Machine learning (ML) is having a significant impact on process manufacturing, leading to improved efficiency, cost savings, better decision making, collaborative change management, and more. Take a look at use cases for ML in manufacturing and some initial steps required to begin integrating and reaping the benefits of these technologies.

Key Use Cases

Machine learning use cases in manufacturing include predictive maintenance, process health, quality control, and supply chain optimization.

  • Predictive maintenance involves using machine learning algorithms to understand the most likely time for equipment failure, allowing proactive maintenance or updates rather than reacting to broken or failed components and facing increased downtime.
  • Process health leverages machine learning to monitor multivariate steps and controls in a manufacturing process to understand optimal productivity and operational stability.
  • Quality control involves using AI-powered computer vision to inspect products for defects, reducing waste and improving production quality.
  • Supply chain optimization uses predictive analytics to optimize inventory levels and delivery schedules, reducing costs, and improving customer satisfaction.

Getting Started

To begin integrating machine learning into a manufacturing environment, here are recommended steps:

  1. Identify the use case: Determine the specific area of the manufacturing process where machine learning and AI can provide the most significant benefits. While this may seem simplistic, it is critical to ensure success with implementing ML. You can talk with a data science partner to ensure the questions you’re trying to answer are properly framed.
  2. Collect and prepare data: Machine learning algorithms require large amounts of data to train accurately – often 18-months to two years’ worth. You may have AVEVA or AspenTech software capturing key process data. This or data from other sources must be readily accessible and properly cleaned and labeled.
  3. Choose the right ML platform: Based on the level of data science expertise within your organization, there are several approaches, including taking a path that requires full coding of models, using software such as R or Python, and requiring the data scientist to fully understand algorithms and best use cases. An alternative that can democratize analytics within an organization involves Automated Machine Learning (or AutoML), which applies automation to algorithm selection and can provide low-code or no-code options that use business language to create and implement models into production.
  4. Develop and train the model: Once the data is prepared, the machine learning model must be developed and trained.
  5. Test and deploy the model: Test the model using a small dataset to ensure accuracy, then deploy it in a live manufacturing environment.

Machine learning is accelerating its positive impact on process manufacturing, delivering efficiency, cost savings, innovation, and more. The team at Lityx are ready to help you with your machine learning integration. Our proven Proof of Concept process is the fastest way to begin capturing the value of machine learning for your organization. Learn more here.