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CASE STUDY / SHEET METAL MANUFACTURING
Laser Cutting Machine Monitoring in Sheet Metal Manufacturing
How can sheet metal manufacturers make hidden production losses visible across a large laser-cutting machine fleet? In this project, the scope covered 136 machines built between 2012 and 2022. WatchMen helped make productive cutting, idle time, setup, loading, microstops and downtime visible, while the customer reported a 26.4% productivity improvement.
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Laser cutting in sheet metal manufacturing: making productive cutting and hidden production losses visible.
+26.4%
higher productivity
Customer-reported result
136
machines in project scope
Built between 2012–2022
15 min
sensor installation
Per machine in the project
In this case study Project · Implementation · Measures · Results · FAQ
THE PROJECT
At a Glance
Industry
Sheet metal manufacturing
Machine fleet
136 machines · Built 2012–2022
Application
Laser cutting
Operations
Calculation model: 2 × 8-hour shifts
Technology
External retrofit sensors + WatchMen
Integration
Direct ERP/BDE connection
Focus
Productive cutting, idle time, setup, loading, microstops and downtime
THE CHALLENGE
A Running Laser Can Still Hide Lost Capacity
Laser cutting is highly automated, but scheduled machine time can still be lost through setup, loading, material waiting, short interruptions and downtime. Material type, thickness, nesting and order size also change the production context.
Order start and completion may already be visible, but they do not explain how the machine used the time in between. The production team therefore needed a clearer view of productive cutting and the losses surrounding it. Novo_AI_Laser_Cutting_Machine_M…
Productive Cutting
Identify how much scheduled machine time actually becomes productive cutting.
Setup & Loading
Make setup, order changes, loading and unloading visible and consistently comparable.
Interruptions
Reveal recurring microstops, longer waiting periods and downtime that can disappear inside overall order duration.
FROM MACHINE TO ANALYSIS
Retrofit. Validate. Understand.
Connecting the machines creates the data foundation. Reliable production analysis comes from validated machine states combined with the order and production context needed to understand why productive cutting stops.
01
Retrofit the machines
External Novo AI sensors create a common monitoring layer across different laser-cutting machine generations without requiring basic PLC access or intervention in the machine control. The supplied installation time is approximately 15 minutes per machine.
02
Validate machine states
Edge processing and validation help distinguish productive cutting from idle periods, setup activity, microstops and downtime. This creates a consistent machine-state timeline across the project scope.
03
Add production context
WatchMen connects machine behavior with order and production information. Material, thickness, geometry, batch size and quality requirements can then be considered when investigating why performance differs between machines or orders.
MACHINE ACTIVITY
Laser Cutting Data Without PLC Access
External sensors capture physical machine behavior and provide the basis for identifying when productive cutting starts or stops and whether a pattern repeats.
WatchMen turns the validated signals into a common machine-state timeline. Order data, material context and operator or production information then help explain why a particular loss occurred.
TURNING DATA INTO ACTION
Turn Recurring Cutting Losses Into Practical Improvements
Machine monitoring shows where scheduled time is being lost. The next step is to focus on the largest recurring losses and verify whether process changes release usable cutting capacity.
01
Reduce hidden idle time
Review extended waiting periods around material availability, next-order preparation and machine readiness. In the calculation model, idle time falls from 560 to 340 hours per machine and year, releasing 220 hours per machine.
02
Improve setup, loading and order changes
Prepare programs, tools, materials and order changes before the machine becomes available. Review material staging and handling flow to reduce recurring loading and unloading delays. In the model, setup/order-change time falls from 480 to 360 hours per machine annually, while loading/unloading falls from 400 to 300 hours.
03
Reduce downtime and recurring microstops
Investigate both repeated short interruptions and longer machine faults. Use the machine timeline together with maintenance and production context, then compare the same indicators after corrective action. In the model, downtime falls from 240 to 190 hours and microstop time from 160 to 132 hours per machine annually.
RESULTS IN CONTEXT
What Improved During the Project
REPORTED PROJECT RESULT
−39.29%
less idle time
In the supplied operating model, idle time falls from 560 to 340 hours per machine per year. This represents a modeled reduction of 220 idle hours per machine annually.
+71,808
additional productive machine-hours per year
Approximate calculated annual capacity gain across the 136-machine project scope.
Reported result: The customer reported a 26.4% productivity improvement. Machine monitoring made productive cutting and recurring losses such as idle time, setup, loading, microstops and downtime visible for systematic investigation.
Annual capacity estimate: The 71,808 additional productive machine-hours represent the estimated annual gain across the complete 136-machine project scope. This is an annualized capacity estimate rather than a full year of directly measured additional output.
Context: These figures relate to the project described. Starting conditions, product mix, shift model and the measures implemented affect the result. A reduction in time losses does not automatically mean an equally large increase in OEE.
GOOD TO KNOW
Questions About Laser Cutting Machine Monitoring
Which machines were included in the case study?
The project scope covers 136 machines built between 2012 and 2022 in a sheet-metal manufacturing environment.
What productivity result did the customer report?
The supplied project result reports a 26.4% productivity improvement. The detailed productive-time, idle-time and annual-capacity figures are modeled calculations used to translate that result into a consistent machine-time scenario.
How much idle time was reduced?
In the calculation model, idle time falls from 560 to 340 hours per machine per year. This represents a relative reduction of 39.29%, or 220 released hours per machine annually.
How much additional productive capacity was calculated?
The annual model estimates 71,808 additional productive machine-hours across the 136-machine project scope. The figure is an annualized capacity scenario rather than a full year of directly observed additional output.
How long does sensor installation take?
The supplied installation value is approximately 15 minutes per machine. The external sensors provide the basic monitoring layer without requiring PLC or machine-control intervention. Across 136 machines, this equals around 34 cumulative installation hours, but this does not mean 34 hours of production downtime.
Is productive-time share the same as OEE?
No. Productive-time share describes how scheduled machine time is divided between productive and nonproductive states. OEE combines availability, performance and quality and therefore requires additional information.
THE COMPLETE CASE STUDY
Details to Support Your Decision
Download the English PDF with the project scope, implementation approach, calculated production losses, capacity scenario and measurement notes.
Download the free PDF ↓APPLYING IT TO YOUR PRODUCTION
What Would You Like to Make Visible?
Tell us how many laser cutting machines you operate, their approximate years of manufacture and your biggest production question: productive cutting time, idle time, setup, loading, microstops or downtime?Together, we can identify suitable pilot machines and determine which losses and production conditions should be made visible first.
Discuss your application →Further reading: CNC Monitoring in Precision Tool Manufacturing · Understanding machine data acquisition: data and system boundaries · All Case Studies
Publisher: Novo AI GmbH. Source of the project information: the corresponding case study PDF. Definition and calculation: OEE based on availability, performance and quality.