Moving from Predictive Maintenance to Predictive Resolution

Predictive maintenance has been utilized in manufacturing for several years and remains a strong standard within manufacturing. However, this idea has begun to evolve from simple maintenance to the concept of “predictive resolution.” While still in its early stages, predictive resolution can help manufacturers identify and rectify problems before they occur.

Background on Terminology

What is the difference between predictive maintenance and predictive resolution? The terms are closely linked and share common goals. Predictive maintenance’s primary goal is to use data in identifying anomalies in equipment performance and determine their root cause. Predictive resolution goes a step further, offering technicians insights on how to resolve many of these issues with greater, data-driven certainty.

According to an article from Hitachi Solutions’ Martin Boggess titled “10 Trends that Will Dominate Manufacturing in 2023,” predictive resolution “increases the likelihood that an issue will be sufficiently addressed on the first try, thereby enabling manufacturers to enhance equipment efficiency, reduce costs, and improve their first-time fix rate.” As the technology involved in predictive resolution becomes more sophisticated, it will likely also become more affordable.

This opens a world of possibilities to manufacturers. Predictive resolution, along with the Industrial Internet of Things (IIoT), can help ensure that equipment operates as intended by addressing issues proactively. The results not only extend equipment’s service life, but also generate data that can provide manufacturers with valuable input for machine learning.

Machine Learning, Predictive Data, and Food Safety

According to joint academic research, innovative applications that use machine learning to analyze data are just on the horizon: “Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents.”

In an article in Food Safety Magazine, the authors also stated that machine learning and predictive resolution may help uncover hidden patterns from whole genome sequencing (WGH) data that are less identifiable when using traditional methods. Machine learning also holds promise to solve difficult problems in the biomedical sciences using genomic data. The authors referenced a recent prominent case as a “breakthrough in predicting protein folding through deep learning of amino acid sequences.”

In an article for the National Library of Medicine’s National Center for Biotechnology Information, authors E. Stavropoulou and E. Bezirtzoglou stated that recurring microorganism hazards stemming from lapses in the handling, processing, and distribution of foods can’t be solved by obsolete methods. “As the industrial domain evolves rapidly, and we are faced with pressures to continually improve both products and processes, a considerable competitive advantage can be gained by the introduction of predictive modeling in the food industry.”

The Future of Predictive Resolution

The potential promises held by the adoption of predictive resolution are substantial. Actionable insights can be drawn from IoT devices or cloud-connected software at the speed of a business day, enabling manufacturers to make meaningful, real-time operational adjustments. Such refinements, enabled by the latest technology, encourage continuous improvements in methodology and allow for a quicker implementation of production line efficiency and safety. This latest advancement is once again opening opportunities to push the industry forward through the application of technology.