A smart factory makes decisions on real data
It doesn't start with robots or big screens. It starts with knowing exactly what the shop floor is doing, where it is off track, and what is about to go wrong.
Four questions to test your factory
If you can't answer them right away, your factory is running on incomplete information.
Right now, which orders are at risk, and for which customers?
Usually you only find out at the next morning's meeting.
Yesterday, how many hours did your most critical machine actually produce?
Without this number, buying another machine is a guess.
When an order starts to slip, how many days before the due date do you know?
Warning the customer early protects your reputation. Letting them find out costs it.
What is your on-time delivery rate?
Every major customer is tracking it.
What a smart factory is not
| It is not | It is |
|---|---|
| Robots and automation | Automation pays off once data shows where to automate |
| Big screens on the shop floor | A screen has value when it leads to a decision |
| AI | AI needs good data first |
| Software you buy and you're done | A capability built step by step with your data and your way of working |
| Only for large factories | A fit for any factory with many item codes and process steps |
Three capabilities
A smart factory is measured by three capabilities:
See
Know immediately what is happening on the shop floor.
Missing when: you have to phone someone to find out.
Link
Know how far you are off plan, and which orders and customers are affected.
Missing when: you know a machine stopped but not which order it affects.
Predict
Know what is about to happen, in time to act and plan ahead.
Missing when: you only find out after it happens.
Link creates the most value: it ties machine status to orders and customers.
Maturity scale
Recognize your maturity level by how the factory answers everyday questions.
| Level | Name | What you typically hear |
|---|---|---|
| 0 | Paper and memory | "Let me check with the night-shift team leader." |
| 1 | Excel and reports | "I'll compile it at the end of the shift and let you know." |
| 2 | See | "Machine 3 stopped for 40 minutes this morning, not sure why yet." |
| 3 | Link | "Customer A's order is running late, with 2 days of buffer left." |
| 4 | Predict | "Customer A's order will be 1 day late unless we change the run sequence." |
| 5 | Self-adjusting | "The system has re-sequenced, and customer A's order is back on time." |
For most factories, the realistic target is level 2 to 3. That is also where the return on investment is highest.
Seven reasons projects fail
Most of them have nothing to do with technology.
01 · Buying software before standardizing data
Wrong numbers from day one destroy trust in the whole system.
02 · Asking operators to enter more data
Add work, and within a few months the data is no longer complete.
03 · Rolling out to the whole factory at once
It takes too long, and patience runs out before results appear.
04 · Automating before measuring
If you don't know where the losses are, the investment easily lands in the wrong place.
05 · Handing the project down the line or to IT
The system changes how operations run and makes the numbers transparent. Without the top executive leading it, the project tends to stall and the data isn't used for decisions.
06 · Measuring what is easy instead of what matters
Plenty of KPIs, yet still no answer to which order is about to be late or which machine is losing hours.
07 · Choosing a vendor by feature list
More features don't make a system usable. What decides success is understanding the shop floor and staying with you until the system is used every day.
Assess your level
A free 1-day assessment at your factory: a hands-on review of your shop floor to find your level on the maturity scale, your three biggest bottlenecks, and where to start.