Existing best practices, such as scheduled preventative maintenance, can mitigate failure, but will not catch unusual, unexpected failures
Scheduled maintenance can also be excessively conservative, resulting in excessive downtime and maintenance costs
Existing process to identify these defects requires manual inspections which are resource-intensive, tedious assignments for maintenance engineers responsible for quality assurance.
Opportunities
Using AI to automate the process allows operators to identify subtle or unknown issues with equipment operation in the collected sensor data, schedule maintenance when maintenance is truly needed, or be automatically notified to intervene when a sudden failure is imminent.
AI will empower your maintenance staff with the information they need to keep operations running without any disruptions
Energy Management
Challenges
Manufacturing operations can be quite energy intensive. waste nearly 30% of that energy.
There can be various causes of energy waste in manufacturing including leaky air compressors, inefficient equipment, and unnecessary heating/cooling in buildings.
Energy waste combined with unanticipated demand can lead to conflated energy costs and be a drag on profitability.
Opportunities
Reduce energy expenditures using predictive analytics to identify high-value savings opportunities
Forecast consumption and predict peak demand events with tailored machine learning algorithms that provide high degrees of accuracy
Monitor asset health and equipment efficiency in real time to improve system reliability and manage costs
Optimize cost- and carbon-saving investments with machine learning insights and ‘what if’ scenario analysis
Achieve sustainability goals with tailored carbon-reduction insights, AI-powered measurement & verification, and streamlined financial and carbon emissions reporting