Case Study: Real‑Time Production Tracking Reduces Hidden Losses on a CNC Line
How a high‑volume automotive machining line kept missing output targets despite modern equipment and experienced operators.
Before digital tracking, production performance was tracked mainly through manual counting and shift‑level summaries. This created three major gaps:
The Visibility Problem
A Tier‑1 automotive components manufacturer operates multiple CNC machining lines for engine components at one of its plants. One finish pin‑hole boring line (Takisawa CNC) consistently under‑delivered against its planned daily output of 1,842 units, even though demand and upstream supply were stable. Management suspected that the problem was not capacity on paper, but hidden performance losses on the shop floor—idle time, downtime, and inconsistent operator performance that did not show up clearly in traditional reports.
Manufacturing Context
No real‑time visibility: Counts were verified at the end of the day; if output dropped mid‑shift, there was no way to see it and correct in time.
Hidden time losses: Idle time between cycles, extended cycle times, and large downtime patches were not captured accurately, so there was no objective view of where time was being lost.
Unclear operator and shift differences: It was difficult to compare shifts and operators fairly, and to understand why some combinations performed better than others.
To solve this, our real‑time production‑tracking solution was deployed on the CNC line using an IIoT 4.0 wireless architecture, providing second‑by‑second visibility into production, cycle time, idle time, and downtime.
Real‑Time Tracking
A wireless gateway was connected to the CNC to acquire machine signals without interfering with control logic.
Production data (cycle start/stop, unit count, status) was transmitted to the cloud every one second, providing high‑resolution visibility of what the machine was doing throughout the day.
A cloud‑based dashboard visualized live production counts, cycle time, idle time, downtime, and shift output.
This setup delivered “track production count on each machine in real time and reduce production loss” by giving both supervisors and operators transparent, up‑to‑date information on performance.
What The Numbers Showed
A best hourly performance of 85 units, which served as a benchmark for what was realistically achievable.
Average hourly production of 66 units, with clear peaks and troughs across the day.
Once real‑time tracking was active, the line’s true performance profile became clear.
Because data refreshed every second, supervisors could see hours where production started drifting below the average and immediately alert operators that output was going down.
On 1 August, the system showed:
On some days, specific shift recorded three consecutive days with zero production, a pattern that did not show up in the usual daily total reports.
Shift‑Wise and Operator Performance
The system also enabled shift‑wise tracking and heat‑map visualizations of performance.
On 1st August, total output of 1,573 units was distributed across three shifts, with clear differences between them.
On 8th August, the third shift achieved 550 units, while the first shift delivered only 93 units, highlighting a major imbalance in utilization
Heat maps summarizing first, second, and third shift performance over the period showed:
Continuous low production in the first shift on certain days.
Very low output on 3rd, 4th and 5th August compared to 1st August.
Zero output periods in the third shift on multiple days.
Cycle times of 42–43 seconds were observed compared to a target of 37 seconds.
Idle Time, Cycle Time, and Short Cycles
Beyond counts, the system captured time‑based losses:
Idle times of 20–29 seconds were frequently observed compared to a target idle time of 6 seconds.
These extra seconds, repeated over hundreds of cycles, added up to significant lost production time.
Even “small” drifts of 5–6 seconds per cycle reduced potential throughput over the day.
On 5 August in the second shift, 17 short cycles were recorded, while total units produced in that shift were only 490.
Short cycles indicated possible operator interventions or process anomalies that needed investigation.
Short cycles:
Cycle time drift:
Idle time between cycles:
By turning second‑by‑second machine data into clear production insights, the project gave the plant a practical way to expose hidden losses and act on them quickly. Teams could see exactly when, where, and why output slipped against the 1,842‑unit daily target on the CNC line.
BENEFITS OF TECH
Real-time visibility & transparency of production – Take Corrective action in Real-time
Track Production count on each machine in Real-time & Reduce Production Loss
Real-time visibility & transparency of operator's performance
Reduce Idle time & M/C Downtime in Real-time
Track & Reduce Excessive Cycle time