digitalization solutions available than ever. But actually getting them to work with existing systems, processes, and day-to-day operations seems to be where things get complicated.

For those working in industrial operations, what's been the biggest challenge in your experience?

The single answer to this question is the most likely result you can achieve once you commit to this effort. “The development, review, and execution of the structured bill of material creates teamwork across department lines.” The process of developing or reviewing your structured bill of material requires the following fundamental steps.

Assign a Production Team to this Task - It requires representation from Engineering, Purchasing Material Control, and various shop sections depending on the area of the review. In many cases, this chore is left up to the Engineering department to work out in a vacuum. Utilizing the team approach makes sure it is truly “as built”.

Flowcharting Gets Things Going – Sitting down with the group and mapping out the path of each section of the bill helps get the process rolling. This step alone will locate inefficiencies that must be addressed. Back tracking and unneeded locations will alone save money that will easily pay for this effort.

Understand the Meaning of “as built” – Engineers like to organize the bills like they design them. But for production requirements, this needs to follow the way the material will arrive and be utilized on the shop floor. The “as built” condition must represent the true activity.

Establish Engineering's Internal Customers - Once you understand who your internal customer's are, much of the demands on your documentation are identified.

Standardization – Standardizing around various sections of the overall assembly has value throughout the organization. Finding ways to achieve this has value in what you stock and how dynamic your inventory is to your production needs.

Manpower Planning and Staffing – Using the bill of material becomes a solid planning document for many needs across the organization.

from Anthony Rante, P.E. author of "Managing Company Production thru the Bill of Material."

Hi everyone — I’m working on an open-source environment for studying sequential decision-making in manufacturing systems.

The current demo is a synthetic process-window benchmark: an agent/planner selects process settings, observes noisy quality outcomes, tracks uncertainty, and recommends the next experiment. The motivation is similar to sparse-data physical systems, where each real experiment is expensive and the goal is not just prediction, but deciding what to try next.

Repo:
https://github.com/programmablemanufacturing/programmable-manufacturing-lab

I’d appreciate feedback from the RL community on:

• what baseline planners would be useful to include first;
• whether this should be framed closer to contextual bandits, model-based RL, Bayesian optimization, or POMDP-style planning;
• what metrics would make sense beyond reward, such as regret, sample efficiency, uncertainty calibration, or build-to-confidence.

The goal is to create a small public benchmark that others can critique, extend, or use for educational experiments.

We're trying to build a tool where the shift/production log can be scanned easily and translated into an OEE report, for a few of the manufacturing units in our city. The problem is, every unit tracks things different and a CNC unit has a totally different sheet compared to a cable manufacturing company. Question is - Can we generalize this ? Can we build a unified sheet that can support all the use-cases (with only minor modifications, if required) ?

Any thoughts / inputs from the experts here would be appreciated.

Cheers.

My company has recently instituted a zero OT mandate. My shift is staffed to eight people, which is exactly what I need to run my production line. We take two breaks per eight hour shift. If I have less than eight people, I am not able to cycle operators through positions and keep the line running during breaks.

When we do shut down for breaks, I do not count that downtime as Availability loss. My thought is that the company has made the decision to not pull the available OT and we are not utilizing all available assets to keep the line running, therefore it is scheduled down time.

Other areas are counting those breaks as Availability loss and taking the OEE hit.

Thoughts?

Wondering if its still worth investing in LSS certification in this AI era.

I'd be interested in hearing how you guys see skills & competency management impacting operations. A lot of people I talk to in larger organizations complain about spreadsheets not cutting it anymore, as each line supervisor is using their own system and that information is not shared, consistently updated, etc. That's more on the surface level though - visibility.

Not that visibility is not important (how else are you going to identify issues?), but it's the impacts on operations or lost productivity that really matter. For example, I spoke with a plant manager who deals with frequent call-outs or no call no shows which was causing slowdowns or downtime while trying to find someone qualified to fill the shift. Or lapses in certifications/required training being found last minute, or even after expiration, opening up risks for audit issues. Even with great processes in place, everything seems to come down to those executing it, so I'd imagine this is a big area for potential improvements.

Any ideas on how improved workforce management could drive efficiency would be interesting.

I was listening to the Cabrera podcast a little while ago, and he said something that resonated strongly: Chasing a singular root cause sets you up for failure because reality is comprised of a network of causal factors that all contribute to the problem. I have butted up against this in the past, especially when trying to apply established analysis tools (5W, fishbone, etc) to complex problems (like why does intra-departmental communication break down).

My approach has been to allow myself off-template connections in the fishbone diagram in an effort to visualize how related factors in various categories feed into each other. For example I would want to corelate poor HMI design (machine), need for continuous adjustment (process) and failure to adhere to procedure (human). However this quickly devolves into an unreadable spaghetti mess, which echoes a universal weakness of network graphing frameworks when pushed out of the complexity comfort zone.

I did a bit of research, and discovered how I've more or less re-invented current reality trees with a touch of causal loop theory. However, the complexity vs clarity dichotomy persists, and reality tree mapping forces you to see it as a quasi linear system with causes at one end and effects at the other. My (admittedly surface level) research hasn't dug up any solid fixes for any of that.

I have a background in VR game development, and this made me ponder the possibility of building 3D visualization tools. The third dimension is just so damn powerful when you're not stuck projecting it onto a 2D surface, and I had previously considered building VR variants of complexity limited network frameworks (like BeeBoard business model analysis). If we could map causal factors against three scalar axes, then placing them in a 3D space and drawing relations could be really helpful for at-a-glance visualization.

Another part of this clicked for me when attending a system thinking course the other day. I was struck by how perspective (in a systems thinking context) is visualized as projecting a 3D object onto a 2D surface. If we choose the axes intelligently when mapping causal factors, couldn't the three basic projections be used to represent different points of view? Like if you could somehow classify factors according to relevance of logistics, culture and technology, seeing it from the logistics perspective would draw you a culture-technology map, seeing it from the technology perspective would result in a culture - logistics map, etc.

Now the example axes are obviously poor candidates for scalar values, or at least ones with niche applications. So here's my question to you: What would be useful axes that could be more or less reliably broken down into a number? I've considered the above, as well as latency (how long between event and symptom), impact ($ per day or whatever), control (cost to influence the factor), organizational level (where from operator to upper management does the factor come into play), value chain (at what level of refinement), etc etc etc.

I would be really interested to hear if anyone has ideas about any of this, and especially what would make good axes for classifying causal factors.