Data plays a critical role in ensuring smooth and optimal operations for a company. By having quality manufacturing, operations, supply chain and products data, companies can build good AI/ML models to streamline manufacturing operations and make the best decisions possible. Unfortunately, many companies just don’t have good data to feed the models. Hence, they suffer from “garbage in, garbage out” syndrome. The key to success is to first have reliable data and then build the best models to ensure good decisions based on sound data.
Data quality is a problem across industries. We at Cognistx see the same at many manufacturing companies. In some cases, they do not capture information correctly, the units are different or there are inconsistent practices across plants. All these lead to poor data quality, which prevents companies from implementing the best AI/ML solutions for their needs.
Once you have clean, actionable data, you can utilize it to reduce manufacturing defects, reduce the number of runs needed to meet specifications, ignore erroneous alerts from sensors and improve staff/labor utilization.
One of the main products we’ve been focusing on during the last three years is our Data Quality Engine (DQE). DQE is based on several years of research and operational experience. DQE helps companies clean their data with a combination of business rules and AI/ML models. DQE systematically helps companies climb the data-quality steps to achieve AI-enabled data, as outlined in the diagram below.
DQE and similar tools help companies identify millions of data issues and allow them to automatically fix them with little human intervention.
By fixing the root cause of the data issues, the company ensures the data remains clean and identifies operational issues before they become big problems. DQE with clean data and AI/ML models puts companies on a prescriptive path, allowing them to stay ahead of constantly changing business conditions and environments to make the best decisions possible.
The key to success is to demonstrate manufacturing excellence and ROI. Improved data quality is a great starting point that allows the following five (5) optimizations.
It is important to build such ROI measures into your data-quality and manufacturing processes from the beginning. It is equally critical to be able to report on them and learn from them for ongoing improvement and continued executive sponsorship.
If your organization has grown over time or you have inherited these issues, our Data Quality Engine can help clean your data and then generate the best predictions.
Our AI/ML models can build upon the clean data to deliver significant manufacturing and operational efficiencies. If you’re interested, we can review your data and processes and deliver significant shareholder value.