The entire manufacturing industry is driven by one buzzword “quality.”And ML analytics plays a crucial role in optimizing the assets for the unpredictable future and the ‘new normal.’
Efficiency, safety, and reliability are the dominant factors that ultimately affect product quality to influence customer satisfaction. Design, sourcing, testing, and inspection together play a critical role in assuring that the products meet their expected quality bar. Product inspections at the initial stages of the production cycle allow reducing risks and overall cost.
While inspections can be efficiently conducted at any particular point throughout the production process, the goal is ultimately to identify, contain, and resolve issues as promptly as possible.
Many manufacturers are progressively looking to their smart, connected machines to assist with anomaly detection. These assets can alert end users of any such anomalies to ensure accelerated interventions, allowing them to maintain quality and assure uptime.
Using advanced analytics, assets can simplify user feedback collection or alert accuracy and improve quality over time. This allows higher outputs and brings down the labor costs due to reduced time spent resolving such issues.
Factories, especially during the COVID-crisis, faced significant challenges for simple issues that went unnoticed. The best example of this is how giants like Rockwell Automation struggled due to mounting rework times due to defective circuit boards.
Rockwell Automation realized this as an opportunity to introduce a product inspection in one of their significant production processes. Lack of early inspection often results in a considerable amount of time spent fixing the errors that occurred in the initial phase of the process.
Enabling ML to test connectivity to save time and lower costs
Companies are implementing an advanced analytics solution to fix such connectivity issues to save time as well as bring down the costs. Using high-speed edge computing and machine learning, all such smaller details get tracked. It then helps predict conditions when the machines fail to meet the quality bar or alert existing defect probabilities.
In case any ML algorithm detects underlying anomaly in the process, operators can immediately stop production, fix the issue, take corrective actions, and restart the production line. This process takes less than a couple of minutes.
Errors that used to take 6 hours to resolve are determined and corrected within minutes using the power of ML. And, given that the value of real-time corrections, there has been a cost-saving benefit achieved by resolving issues before any of the parts fail, reducing scrap and other related waste. The financial benefit of such technology application goes way beyond than imagined.
Scaling similar ML solutions for creating massive future impact
In this newly evolving market, revolving around the pre-pandemic world pushes the need to scale such ML solutions adoption across various manufacturing sectors. New capabilities have yielded nearly immediate results in terms of time as well as cost savings.
Factories with a futuristic vision see the massive potential for real-time analytics to multiply their daily production activities across their facilities. In turn, this will allow firms who are looking towards additional use cases of advanced analytics and machine learning to bring even more intelligence into their existing operations.