Predictive Maintenance on the Edge: Mitigate Supply Chain Issues

Avoid unplanned downtime: Manufacturers need equipment to process production loads when raw materials arrive. Sensors track key indicators, such as vibration or temperature anomalies while computer vision can monitor the quality of the product flowing through the manufacturing process. The integrated PAM system automatically alerts operators when sensor data falls outside of defined specifications or safety protocols. The ability to proactively identify and mitigate issues before they become major repairs or failures helps to extend the life of the asset and throughput

Automatically route production loads: Smart sensors provide operators with critical data on equipment status and digital twins simulate production alternatives. PAM technology can route production loads to different machines if issues arise. Manufacturers can achieve production goals and take other key actions, such as purchasing replacement parts to minimize downtime and slowdowns.

Optimize asset performance: Proactively servicing equipment assets can extend their lifespan significantly. This capability enables manufacturers to reduce CapEx investments and plan strategically for equipment replacements.

Improve worker safety: Equipment issues can create hazardous conditions, resulting in worker injuries. In the US, being struck by an object or equipment accounted for 9.7% of workplace incidents across all industries, whereas being compressed by running equipment or machines caused 3.7% of all accidents. Better to proactively detect and address issues before they harm workers.

Prevent latency issues: PAM requires the real-time processing of large data volumes to inform artificial intelligence models. Collecting sensor data from machinery, sending it over the cloud for analysis and returning analytics for action creates latency. By using edge technology, which computes information at or near the user and or the data source, manufacturers can gain real-time reporting on equipment exceptions. Even an incremental time advantage on issues can help manufacturers make crucial decisions, such as rerouting loads, taking equipment offline and scheduling immediate asset inspections and repairs, minimizing production impacts.

Develop a business case: Manufacturers should conduct an assessment to determine data and equipment readiness for PAM, as well as their budget for the initiative. Many will choose to work with a partner to speed this process, gain fresh insights and incorporate leading practices. The joint team will develop a business case that explores how to leverage new technology, like edge computing and digital twins, to gain new efficiencies. A roadmap should be crafted to deliver early and ongoing value, build internal capabilities and tackle new use cases when the time is right.

Prepare to execute: Organizations will want to collect sensor data from key assets and calibrate it with their manufacturing line to make sure assets are operating optimally. If manufacturers have older equipment that is difficult to update with digital sensors, they may want to prioritize new capital investments to digitize key processes. The best place to start is in a high-performing region at the divisional level. Key learnings from a local deployment can then be scaled.

Conduct an architectural review: The IT team will conduct a review of the current IT architecture and design the state needed to achieve PAM goals. This process involves executing assessments and interviews to understand the current level of technology maturity and what changes need to happen at a divisional, regional and enterprise level.

Deliver the solution(s): Partners can implement new technology, algorithms, and monitoring solutions. Manufacturers will gain self-healing code that can learn from big data, solve problems, and build on itself. Organizations can monitor and measure results and adjust solutions to deliver more value.