Actual Production Rate = Total Units / Operating Time = 10,800 / 435 minutes = 24.827 units/minute
Performance = (Actual Rate / Ideal Rate) * 100%
(24.827 / 30) * 100% = 82.757%
3. Calculate Quality
Good Units = Total Units – Defective Units = 10,800 - 216 = 10,584 units
Quality = (Good Units / Total Units) * 100%
(10,584 / 10,800) * 100% = 98%
4. Calculate Final OEE
OEE = Availability * Performance * Quality
0.90625 * 0.82757 * 0.98 = 0.7347
OEE ≈ 73.47%
The OEE calculation breaks down machine effectiveness into three core components. The IIoT data allowed us to precisely measure unplanned downtime for availability, actual production rate versus ideal for performance, and defective units for quality. The resulting OEE of 73.47% indicates areas for improvement, particularly in performance (speed losses) and availability (downtime).
This calculation helps in network planning. By estimating the data throughput, you can ensure your existing network infrastructure can handle the load. A 10.56 Kbps requirement is relatively low, indicating that common industrial networks could likely handle this specific IIoT deployment.
Solution for Exercise 3: Cost Savings
1. Calculate Costs Before IIoT
Monthly Cost Before = 4 breakdowns/month * $5,000/breakdown
= $20,000 per month
2. Calculate Costs After IIoT
Monthly Cost After = 0.5 breakdowns/month * $5,000/breakdown
= $2,500 per month
3. Calculate Gross and Net Savings
Gross Savings = Cost Before – Cost After = $20,000 – $2,500 = $17,500
Net Savings = Gross Savings – IIoT System Cost
$17,500 - $800 = $16,700
Net Monthly Savings = $16,700
This exercise demonstrates the direct financial impact of moving from reactive to predictive maintenance using IIoT. Even with the cost of the IIoT system, the significant reduction in unplanned downtime leads to substantial net monthly savings, highlighting a clear return on investment.
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