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CASE STUDY / PLASTIC HOSE MANUFACTURING
Plastic Hose Extrusion Monitoring
How can plastic hose manufacturers make hidden production losses visible around a continuous extrusion process? In this project, Novo AI WatchMen was used within a 103-machine production environment with machines built between 2015 and 2023. The customer reported a 28.58% productivity improvement, while machine-state data helped make idle time, setup, material changes, winding and coiling, microstops and downtime visible.
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Plastic hose manufacturing: making productive extrusion and hidden production losses visible.
+28.58%
higher productivity
Customer-reported result
103
production machines
Overall production environment
15 min
sensor installation
Per machine in the project
In this case study Project · Implementation · Measures · Results · FAQ
THE PROJECT
At a Glance
Industry
Plastic hose manufacturing
Production environment
103 production machines
Build years
2015–2023
Operations
Operating model: 3 × 8-hour shifts
Technology
External retrofit sensors + WatchMen
Focus
Productive hose extrusion, idle time, setup, material changes, winding/coiling, microstops and downtime
THE CHALLENGE
Continuous Hose Extrusion Still Loses Time Around the Run
Hose extrusion is a continuous process, but scheduled machine time can still be consumed by material changes, winding and coiling, process stabilization, idle periods, short interruptions and downtime.
Hose construction, material and process settings also change the production context. The challenge was therefore to distinguish productive extrusion from the losses surrounding the run and understand where recurring production time was being lost.
Productive Extrusion
Identify how much scheduled machine time actually becomes productive hose extrusion.
Setup & Material Changes
Make setup, material changes and process-stabilization periods visible and comparable.
Winding & Interruptions
Identify winding and coiling losses, recurring microstops and longer periods of downtime.
FROM MACHINE TO ANALYSIS
Retrofit. Validate. Understand.
Connecting the equipment creates the machine-data foundation. Reliable production analysis comes from validated machine states combined with the order, material and production context needed to understand why productive extrusion stops.
01
Retrofit the equipment
External Novo AI sensors create a common monitoring layer on existing production equipment without requiring PLC or machine-control intervention. Novo AI's retrofit benchmark is under 15 minutes per machine.
02
Validate machine states
Edge processing and validation support the distinction between productive extrusion, idle periods, setup activity, microstops and downtime.
03
Add production context
WatchMen links machine states with order and production information. Hose construction, thermoplastic material, geometry, tooling, winding format and quality requirements can then be considered when investigating recurring losses.
EXTERNAL SENSORS
Hose Extrusion Data Without PLC Access
External retrofit sensors capture physical machine behavior without requiring intervention in the machine control system.
WatchMen turns validated signals into a machine-state timeline, making productive extrusion, idle periods and recurring interruptions visible. Order, material, tooling and production context can then help explain why a particular loss occurred.
TURNING DATA INTO ACTION
Turn Recurring Hose-Extrusion Losses Into Practical Improvements
Monitoring shows where scheduled extrusion time is being lost. The next step is to investigate the largest recurring loss categories and verify whether process changes release usable production capacity.
01
Reduce hidden idle time
Review longer idle and waiting periods around next-order preparation, material staging and machine readiness. Use recurring patterns to improve preparation and response before productive extrusion should begin.
02
Improve material changes and winding readiness
Prepare compounds, tooling, changeover tasks and winding or coiling equipment before the line becomes available. Review recurring setup, material-change and downstream-handling losses to improve production readiness.
03
Reduce recurring interruptions and downtime
Investigate repeated microstops as well as longer equipment faults. Combine the machine timeline with material, process and maintenance context, implement corrective actions and then compare the same indicators again.
RESULTS IN CONTEXT
What Improved During the Project
REPORTED PROJECT RESULT
−40.63%
less idle time
In the modeled production scenario, idle time was reduced by 40.63%. The detailed before-and-after values and calculation basis are included in the complete case study PDF.
+857.4
additional productive machine-hours per year
Approximate annual capacity estimate for the monitored extrusion line.
Reported result: The customer reported a 28.58% productivity improvement. WatchMen helped make productive hose extrusion and recurring losses such as idle time, setup, material changes, winding and coiling, microstops and downtime visible for systematic investigation.
Annual capacity estimate: The modeled improvement corresponds to approximately 857.4 additional productive machine-hours per year on the monitored extrusion line. This represents released production capacity within the existing schedule rather than a full year of directly measured additional hose 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 Plastic Hose Extrusion Monitoring
What was the scope of the project?
The project record covers a production environment with 103 machines and a 3-machine pilot. The detailed extrusion-capacity model is based on the monitored extrusion line. Machine build years in the production environment span 2015 to 2023.
What productivity result was reported?
The customer reported a 28.58% productivity improvement. The detailed idle-time, loss-category 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 40.63%. The complete before-and-after values and calculation basis are included in the full case study PDF.
Does the 857.4-hour estimate apply to all 103 machines?
No. The 103-machine figure describes the wider production environment. The detailed annual extrusion-capacity estimate applies to the monitored extrusion line and should not be multiplied across all 103 machines.
Can existing extrusion machines be monitored without PLC access?
Yes. Novo AI's external retrofit approach can capture machine behavior without intervention in the machine control system for the basic monitoring layer.
Which production losses can monitoring make visible?
The case study considers productive extrusion, idle time, setup and material changes, winding and coiling, process stabilization, microstops and downtime.
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 extrusion machines or lines you operate, their approximate years of manufacture and your biggest production question: productive extrusion, idle time, material changes, winding and coiling, microstops or downtime?
Together, we can identify suitable pilot machines and determine which machine states and 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.