00

AI/ML Data Analytics can Transforming Manufacturing

Call it Industry 4.0 as the Germans, Smart Manufacturing as the Americans or Smart Factory as the Koreans, the manufacturing industry is witnessing a technological overhaul that is propelled by the power of data and analytics. Given that the manufacturing involves complex production activities, it is leveraging greater digitization, the adoption of connected systems, and implementation of sensors in various manufacturing processes to improve the production accuracy and quality of products. With the help of the huge volumes of data that these systems generate, the manufacturing sector is using machine learning techniques and big data analytics to develop predictive statistical models that can help them make smarter decisions, improve operational processes and increase profitability.

WHAT IS MACHINE LEARNING?

There’s a lot of buzz surrounding big data analytics and machine learning in general. But what essentially is Machine Learning and is it any different from big data analytics? By definition, Machine Learning is a method of data analysis that provides computers the ability to learn without explicit programming. By automating analytical model building, machine learning algorithms have the capabilities to learn iteratively from data and discover insights without needing explicit programming and human intervention. Machine Learning algorithms also have greater computational processing capabilities and can analyse larger and more complex data sets and deliver accurate results faster.

THE ROLE OF MACHINE LEARNING IN MANUFACTURING

Given the availability of a huge data pool, the manufacturing sector has taken a deep data dive to optimize areas that have the most impact on production activities. Today manufacturing companies have access to real-time shop floor data and along with that have the capability to conduct sophisticated statistical analysis using big data analytics and machine learning algorithms that consist of neural networks, decision trees, sequential covering rule building, etc., to uncover hidden patterns, reveal important insights and make smarter business decisions.
In manufacturing, Machine Learning techniques are applied to analyse large data sets for developing approximations regarding the future behaviour of the systems, detect irregularities and identify scenarios for all possible situations. In this blog, we take a look at how big data analytics and machine learning are transforming the manufacturing sector.

PREDICTIVE MAINTENANCE

One of the greatest and most visible impacts of Machine Learning in manufacturing has been that of predictive maintenance. The Industrial Internet of Things market is standing at an estimated $11 trillion and predictive maintenance can help companies save almost USD$ 630 billion over the course of the next 15 years. Machine Learning can provide deep insights into the health of the machines and indicate if a machine is going to experience a breakdown. This information can help companies take preventive measures instead of reactive measure and prevent downtime, excess costs and also long-term damage to the machine. Machine Learning algorithms can improve the Overall Equipment Effectiveness (OEE) by leveraging and analysing sensor data and improve equipment quality and the entire product line along with boosting shop floor and plant effectiveness.

CONDITION BASED MONITORING

Machine Learning and big data analytics are being effectively used in manufacturing to also bolster preventive maintenance. Given that manufacturing companies have a large install base of machines, they need to ensure that all their equipment are functioning to their optimal capacities. Detecting equipment failures before they happen and fixing them can be done using Condition Based Monitoring. With the help of condition-based monitoring, which is a process of continuous machine monitoring using pre-defined parameters, machine failure predictions can be made on time by tracking patterns that indicate equipment failure. Condition Monitoring ensures that an equipment is running or is maintained by iterating parameter variances that are constantly monitored.

ACHIEVE SUPERIOR QUALITY CONTROL

In today’s regulatory landscape product quality is of paramount importance. Remember how Volkswagen had to recall approximately 83,000 diesel vehicles over emission protocols? Machine learning and big data analytics are being used effectively in the manufacturing sector to achieve superior quality control. Leveraging principles of advanced big data analytics and machine learning concepts such as neural networks, manufacturing companies can identify defects, uncover the root cause of problems, predict quality- critical deviations with accuracy, reduce the risk of shipping non-conforming parts and also enable engineering improvements.

IMPROVE SUPPLY CHAIN MANAGEMENT

Big data analytics and machine learning algorithms have helped manufacturing companies improve supply chain management. Machine learning algorithms continuously assess the state of the supply chain and drive efficiencies with inventory optimization, demand planning, supply planning, operations planning, logistics etc. This allows manufacturers and suppliers to collaborate more effectively and thereby prevent keeping safety stocks levels high, adjust inventory positions to ensure that the right inventory is positioned at the right location to service customers better and prevent stock-outs, and also improve transportation logistics. Along with this, machine learning and advanced analytics can improve the quality of Sales and Operations Planning by supply chain network optimization and using descriptive models to assess demand forecasts and other parameters such as future raw material costs, the cost of manufacturing and distribution, working capital analysis, etc.

With the help of Machine Learning algorithms, manufacturing companies are creating greater value from the Big Data that lies at their disposal. This is helping with greater process optimization and increased predictive accuracy along with reduced time and resource wastage and this ultimately has a positive impact on the company bottom lines as it not only helps them reduce wastage but also helps in identifying new opportunities.