Machine Learning in Manufacturing – Advantages, Challenges & Applications

Machine Learning in Manufacturing

Manufacturing is one of the most complex yet emerging and essential industries. Products manufacturing is costly and a complicated process for companies that do not have the right resources and tools to develop production quality and enriched goods. In order to operate the machines and deliver the resulting quality products, the manufacturing industry must depend on technology and automation.

Nowadays, advanced technologies like artificial intelligence and machine learning are more prevalent in producing and assembling goods, which helps in reducing labor cost, production cost and time. That’s the reason most of the large manufacturing companies like Intel, Siemens, GE, Microsoft, etc., are making investment in machine learning technology.

Surprisingly, 40% of the potential value that can be generated by analytics today comes from AI and Machine Learning methods.  As per the research report of Global Market Insights, the global machine learning in the manufacturing market is projected to reach $ 16 billion by 2025, up from $ 1 billion in 2018. In addition, there is a constant need to cut the costs and adopt Industry 4.0 technology.

Here, in this blog post, we would like to give an overview about the applications, advantages and challenges of machine learning in manufacturing industry.

Advantages of machine learning application in manufacturing

Machine Learning improves resource utilization in some NP-hard manufacturing issues like equipment failures and reduces scrap and cycle time. In addition, ML offers powerful tools for frequent quality improvement in a complex and large process like semiconductor manufacturing. ML technology also helps simplify the supply chain management process.

Ability to handle high dimensional data and problems is the primary advantage of ML algorithms. This will be especially important in the future, especially concerning the availability of complex data.

A few algorithms like Distributed Hierarchical Decision Tree and SVM can handle larger sizes than others Wolf, Bar-Or, Lallich, and Schuster. As mentioned earlier, most of those ML algorithms in manufacturing can handle high-dimensional data. Hence, the ability to withstand high volume can be considered as a benefit of Machine Learning application in smart manufacturing.

Another plus point of machine learning technique is the increased use of the algorithms due to programs such as Rapidminer. This enables easy application in most cases and allows flexible adjustment of parameters to maximize performance classification.

As mentioned earlier, the main purpose of ML algorithms is to detect previously unknown knowledge (implicit) and to detect indirect relationships in data-sets. Based on the ML algorithm nature, requirements may vary towards available data.

However, the overall efficiency of the ML algorithm in achieving results in a productive environment has been successfully demonstrated (For ex: Philipp & Junker, 2000; Nilsson, 2005)

The specific nature of production systems is complex, dynamic, and uncertain. Here, Machine Learning algorithms offer the opportunity to learn from the dynamic system and to adapt somewhat automatically to the changing environment. Adaptation is based on the ML algorithm, is reasonably faster than traditional methods in almost all cases.

ML application in manufacturing sector can provide a model from real time data-sets that give a basis for the growth of approximations about the near future behavior of the system. This may assist process owners in making their decision or be used to improve the system automatically. Finally, some ML techniques aim to identify certain regularities or methods that describe relationships (Ex: Alpaydin).

 Challenges of machine learning application in manufacturing

The most common challenge of an Machine learning application in manufacturing filed is to gain relevant data. This is also a limitation to the quality, availability, and composition of data at hand has a strong impact on the performance of ML algorithms. A few challenges involve a data-set, e.g. high-dimensional data represent some of the ML algorithms, i.e. they contain high levels of repetitive and inconsistent information that affect the performance of learning algorithms (Liu & Yu, 2003).

Nowadays, most of the ML techniques handle can only data with nominal and continuous values ​​(Pham & Afify, 2005). How important the effect is based on several factors, including the parameter and parameter settings.

While in many cases ML allows knowledge to be extracted and yields better results than most traditional machine learning models with fewer requirements for available data, some aspects of available data that could prevent a successful application still need to be considered. Together with the next point, it highlights the need to understand the data in order to apply ML.

Once the available data is secure, the data must often be pre-processed based on the requirements of the selection algorithm. Data pre-processing can have a significant impact on results.

However, there are many standard ML (Machine learning) tools available that support the common pre-processing processes, such as filtering and normalizing data. Training data should also be checked for imbalances. This can be very challenging to train some algorithms.

In manufacturing practice, a common problem is whether certain attribute values ​​are available in the data-set (Form & Affi, 2005). These missing values ​​challenge the application of ML algorithms. There are some practical motivation systems available that can fill in the blanks (Form & Affi, 2005). However, each problem and subsequently applied ML algorithm has specific requirements when replacing missing values.

One of the major challenges of increasing importance is to choose the ML algorithm and technique. However, attempts have been made to follow the definition of ‘General ML Techniques’, with different problems and their requirements highlighting the requirement for specific algorithms with certain weaknesses and strengths (Hoffman, 1990).

As the focus of researchers and practitioners and on the area of ML in manufacturing has increased, a large number of diverse ML algorithms are available. In addition to complexity, combinations of different algorithms called ‘hybrid approaches’ give better results than the ‘individual’ single algorithm application (For ex: Lee & Ha).

So far, the generally approved procedure for selecting the appropriate ML algorithm for a particular problem is as follows:

It looks at the data available to choose between the supervised, overseen, or RL approach and how to describe it (unlabeled, labeled, available expert knowledge, etc.).

Research should analyze the general applicability of available algorithms relative to problem requirements (e.g. the ability to handle large volumes). You should be paid attention to the amount of data available, structure, and data types that can be used for evaluation and examination.

Previous applications of algorithms will be researched on similar issues to identify the appropriate algorithm. ‘Similarity’ in this context means that research problems with comparable requirements, e.g. in other domains or sections.

Also, click in the link to learn the Applications & Examples of Machine Learning in the Manufacturing 


Machine learning is reviving in many industries, including manufacturing. It is set up to improve the processes involved and to help implement better strategies for better results and optimized solutions. Undoubtedly, you can reduce downtime, improve the production process, and get better resources with machine learning capabilities in your business.

Looking to adopt machine learning technology in your business? Please contact us.

Our ML-driven solutions empower manufacturers to bring automation in manufacturing tools, manufacturing processes, analytics and predictive maintenance.




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