Prescriptive maintenance is equivalent to predictive maintenance, but the attempt to automate the maintenance process goes one step further. Rather than merely tracking and offering suggestions, the goal is to leverage Machine Learning and AI to enable the machine to make its own choice on maintenance measures.
It is critical for manufacturers to limit maintenance costs and prevent downtime. Even brief periods of downtime can massively impact revenue, waste materials and require additional labor. Yet, many manufacturers take traditional maintenance approaches that often fail to prevent downtime and lead to unnecessary costs.
- A reactive maintenance approach is where you let assets to run until they fail and only then perform maintenance, inherently resulting in downtime.
- A scheduled maintenance approach is based on a calendar instead of a need, leading to unnecessary maintenance on healthy assets or a larger, more expensive failure occurring in between services.
- 800 hours Amount of annual downtime manufacturers often face.
- 20% Of production lost by many factories due to asset downtime.
- $30-$50k Typical cost of downtime in many industrial settings per hour.
- 42% Of unplanned downtime is the result of equipment failure.
cost savings compared to scheduled repairs
reduction of overall maintenance cost
reduction in asset breakdown
Prescriptive maintenance using MindSphere can help you not only predict when asset failure might occur but gain a thorough understanding of the best way to respond.
- Track the condition of your critical assets in real-time to identify faults and better predict failure.
- Understand the outcomes and ramifications of potential remedial actions to make more informed decisions.
- Gain alerts with the optimal remedial actions to take, accelerating time to response.
- Proactively respond to asset faults to prevent potential failures and unplanned downtime.