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CASE STUDY / INDUSTRIAL CONTRACT MANUFACTURING
CNC Milling Monitoring in Industrial Contract Manufacturing
How can an industrial contract manufacturer make hidden capacity losses visible across a mixed-generation CNC fleet? In this project, the relevant production environment included approximately 78 CNC machines, with a 3-machine pilot covering 3-axis and 5-axis milling equipment built between 2009 and 2018. The customer reported a 28.57% productivity improvement, while WatchMen helped make idle time, setup, tooling-related waiting, microstops and downtime visible.
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Industrial contract manufacturing: making productive CNC machining and hidden production losses visible.
+28.57%
higher productivity
Customer-reported result
78
production machines
Build years 1998–2014
15 min
sensor installation
Per machine in the project
In this case study Project · Implementation · Measures · Results · FAQ
THE PROJECT
At a Glance
Industry
Industrial contract manufacturing
Machine fleet
78 machines · Built between 2009 and 2018
Machine types
3-axis and 5-axis CNC milling
Operations
Operating model: 2× 8-hour shifts
Technology
External retrofit sensors + WatchMen
Focus
Productive machining, idle time, setup, tooling-related waiting, microstops and downtime
THE CHALLENGE
A Busy CNC Machine Can Still Hide Lost Productive Capacity
In high-mix CNC milling, a machine can remain occupied while setup, tooling, inspection, waiting, microstops and downtime reduce value-creating machining time.
Different operating patterns across 3-axis and 5-axis equipment can make direct comparisons even more difficult. The challenge was therefore to distinguish productive machining from the losses surrounding it and understand why individual jobs or machines were using more time than expected.
Productive Machining
Identify whether scheduled machine time is actually being used for value-creating machining.
Setup & Tooling
Make setup, changeovers and tooling-related waiting visible and consistently comparable.
Interruptions & Downtime
Reveal recurring microstops, longer waiting periods and downtime that can otherwise disappear inside overall order duration.
FROM MACHINE TO ANALYSIS
Measure. Interpret. Add Context.
Monitoring provides the physical machine timeline. Useful CNC analysis comes from validated machine states combined with the order, program, tooling and production context needed to understand why productive machining stops.
01
Capture machine behavior
External Novo AI sensors create a common machine-state layer across mixed-generation CNC equipment without requiring basic PLC access or machine-control intervention.
02
Validate machine states
Edge processing and validation support the distinction between productive machining, idle periods, setup activity, recurring interruptions and downtime.
03
Add production context
WatchMen links machine states with order and production information. Axis configuration, component family, geometry, program, tooling, batch size, inspection and quality requirements can then be considered when investigating deviations.
EXTERNAL SENSORS
CNC Milling Data Without PLC Access
External retrofit sensors capture physical machine behavior without requiring intervention in the CNC control system for the basic monitoring layer.
WatchMen turns validated signals into a machine-state timeline, making productive machining, idle periods, setup activity and recurring interruptions visible over time.
Order, program, tooling, quality, maintenance and operator context then help explain why a particular loss occurred.
TURNING DATA INTO ACTION
Turn Recurring CNC Losses Into Practical Improvements
Monitoring provides the machine evidence. The production team can then focus on the largest recurring losses and verify whether operational changes release usable CNC capacity.
01
Reduce hidden idle time
Review longer waiting periods around material readiness, next-order preparation, operator response and quality handoffs. Recurring idle patterns help identify where preparation and coordination need to improve.
02
Improve setup and tooling readiness
Prepare fixtures, tools, programs and next-job material before the machine becomes available. Pre-kit tooling and use offline presetting where practical to reduce avoidable tool-related waiting.
03
Reduce recurring interruptions and downtime
Investigate repeated microstops as well as longer machine faults. Combine the machine timeline with order, tooling and maintenance context, implement corrective actions and then compare the same indicators again.
RESULTS IN CONTEXT
What Improved During the Project
REPORTED PROJECT RESULT
−42.00%
less idle time
In the modeled production scenario, idle time was reduced by 42.00%. Detailed before-and-after values and the calculation basis are included in the complete case study PDF.
+1,714.2
additional productive machine-hours per year
Approximate annual capacity estimate across the 3 monitored CNC pilot machines.
Reported result: The customer reported a 28.57% productivity improvement. WatchMen helped make productive machining and recurring losses such as idle time, setup, tooling-related waiting, microstops and downtime visible for systematic investigation.
Annual capacity estimate: The modeled improvement corresponds to approximately 1,714.2 additional productive machine-hours per year across the three monitored CNC machines. This represents released machining capacity within the existing machine schedule 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 CNC Milling Monitoring
What was the scope of the project?
The relevant production environment contained approximately 78 CNC machines, including 3-axis and 5-axis milling equipment. The monitored pilot covered with machine build years spanning 2009 to 2018.
What productivity result did the customer report?
The customer reported a 28.57% productivity improvement. The detailed idle-time, setup, tooling and annual-capacity figures are modeled values used to interpret that reported result.
How much idle time was reduced?
In the modeled production scenario, idle time was reduced by 42.00%. Detailed before-and-after values and the calculation basis are available in the complete case study PDF.
How much additional productive capacity was calculated?
The model estimates approximately 1,714.2 additional productive machine-hours per year across the three monitored CNC machines. This is an annualized capacity estimate rather than a complete year of directly measured additional production.
What production losses can CNC monitoring make visible?
The case study considers productive machining, idle and waiting time, setup and changeovers, tooling-related waiting, microstops, downtime and other supporting production time.
Can both 3-axis and 5-axis CNC machines be monitored?
The relevant CNC environment in this case included both 3-axis and 5-axis milling equipment. When comparing machines, axis configuration, component geometry, programs, tooling and production requirements should also be considered.
THE COMPLETE CASE STUDY
Details to Support Your Decision
Download the English PDF with the project description, results and measurement notes.
Download the free PDF ↓APPLYING IT TO YOUR PRODUCTION
What Would You Like to Make Visible?
Tell us how many CNC milling machines you operate, whether you use 3-axis or 5-axis equipment and your most important production question: productive machining, idle time, setup, tooling-related waiting, microstops or downtime?
Together, we can identify suitable pilot machines and determine which machine states and recurring production losses should be made visible first.
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.