Machine Learning for the Manufacturing Sector
Machine learning is an artificial intelligence area that enables computers to learn without having to be explicitly taught. It may generate predictions and judgments based on previous data, making it applicable to many businesses, including manufacturing. Machine learning’s major objective is to create systems that can do tasks automatically or semi-automatically based on prior experience and patterns observed in a dataset.
In this post, we’ll look at how machine learning may help your company improve quality control, increase productivity, save expenses, and more. We’ll also provide you with a short review of what you need to know about machine learning before you start adopting it in your business.
What is Machine Learning, and how does it work?
Before we get into why machine learning is so vital for manufacturers, let’s look at why it’s so important. This term encompasses a wide range of machines, including robots and self-driving automobiles. Humans, on the other hand, can reason and digest data fast. Machines don’t always have these talents; instead, they depend on algorithms to carry out their tasks. These algorithms may be developed by hand or generated via machine learning.
What kind of information do manufacturers gather?
In addition to the obvious ones like sensors and PLCs, manufacturing data sources such as business transactions, maintenance records, geographic data, and RFID scans may give insights into industrial processes. Of course, to extract such insights, you’ll need access to such data.
Industrial data is commonly siloed, meaning it is only accessible by one department or division and limits its value unless separated from the rest of the company. More than two-thirds of all industrial data is wasted, according to some estimates. Manufacturers suffer storage costs while receiving no value from this so-called “black data,” which is usually retained exclusively for compliance reasons.
Breaking down data silos to recover costs and increase return on investment is a well-known problem that has spawned a host of industrial data visibility and data governance solutions.
Given the complexity of managing factory data, one would wonder whether the expenses of equipment, data collection, and industrial data storage are genuinely justified.
Getting the most out of industrial data
The route to factory digitalisation may look straightforward: collect more production data using a mix of legacy equipment instrumentation and industrial connectivity, then analyse the data using advanced manufacturing analytics.
Add in the massive number of manufacturing AI applications in Industry 4.0, and it’s simple to see why selecting where to start is the most challenging difficulty in producing value from manufacturing data. Unfortunately, most digital transformations fail at the proof-of-concept stage, with fewer than one-third of them succeeding.
Many issues, including bad communication strategies, may be blamed for the lack of employee participation. Finally, understanding the issue you’re trying to solve and how approaches like machine learning may help you solve it is crucial to extracting value from manufacturing data.
When implemented effectively, machine learning may assist manufacturers in achieving new levels of product quality; however, they must first understand machine learning.
Types of machine learning for manufacturing
Machine learning uses a variety of algorithms to solve problems. The way these algorithms learn is typically how they are categorised. The way an algorithm consumes data and whether that data is labelled or unlabeled determines its learning style. Unlabeled data lacks a tag or classification, while labelled data does.
Consider units that succeeded or failed an end-of-line test: the data set is labelled if such units are recognised as succeeding or failing in the data. The data set is unlabeled if there is no indication of whether portions of the units succeeded or failed.
- Supervised learning algorithms are those that make use of labelled data.
- Unsupervised learning algorithms, on the other hand, are algorithms that employ unlabeled data.
- Semi-supervised learning algorithms are those that employ data that has been partly labelled.
The available production data and the circumstances at hand will decide which machine learning approach works best. Consequently, manufacturers may determine that a mix of supervised, unsupervised, and semi-supervised learning is desirable across facilities or even within lines within a single facility.
Machine learning’s advantages in manufacturing
Machine learning is revolutionising the industrial industry by increasing efficiency. The following are the main advantages of machine learning for manufacturing efficiency:
1. Increasing production capacity by up to 20% while lowering material consumption by 4%.
2. More relevant data is being delivered to finance, operations, and supply chain teams to effectively manage factory and demand-side constraints.
3. Improving preventive maintenance and Maintenance, Repair, and Overhaul (MRO) performance by increasing component and part forecast accuracy.
4. Giving manufacturers the scale they need to regulate Overall Equipment Effectiveness (OEE) at the plant level, boosting OEE performance from 65% to 85%.
5. Machine learning transforms relationship intelligence, and Salesforce is quickly establishing itself as the industry leader.
6. Machine learning algorithms revolutionise product and service quality by determining which factors have the greatest and least influence on company-wide quality.
7. Machine learning is already enhancing production yields by optimising team, machine, supplier, and customer demands.
8. Machine learning will allow subscription models for production services, bringing Manufacturing-as-a-Service closer to reality.
9. Machine learning is useful for streamlining supply chains and achieving scale efficiencies.
10. Machine learning will make it commonplace to know the exact amount to charge a specific customer at the precise moment to maximise margins and complete sales.
Any manufacturing business may use machine learning to improve its operations and get a competitive edge by acquiring predictive insights into production.
Machine learning’s core technologies are well-suited to the complicated problems that manufacturers encounter daily. Machine learning algorithms can increase forecast accuracy at every level of the manufacturing process, from keeping supply chains moving smoothly to delivering customised, made-to-order goods on time.
Several of the algo being developed are iterative to learn and improve results over time. Because these algorithms repeat in milliseconds, manufacturers may find the best outcomes in minutes rather than months.