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CASE STUDY / AUTOMOTIVE COMPONENT MANUFACTURING
Forging Machine Monitoring in Automotive Component Manufacturing
How can automotive component manufacturers make hidden losses visible across an existing hot-forging fleet? In this project, Novo AI WatchMen was applied to a documented fleet of 6 hot-forging machines. The supplied project result reports a 24.93% productivity improvement, while machine-state data helped make idle time, die changes, billet waiting, microstops, downtime and reduced-performance production visible.
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Hot forging in automotive component manufacturing: making productive utilization and hidden production losses visible.
+24.93%
higher productivity
Supplied project result
6
hot-forging 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
Automotive component manufacturing
Machine fleet
6 hot-forging machines
Documented press investments
2004 and 2010
Operations
Operating model: 3 × 8-hour shifts
Technology
External Novo AI retrofit sensors + WatchMen
Focus
Productive forging, idle time, die changes, billet waiting, microstops, downtime and reduced-performance production
THE CHALLENGE
A Running Forging Press Can Still Hide Lost Productive Capacity
Hot forging is repetitive and cycle-based, but a running press is not automatically using its available capacity productively. Die changes, billet readiness, reduced-performance production, recurring microstops and downtime can all consume scheduled machine time.
The challenge was therefore to separate productive forging from the losses surrounding it and understand where usable forging capacity was being lost across the existing fleet.
Productive Forging
Identify whether the press is actually forming productively rather than simply running.
Die Changes & Billet Readiness
Make setup, die changes and material waiting visible and consistently comparable.
Interruptions & Reduced Performance
Reveal microstops, downtime and periods when the press is running below expected performance.
FROM MACHINE TO ANALYSIS
Measure. Interpret. Add Context.
Machine monitoring provides the physical production timeline. Useful forging analysis comes from validated machine states combined with the part, die, billet, heating and production context needed to explain recurring losses.
01
Capture machine behavior
External Novo AI sensors capture physical machine behavior and create a common monitoring layer across existing hot-forging equipment without requiring basic PLC access or machine-control intervention.
02
Validate states and cycle patterns
Edge processing and validation support the detection of productive forging, idle periods, setup activity, recurring interruptions and cycle-related behavior.
03
Add production context
WatchMen links machine states with order and production information. Part family, material, billet size, die configuration, forming stages, heating, transfer sequence and quality requirements can then be considered when investigating performance differences.
EXTERNAL SENSORS
Forging Machine Data Without PLC Access
External retrofit sensors capture physical machine behavior on existing forging equipment without requiring intervention in the machine control system for the basic monitoring layer.
WatchMen turns validated signals into a common machine-state timeline. This makes productive forging, idle periods, recurring interruptions and below-target operating periods visible over time.Order, die, billet, heating, maintenance and production context then help explain why a particular loss occurred.
TURNING DATA INTO ACTION
Turn Recurring Forging 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 forging capacity.
01
Reduce hidden idle time
Review longer waiting periods around next-order readiness, support response and machine availability. Repeated idle patterns can show where preparation or coordination needs to improve before the press should return to productive operation.
02
Improve die-change and billet readiness
Pre-stage dies, tools, programs and changeover tasks before the press becomes available. Coordinate billet preparation, heating, material supply and transfer so that technically available machines do not wait unnecessarily for the next production step.
03
Reduce interruptions and below-target production
Separate longer downtime, recurring microstops and reduced-performance running. Combine the machine timeline with technical, material and production context, implement corrective measures and then compare the same indicators again.
RESULTS IN CONTEXT
What Improved During the Project
REPORTED PROJECT RESULT
−30.56%
less idle time
In the modeled production scenario, idle time was reduced by 30.56%. The detailed before-and-after values and calculation basis are included in the complete case study PDF.
+4,487.4
additional productive machine-hours per year
Approximate annual capacity estimate for a six-machine forging rollout scenario.
Supplied project result: The project data reports a 24.93% productivity improvement. WatchMen helps make productive forging and recurring losses such as idle time, die changes, billet waiting, microstops, downtime and reduced-performance production visible for systematic investigation.
Annual capacity estimate: The modeled improvement corresponds to approximately 4,487.4 additional productive machine-hours per year across a six-machine rollout scenario. This represents modeled released forging capacity within the existing scheduled time rather than a full year of directly measured additional output across all six presses.
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 Forging Machine Monitoring
Which machines were included in the case study?
The documented forging environment consists of 6 hot-forging machines: 1 HM75, 3 AMP70 and 2 AMP50 presses. The source documents press investments in 2004 and 2010.
What productivity result was supplied?
The supplied project result reports a 24.93% productivity improvement. Detailed idle-time, loss-category and annual-capacity figures are modeled values used to interpret that result.
How much idle time was reduced?
In the modeled production scenario, idle time was reduced by 30.56%. Detailed before-and-after values and the calculation basis are included in the full case study PDF.
How much additional productive capacity was calculated?
The model estimates approximately 4,487.4 additional productive machine-hours per year for a six-machine rollout scenario. This is an annualized model result rather than a full year of directly measured additional output across all six presses.
What losses can forging monitoring make visible?
The case study considers productive forging, idle time, die changes and setup, billet waiting, downtime, microstops and reduced-performance production.
Can existing forging machines be monitored without PLC access?
The Novo AI retrofit approach uses external sensors to capture physical machine behavior, allowing a basic monitoring layer without direct PLC or machine-control intervention.
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 forging machines you operate, the machine types and your most important production question: productive utilization, idle time, die changes, billet waiting, recurring microstops, downtime or reduced-performance operation?
Together, we can identify suitable pilot machines and determine which machine states and recurring 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.