Machine Learning Projects in Manufacturing: Expectations vs. Reality
By: Hedi Hunt
Tags: Manufacturing, Data, Project Management, Machine Learning, Efficiency

Machine learning is becoming more common in the manufacturing world. However, many businesses have unrealistic expectations about what this technology can do for their businesses.

Machine learning (ML) relies on complex computer algorithms to analyse data. The software learns from the data obtained to make accurate predictions that help to streamline or improve almost any process, including manufacturing processes. This technology offers many advantages, but it has not upended the manufacturing industry yet.

Machine learning is not a magic bullet

Some executives imagine that machine learning naturally leads to positive results. However, implementing this technology does not instantly solve your problems or automatically increase efficiency. For machine learning to be effective, you need to have an effective plan for implementation. This typically involves defining the specific problems that you want to solve.

Businesses that want to increase productivity may use ML software to achieve their goals. However, they first need to identify which processes tend to drag down efficiency. The software can then analyse those processes to provide insight.

Technicians and engineers may then use the insight obtained to make improvements. But if you expect the software to magically fix your problems, you will quickly become disappointed.

Machine Learning Requires Access to Accurate Data

As mentioned, machine learning is only effective when you have a specific plan for implementing the technology. Part of the implementation process requires access to data.

With machine learning, complex software analyses data and provides predictions or estimates. The predictions and estimates are only beneficial when the software receives the right data.

Data quality is one of the biggest obstacles to machine learning. Data is often provided from a variety of sources including sensors, instrumentation, and manual input. In some cases, the software receives contradictory data.

When implementing machine learning software, you need to optimise each data source. Validating the authenticity of data is a time-consuming process but allows you to detect anomalies and errors that may limit the accuracy of your data collection methods.

Machine Learning Software Needs Time to Learn

Another common misconception of ML technology is that it can quickly lead to improvements in your daily operations. Unfortunately, every aspect of implementing ML systems takes time.

After validating the accuracy of your data, you need to continue feeding your ML systems with information. It may take months for the software to receive enough data to accurately analyse your processes.

The average deployment process for AI and machine learning technologies takes over one month. You then need to wait several months for the software to accumulate and process data.

Machine Learning Will Not Replace the Need for Human Labour

Manufacturing engineers are often afraid that machine learning will replace human labour. Instead of reducing labour, deploying machine learning may make changes in the talent pool required.

Automation and machine learning will require employees with technical skills to develop, maintain and monitor it. Therefore, although the use of machine learning can reduce the amount of work done by the engineers, it also requires people to take care of the system itself. Over the time the savings from labour costs will be visible but the effects are rarely instantaneous.

The Future of Machine Learning in the Manufacturing Industry

Before implementing machine learning, you need to ensure that you are prepared. Some of the topics that need to be addressed include:

Defining goals for the technology

Providing software with accurate data

Making sure you have the right technical knowledge

Optimising your manufacturing processes

Defining your goals and objectives as part of the first step is integral. As with any business plan, you need to have a clear idea of what you hope to achieve and how you measure the achieved result.

There are already several areas where ML can improve factory automation:

Quality control

Predictive maintenance

Increased efficiency

Quality control is a common use for machine learning technologies in manufacturing. With a consistent supply of data, ML software can detect defects or errors with greater accuracy. One study found that ML can improve defect detection rates by up to 90%.

ML software may also improve predictive maintenance. The software can detect the smallest changes in the operational efficiency of various equipment and machinery. These changes can signal the need for upcoming maintenance. After detecting that a machine may require maintenance, the software can help determine the right timeframe for taking it offline to minimise any disruption to your manufacturing processes.

The potential benefits of machine learning are far-reaching, especially in manufacturing. However, you also need to have realistic expectations. Remember that ML will need time to start showing consistent results. You need to define the problems that you want to solve and determine how best to use the software to aid your decision-making processes.