Your team has bought a new deep learning-based machine learning solution to predict when any one of the 10 000 machines needed in your product production line will fail. The team is excited by the new predictive maintenance AI model that reports “prediction accuracy is 98%” in predicting if a given machine will fail in the coming month.
The new ML system is being presented to the CEO who needs to implement a business decision based on this system. He is presented with the fact that the cost of not replacing a machine that will fail is 100,000 USD since it will shut down the production line, while the cost of replacing a machine before a failure occurs is only 50,000 USD.
How would you implement the decision process for predictive maintenance based on this new machine learning solution?
Explore what decisions should be made in each case by interacting with the simulation.
| After 1 month with 10 000 machines in operation | Expected Results |
|---|---|
| Total Failures | |
| ... of which correctly predicted as failing | 2012 |
| ... of which incorrectly predicted as not failing | |
| Total Non-Failures | |
| ... of which correctly predicted as non failing | 2012 |
| ... of which incorrectly predicted as failing |
| Business Value Analysis | Expected Cost | Best |
|---|---|---|
| Strategy 1: Replace machine whenever failure predicted | ||
| Strategy 2: Ignore ML system and replace upon actual failure |
| Preferred Parameter Setting | |
|---|---|
| Sensitivity | |
| Specificity | |
| Probability of part failure | |
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