Machine learning is largely dependent on the ingestion of data – and not just any data. We need to look at the complexity that surrounds large industrial operations and the type of data manufacturing plants are probably already collecting.
It starts with quality
In modern production environments, there are many places to measure the pulse of your operation: from takt time on your production line to the size of your goods-receiving warehouse, and then to scrap and rework rates, and even the cost of non-quality. All of these have a direct impact on your business’s efficiency, and it is important to understand the dynamic interplay between these variables.
Following the process: identification and traceability
To gain useful insights into your entire process, the quality result needs to be connected to the conditions that either enabled a good quality result or led to a poor quality result.
Process control: set-points and targets
It’s important to determine the complex interplay between parameters and the extent to which a small change in one impacts adjacent parameters and how the change cascades down through subsequent processes.
A holistic representation: combining quality, traceability, and control
This is the hardest part of realizing value from Industry 4.0 installations. Combining these three different data sources is crucial to creating a representation upon which an AI system can learn how process parameters influence quality and process yield.
DataProphet uses machine learning to understand how a plants process variables interact and combine. Machine learning is amazingly well suited to this problem and the output is a control plan that can enable production teams to fine-tune processes to reduce defects and eliminate scrap.
Read the full paper on maturity self-assessment for industry 4.0 here.