Challenge #2: Gathering the Data Needed for Machine Learning in Manufacturing

One of the toughest challenges when it comes to machine learning in manufacturing is gathering the data that you need.

This blog post is part of our machine learning series, for manufacturers. Over the course of several posts, we will take a deeper dive into the various items initially addressed in our blog on The Challenges and Advantages of Machine Learning in the Manufacturing Industry.

In this installment, we tackle the complicated task of gathering data to implement machine learning and achieve success.

Where to Start?

Without data, machine learning is impossible. Machine learning is a type of artificial intelligence, where machines are able to learn and improve as they gather data. The concept is similar to human learning: the more information we have, and the more experience we have working on a task, the better we will be at completing it.

Identify the Problem

Break down the problem you are looking to solve with machine learning and develop a strategy for capturing data. Figuring this out will help you understand what kind of data is most important to this initiative.

Collect Data

If you’re a new company or have not previously had a reliable system in place, this is your chance to start collecting data that will improve your business, in addition to using machine learning techniques. You can also look to open source datasets (data that is freely available for use).

Implement an Enterprise Resource Planning (ERP) system to gather and make better use of data, moving forward. Find a partner that has expert ERP knowledge and experience working with manufacturing companies, and let them help you choose an ERP to integrate with your existing systems.

Prepare Data to Eliminate Flaws

If you already have data on paper or in outdated files, it will need to be prepared. Next, you’ll need to reduce the complexity of your data, and eliminate any flaws to ensure machine learning can happen smoothly. Make sure data inputs are formatted consistently. Seemingly minor inconsistencies can create issues for machines. For example whether a measurement is written 5 in., 5 inches or 5”, can cause confusion. Additionally, if you have missing data, approximate values will result in greater accuracy than leaving these spaces blank.

Manufacturing Automation Opportunities

Despite the challenges of machine learning in manufacturing, using this kind of artificial intelligence is an exceptional way to optimize your business processes. It is a great choice for the following:

  • Identifying products that are not driving a profit, and should be removed from production.
  • Determining which products to recommend to customers who have previously done business with you.
  • Categorizing items, based on answers to yes/no questions.
  • Ranking products based on features, product reviews, etc.
  • Numerical tasks, for example, creating prices for new products.
  • Segmenting customers according to various differentiators, for personalization or marketing purposes.
  • Machine learning in additive manufacturing can also vastly improve the accuracy of 3D printing.

Operational Efficiencies to Gain

Data is the key to endless opportunity when it comes to applications of machine learning in manufacturing. Once you’ve gathered your data, you can optimize decision making processes and production schedules. You’ll be able to make better pricing and production choices, improve your sales and marketing tactics, and more.

Learn More about the Applications of Machine Learning in Manufacturing

We are experts in Epicor ERP, working with aerospace and defense, engineer to order, furniture and woodworking and general manufacturing companies to implement machine learning, improve efficiencies and futureproof your business.

Learn more about the advantages and challenges of machine learning in manufacturing, by bookmarking our blog page to keep track of the series, or by contacting us for guidance tailored to your business needs.

Posted in ERP