Machine Learning: The challenges and opportunities for the industrial process industry

Machine learning (ML) is transforming the industrial process industry by enabling businesses to optimize production, improve efficiency, and reduce costs. By leveraging advanced algorithms and predictive analytics, machine learning can help organizations make better decisions, improve operations, and enhance the bottom line. We explore these benefits, and the challenges that organizations must overcome to succeed.

Advantages of Machine Learning in Industrial Process Industry

Automate Repetitive Tasks

One of the key advantages of machine learning in the industrial process industry is its ability to automate repetitive tasks and increase efficiency. On average data experts spend almost 40% of their time on mundane tasks, such as data wrangling. By automating routine tasks such as data collection, analysis, and reporting, with auto-ML tools, organizations reduce the time and resources required to complete these tasks manually, and get to the operational insights more rapidly. 

Identify Problems

Another advantage of machine learning is its ability to optimize production and improve quality. By analyzing large volumes of data from sensors, machines, and other sources, ML algorithms can identify patterns and anomalies that may indicate problems or opportunities for improvement. For example, ML can help organizations identify the root causes of production incidents, and schedule maintenance and repairs before breakdowns occur, reducing downtime and increasing efficiency. One oil and gas platform used machine learning insights to prolong their gas compressor reliability, to the value of $21.7m USD within four months.

Optimize Processes

Machine learning can also help organizations reduce costs by optimizing processes and reducing waste. By analysing data from various sources, algorithms can identify inefficiencies and bottlenecks in production processes and recommend ways to optimize these processes. Additionally, it can help organizations reduce waste by identifying ways to use resources more efficiently. In one example, machine learning was utilised by a Power Plant for the comparison of two identical air supply units. The models identified the root cause of energy production loss, as well as excessive energy consumption and reliability issues. Once resolved this translated to an annual saving of $280,000 in fuel costs.

Challenges of Machine Learning in Industrial Process Industry

Despite its many advantages, machine learning also presents several potential challenges for organizations in the industrial process industry.

Quality Data

One of the main challenges associated with ML is the need for high-quality time-series or process data. In order to train machine learning algorithms, organizations must have access to large volumes of high-quality data that accurately represent the production process. Additionally, the quality of the data used to train ML algorithms will directly impact the accuracy and effectiveness of these algorithms. It is not uncommon in industrial process models to use twelve or more months of historical data from over 30,000 tags, along with continuous real-time data feeds. Organizations that want to adopt ML first need to carefully consider how they will connect and store their data - see more here on data storage comparisons.

Specialized Data Skills

Another potential challenge of machine learning in the industrial process industry is the need for specialized skills and expertise. Machine learning is a complex and technical field that requires specialized skills and knowledge to implement effectively. Thankfully, AutoML is being recognised as an exciting innovation to automate many of the data science tasks, and most importantly enable non-data science experts to train machine learning models, which is a critical step in scaling machine learning.

Model Production

The last challenge for organisations to overcome are the roadblocks to productionizing machine learning models. In the 2022 State of Data Science Report, the top three roadblocks were reported as Meeting IT/InfoSec standards (34%), Securing data connectivity (28%) and Re-coding models from Python/R to another language (26%). These roadblocks may be one reason why Algorithmia reported that 55% of companies in their survey had not deployed a machine learning model. One way to overcome these obstacles is with Machine Learning Operations (MLOps) which helps organisations develop, deploy, monitor, manage ML models in a systematic, scalable and reliable way.