How an expanded-metal manufacturer used Novo AI’s WatchMen platform across 15 production machines to uncover performance losses, reduce nonproductive time by approximately 32.8% and recover an estimated 2,823 hours of productive capacity per year.
External retrofit sensors connected the existing machines without PLC access. Combining machine activity with ERP/BDE information helped the team investigate stroke performance, recurring interruptions and differences between shifts.
Industry: Expanded Metal Manufacturing
Application: Stretching and Cutting Machines
Scope: 15 Production Machines · Three Shifts
Solution: Novo AI Retrofit Sensor + WatchMen
Focus: Stroke Performance, OEE, Nonproductive Time and Production Planning
Stroke performance
Compare actual stroke output with planning targets in the context of each order.
Hidden production losses
Identify waiting periods, microstops and extended downtime during production runs.
Shift differences
Compare machine activity across shifts while accounting for material, product and machine differences.
A running machine could still fall behind the production plan
The production plan set an expected output, but it did not fully explain what happened during each order. Slower stroke performance, waiting periods and brief interruptions accumulated over production runs that often lasted many hours.
The team needed to distinguish avoidable losses from targets that required adjustment for the material, mesh geometry or machine. Increasing stroke output was only useful when finished expanded metal remained within specification.
One monitoring layer across the existing machine fleet
Novo AI connected all 15 production machines using external retrofit sensors, without accessing the PLC or changing the machine controls. Sensor installation took approximately 15–20 minutes per machine, with no additional shutdown required for installation.
An initial learning and validation phase established a monitoring baseline. Direct ERP/BDE integration connected machine activity with order information, while employee onboarding and improvement routines were introduced over approximately two months.
Investigate low stroke performance
Compare stroke output and machine states across shifts. Check the production conditions behind sustained deviations before deciding what needs to change.
Reduce waiting and recurring interruptions
Use machine timelines to identify extended stops and repeated microstops. Combine these events with operator feedback and BDE information to investigate the causes.
Improve production planning
Review whether each target is realistic for the article, material and machine. Address avoidable process losses and correct planning assumptions where needed.
WatchMen provided the evidence. The production team used it to review recurring losses, adjust daily routines and evaluate the results.
A result worth exploring
32.8% less nonproductive time · An estimated 2,823 additional productive hours per year
The project reported a reduction in the share of nonproductive time from approximately 49.7% to 33.4% after a three-month improvement period.
The annual capacity gain is an approximate estimate across the 15-machine fleet, not a full year of directly observed additional output.
Can older stretching and cutting machines be monitored?
This project connected machines built from the 1980s through 2010. External sensors enabled monitoring without PLC access. Novo AI can assess your machines and monitoring requirements with your team.
Does installation interrupt production?
Sensor installation took approximately 15–20 minutes per machine in this project, with no additional shutdown required. Signal validation and employee onboarding followed installation and were separate stages of the rollout.
Which production metrics does this case study cover?
The case study examines stroke performance, OEE, productive-time share, nonproductive time, microstops and shift differences. It includes before-and-after results and an illustrative calculation of how stroke performance can affect order duration.
Can WatchMen connect with our ERP or BDE system?
The project included direct ERP/BDE integration to connect order information with actual machine activity. Novo AI can review your available interfaces, production data and integration requirements.
Does higher stroke output always mean better production?
No. Achievable performance depends on the material, mesh geometry, tooling, machine and required product quality. In this project, higher stroke output only counted as an improvement when the finished expanded metal remained within specification.