Posted by Thelma Marshall, VP o9f Solutions, August 18, 2020

Share on facebook
Facebook
Share on twitter
Twitter
Share on linkedin
LinkedIn

Manufacturers realize that there is a need for big data analytics and predictive maintenance. They want schedules and choices to be less risky. But many still fall short of actively collecting the data they need to be proactive in management.

For example, most manufacturers have sensors installed that will alert managers on the floor when a piece of machinery breaks, but this only serves as a signal to call the repair technician. It is a reactive use of technology instead of a proactive action that uses historical data from past mechanical issues.

We either learn from history or it ends up repeating. Historical data is key to accurately predicting breakdowns or accidents before they occur— in time to prevent them.

 What is required for predictive management?

Predictive modeling and maintenance require both a variety of sensors and an understanding of previous problematic conditions— and how to learn from them. Historical data can be used by managers to identify a problem that is brewing based upon similar issues that happened in the past.

Manufacturing forecasting efforts also need to improve with AI and machine learning. This allows manufacturers to improve how they forecast product demands accurately, leading to higher efficiency and simpler scaling back of production to fit demand.

What are your machines telling you?

Machine learning is now an essential element that will revolutionize every aspect of supply chain and facility management. 

Software platforms merge what machines “experience” and patterns that emerge with advanced analytics, IoT sensors, and real-time monitoring. This quickly provides answers based on insight versus guesswork.

With increasing demand for speed, quality, accuracy and transparency, machine learning oversees and shapes daily warehouse operations. It creates:  
  • Intelligent warehouses – with increased tracking of operator patterns, order picking, inventory monitoring, and just-in-time deliveries 
  • Better communication – all systems can “talk” to each other; combined information that improves routing and traffic patterns 
  • Integration of all insight – telematics systems that partner with other SMART systems; create efficiencies that better track and monitor activity within the facility. 
  • Increased indoor locationing capabilities – systems are becoming more “context aware,” able to locate inventory (down to the foot) and suggest optimum forklift routing
  • Tailored operational parameters – forklift parameters based on location, battery charge state, presence of other vehicles, physical conditions at current location, historical performance and predictive calculations, etc. 
Machine-derived data has become invaluable in determining which causal factors most influence machinery performance and operational costs. Things like: 
  • Maintenance and repair schedules that support safer operation, less downtime, and increased productivity
  • Proper charging that extends battery lifespan
  • Operators’ skill level, recklessness or excessive speed. Software that learns operator patterns assists in safer equipment usage.

The needs for AI continually evolve, and manufacturers that continually expand their data sets are uniquely positioned to improve and grow at scale. Advanced telematics platforms facilitate machine learning and predictive management through matching patterns and providing easy-to-use data insight in real time.

Leave a Comment

Your email address will not be published. Required fields are marked *