Industry 4.0 research by analysing
35 million machine hours
Dasarathi G V
Director in Leanworx
Dasarathi has extensive experience in CNC programming, tooling, and managing shop floors. His expertise extends to the architecture, testing, and support of CAD/CAM, DNC, and Industry 4.0 systems.
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After analysing 35 million machine hours across 200 machines in indian shop floors , Here is what we found
Before Industry 4.0 Implementation
| Metric | Lowest | Highest |
|---|---|---|
OEE (Overall Equipment Effectiveness |
35% |
50% |
Spindle Utilization (CNC Machines) |
14% |
35% |
Operator Work Ethics (Downtime) |
5% |
30% |
Effect of Late Start/Early Stoppage |
10 min/shift (2%) |
60 min/shift (14%) |
Night Shift Downtime |
3 min/shift |
4 hrs/shift |
Parts Count Difference (Reported vs Actual) |
1% |
5% |
Cycle Time Deviation (Actual vs Standard) |
0% |
20% |
Before Industry 4.0 Implementation
| Metric | Lowest | Highest |
|---|---|---|
Increase in OEE |
+18% |
+33% |
ROI (Revenue Benefit) |
3x in 10 days |
60x in 60 days |
Why is Data important ?
There are big doubts about what industry 4.0 does, and what its benefits are .
India is no longer a low-cost country; it is a low-productivity country. This study shows the effect of data from an Industry 4.0 machine monitoring system across various shop floors.
Where is this Data from ?
Big Data collected from 2,000 machines on high-mix, low-volume (HMLV) shop floors across India, covering a wide range of metal-cutting and metal-forming industries in discrete manufacturing.
Ranging from MSMEs with 20 machines to large corporations with 1,200 machines, a total of 35 million machine hours has been collected by a Machine Monitoring System.
BEFORE implementation of industry 4.0 for machine data collection for LEAN
OEE of shop floor
OEE (Overall Equipment Effectiveness), weighted for the whole shop .
Highest - 50%
Lowest - 35 %
Spindle Utilization in CNC machines
Spindle running time as % of planned production time in aerospace firms .
Highest - 35%
Lowest - 14%
Operator work ethics
Machine Downtime due to poor operator work ethics in an 8 hour shift .
Highest - 30%
Lowest - 5%
Effect of late start , early stoppage
Downtime due to machines starting late at shift start and stopping early at shift end as % of planned production time during an 8 hour shift .
10min/shift - 2%
60min/shift - 14%
Downtime in night shift
Machine downtime due to extended period of zero production as % of planned production time during an 8-hour night shift .
Lowest : 3 min / shift
Highest : 4 hrs / shift
Parts count difference Reported vs Actual
Parts count difference between produced quantity reported by the operator vs Actual production quantity .
Lowest - 1%
Highest - 5%
Cycle time deviation Actual vs Standard
Cycle time difference between actual cycle time and standard cycle time as % .
Lowest - 0%
Highest - 20%
AFTER implementation of industry 4.0 for machine data collection by LEAN
Capacity utilization Effect of industry 4.0
Increase in OEE after implementation of machine monitoring system.
Lowest - 18%
Highest - 33%
Return on Investment (ROI)
Revenue benefit of machine monitoring system compared to the cost of the system
60x in 60 days
3x in 10 days
Conclusion
With traditional manual , paper based data collection , machine capacity utilization is 50% or less in HMLV (high mix low volume ) shop floors .
ROI is like paying for a full banana , but eating only 50% or less .
Key Takeaway :
Data is king , More machines CANNOT improve profitability but accurate data can.
Increase your profits automatically. Use Leanworx to get accurate and instant data 24/7.
