Predictive Maintenance
By implementing predictive maintenance services, organizations can keep critical assets and systems operational for as long as possible. This allows organizations to leverage existing data to proactively address potential outages and disruptions, rather than reacting when problems arise. This includes:
Reduce costs by reducing unscheduled downtime, reducing redundant inspections and ineffective preventative maintenance. Savings come from increased productivity and reduced labor and material costs.
Reduce equipment life cycle costs through improved performance and extended equipment life. Indirect benefits such as better quality, less rework, fewer errors, better safety, and better energy efficiency.
McKinsey data shows that predictive maintenance tools can reduce production machine downtime by 30-50% and extend machine life by 20-40%. Manufacturers can also improve their operations and keep their supply chain intact.
Predictive maintenance has great potential to improve efficiency and productivity in multiple industries that rely on assets that require frequent repairs.
Manufacturers can use predictive maintenance technology to implement safeguards that notify appropriate personnel when equipment requires inspection. Using existing historical data such as current, vibration and noise generated by devices, manufacturers can create models and predict potential failures before they occur. These models can identify which devices are most at risk of failure so maintenance teams can respond accordingly. Insights gained from models fitted to historical data can also help reveal the root cause of problems and alert operators to underlying problems.
Supply chain operators can also use predictive maintenance analytics to plan equipment downtime and potential disruptions. Insights in the model let supply chain teams know how long an asset, system, or component may be offline, so they can plan accordingly. Original equipment manufacturers (OEMs) can offer predictive maintenance as a service. By collecting data from multiple customer devices, OEMs can build models using data collected from a broader customer base to provide individual customer insights and device-specific maintenance plans.
Government agencies can also benefit from implementing proper predictive maintenance technology. Automated machine learning for predictive maintenance helps authorities understand when military equipment such as helicopters, airplanes and weapons systems will need new parts, components and overhauls. By deploying predictive maintenance models powered by AI and machine learning, agencies can operate more efficiently, keep expensive assets running longer, and improve supply chain operations. By running models that can predict patterns based on the environment of various assets,
InfiTekPro helps government and other public sector officials deal with time-consuming Failure Modes, Effects and Severity Analysis (FMECA). Helpful. These predictive maintenance models lead to more accurate asset and component life and can be applied to other use cases such as accident analysis and work optimization.