I have created a dashboard to monitor and analyze manufacturing line productivity and downtime factors. The goal of this project was to identify inefficiencies, understand operator performance, and estimate potential improvements if operators shared their best practices.
This post summarizes some of my key findings.
🔹 Product Efficiency
Among all products in the dataset, OR-600 stands out with the highest line efficiency. This makes it a benchmark product to compare against others in terms of process stability and operator handling.
🔹 Operator Performance
When comparing operators, Mac appears to be underperforming compared to his peers, despite the fact that Charlie actually logs more total downtime.
This is really strange, so I check for non-operator downtime.
It confirms that he’s underperforming compared to his peers.
But there’s another thing that needs attention, type of downtime:
- Batch Change:
Mac struggles significantly here, while the other operators show little difficulty. This impacts his overall performance.
- Machine Adjustment:
Interestingly, this is where the story flips. Machine adjustment is the single leading cause of downtime across all operators, but Mac actually performs better than everyone else in handling it.
This reveals that each operator has different strengths and weaknesses, and no one operator is simply “bad” or “good.”
🔹 Leading Downtime Factor
From the dashboard, machine adjustment emerges as the most significant contributor to downtime.
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Most operators struggle with it.
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Mac, however, manages it well, giving him an edge in this particular area.
Understanding this factor is crucial, because any improvement in machine adjustment processes will directly translate into large efficiency gains for the entire line.
🔹 What If Operators Shared Knowledge?
Instead of working in silos, what if operators could share their expertise?
By simulating this “knowledge sharing” scenario, I estimate a potential efficiency gain of around 16%.
Here’s the math:
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Current average cycle time per batch = 101 minutes
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With knowledge sharing = 85 minutes per batch
That’s a reduction of 16 minutes per batch, which compounds into significant time savings at scale.
🔹 Takeaways
This analysis highlights a few key points:
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OR-600 is the most efficient product line and can serve as a benchmark.
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Operator performance is nuanced—Mac work more slowly and underperform in batch changes but excel in machine adjustments.
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Machine adjustment is the top downtime factor, making it the biggest opportunity for improvement.
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Knowledge sharing could reduce average cycle time per batch by 16%, a major gain for overall productivity.
