Challenge #1: Identifying Where to Use Machine Learning in the Manufacturing Industry

In our recent blog post on “Challenges and Advantages of Machine Learning in the Manufacturing Industry” we identified key issues associated with machine learning. Today, we take a deeper dive into the first “challenge” noted: identifying where to use machine learning.

Of the various perceived challenges of machine learning in manufacturing, we consider this to be one of those most commonly recognized hurdles to machine learning success among manufacturers. By investigating and tackling this issue, we aim to empower manufacturers to embrace machine learning concepts and technology that can future-proof their businesses.

Where to Start?

There are countless applications of machine learning in manufacturing, all with valuable, beneficial purposes and the potential to increase efficiencies.

For many manufacturers, this leads to a halt in progress, as they remain uncertain which avenue to explore or where to begin.

Research Applications of Machine Learning in Manufacturing

If you’re new to working with machine learning and artificial intelligence, take a little time to do some research into the options that may be available to you. Read up on where other manufacturing companies are using machine learning to succeed, and make note of ideas that could also benefit your business. With a deeper understanding of the opportunities available to you, you’re more likely to be in a better place to make decisions surrounding machine learning for your own business.

You’re likely to find examples of machine learning solutions that impact every part of your business. Forbes highlights the following opportunities for improvement and more:

  • Automated processes
  • Product inspection and quality
  • Bottlenecks
  • Operations
  • Supply chain management
  • Worker productivity
  • Energy consumption
  • Employee retention
  • Custom product workflows
  • Security
  • Sustainability
  • Product lines to discontinue

Reach for Low Hanging Fruit

Now you have a better understanding of the options available, you can start to decide where to begin. Take a look at your list of ideas. Which items:

  • Have the greatest potential to improve your current operations?
  • Can be completed with relative simplicity?

You may have a whole scope of ideas to start getting estimates for, or you may have a strict budget that empowers you to dip your toe in the water, to prove to yourself or your management team that machine learning is the way of the future.

Manufacturing Automations to Improve

A common starting point is with a goal to automate manual tasks currently performed by workers, day to day.

The automation of simple, manual tasks can improve safety and efficiency. However, not every automated task will require machine learning. Even those that require artificial intelligence may not require machine learning specifically.

When a piece of equipment uses machine learning, it is able to improve its abilities, for example the efficiency or accuracy with which it completes a task, by mimicking the human ability to learn through trial and error, improving as it gathers more data.

Automation is a great way to improve the efficiency of tasks that involve repetition. Machine learning, on the other hand, is best for tasks where learning is required, for example to improve processes or schedules, or identify inaccuracies, patterns or anomalies.

Operational Efficiencies to Gain

There are many benefits to gain by embracing various applications of machine learning in manufacturing. With machine learning supporting your manufacturing plant, you may expect to see:

  • Improved and faster data-based decision making.
  • Optimized production schedules.
  • New products designed with greater likelihood of success.
  • Improved 3D printing with machine learning in additive manufacturing.
  • Reduced costs, with machine learning managing energy use and HVAC systems.
  • Optimized maintenance schedules, saving costs by maintaining machinery based on actual use.
  • Improved accuracy and speed of product inspection.
  • Identification of production bottlenecks.
  • Improved processes to develop custom products.
  • Improved security and worker safety.
  • Reduced stock levels and waste.

Overcome the Challenges of Machine Learning in Manufacturing

Ready to overcome the challenges of machine learning in manufacturing and improve operational efficiencies? Contact CTND today to identify where to use machine learning to improve your processes.

Posted in ERP