Het onmogelijke mogelijk maken
Machine learning and artificial intelligence has revolutionized the way industrial companies operate. With machine learning we can extract insights and provide predictions from massive amounts of data. This data would be hard to analyze using traditional methods. In this blog, I will explore the practical applications of machine learning and the value it brings to industrial companies.
First, let us understand what machine learning is. Machine learning is a subfield of artificial intelligence that enables us to learn from the available machine data without being explicitly programmed. It is a data-driven approach that uses statistical techniques to learn patterns and relationships from large datasets. The more data the machine learning algorithm is exposed to, the better it becomes at making predictions and detecting deviations.
Now, let us look at the practical applications of machine learning for industrial companies. One of the primary applications of machine learning is predictive maintenance. Industrial companies can use machine learning to analyze data from process data to predict when equipment is likely to fail. This approach can help companies to reduce downtime, minimize maintenance costs, and improve asset reliability. By predicting that a machine will fail on short notice, machine learning enables companies to prevent downtime and optimize their maintenance schedules.
Another practical application of machine learning is quality control. Industrial companies can use machine learning to analyze sensor data from their quality control stations to identify defects in products. With the help of machine learning we can learn to recognize patterns and anomalies in data and flag any issues in real-time. Or even better set the control limits in such a way that drifts in quality are flagged before the quality limits are exceeded. This approach can help companies to reduce waste and improve product quality.
Machine learning can also be used for supply chain optimization. Industrial companies can use machine learning to analyze data from suppliers, transportation providers etc. to optimize their supply chain and production planning. For one of my previous Nyenrode modules `Business processes and technology` I used the data of transportation companies to optimize the supply chain and prevent the `Bullwhip effect`. With Machine learning this optimization can be further improved by learning to identify patterns in the data and make predictions about demand, lead times, and inventory levels. This approach can help to reduce costs, improve efficiency, and enhance customer service or OTIF levels.
So, what value does machine learning bring to those of us who are interested in boosting the performance of industrial companies?
It is essential to note that machine learning is not a magic solution that can solve all problems. To apply machine learning it is important to start “learning from machines”. It requires careful planning, implementation, and management to realize its full potential.
To implement machine learning successfully, industrial companies need to have the right talent, infrastructure, and data governance in place. Companies need to have data scientists and engineers who can develop and deploy machine learning models. Companies also need to have the right infrastructure to store, process, and analyze data. Finally, companies need to have robust data governance policies in place to ensure data quality and data-security.
Machine learning has practical applications in many areas of industrial companies, including predictive maintenance, quality control, supply chain optimization, and line balancing. Machine learning can help us to extract insights from vast amounts of data, make better predictions, and identify anomalies in real-time. With this approach we can help your company to improve efficiency, reduce costs, and improve reliability. Therefore, I would like to recommend industrial companies to explore the potential of machine learning and implement it in their operations.
Feel free to contact me to align on the machine learning possibilities to improve your factory performance.
Publicatie: 8 mei 2023
Auteur: Jan Visser
