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Machine Learning is one of the powerful forces driving the fourth industrial revolution (4IR or industry 4.0). The transformative fusion of technologies, also known as the 4IR technologies, encompasses advances in Artificial Intelligence (AI), IoT, robotics, quantum computing, and several other technologies. Together, these technologies are changing our world and every aspect of business across industries.
According to McKinsey & Company, the 4IR technologies are expected to create up to $3.7 trillion in value by 2025. Artificial intelligence alone can generate $1.2-$2 trillion in value for manufacturing and supply chain management.
Manufacturing is one of the prominent industries where the winners are successfully using machine learning and other innovative technologies to gain a competitive advantage over their peers. As a result, they have significantly reduced machine downtime, improved labor productivity, and accelerated time-to-market by transforming their people and processes with next-generation digital technologies.
In this blog, we’ll understand the foundational blocks of machine learning, machine learning use cases in manufacturing, and how Kellton enables manufacturers to strategize and accelerate their journeys toward Digital Transformation.
What is Machine Learning?
Google defines machine learning as a “subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data.”
Although ML and AI are often used interchangeably, Machine learning (ML) is a subfield of artificial intelligence. AI is a broad science of mimicking or replicating human intelligence, whereas ML is a method of training models to act smarter and faster.
Machine Learning methods
Machine learning can be classified into supervised learning and unsupervised learning.
Supervised learning: Supervised learning is a subset of machine learning and artificial intelligence. In this machine learning method, labeled datasets are used to train algorithms to predict highly accurate outcomes. Businesses across industries leverage supervised machine learning to uncover critical insights to drive decision-making that ultimately impacts their crucial growth metrics. Some methods used in supervised learning include linear regression, random forest, neural networks, and more.
Unsupervised learning: It leverages unlabeled data to analyze and categorize information based on common characteristics or trends. With unsupervised learning, developers train algorithms to work independently without needing manual intervention to detect hidden patterns and identify new opportunities and risks. This machine learning method is so effective that many companies use it to analyze exploratory data, strengthen cross-selling strategies, and segment their customer base.
Top 7 applications of Machine Learning in Manufacturing
- Machine Learning for Predictive Maintenance: A McKinsey report notes that the most significant value from AI in manufacturing is because of predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. BCG calls predictive maintenance the first Industry 4.0 priority, especially for cement producers.
Machine learning technology empowers predictive maintenance. Factory officials can use ML solutions to assess the working of certain parts and equipment within their machines and schedule maintenance work accordingly to prevent system failures at scale. The technology can be applied to businesses across the manufacturing industry and help avoid aviation accidents, car breakdowns, and system failures that can put people and resources at risk.
- Digital Twin: A digital twin is a dynamic and visual representation of an asset, equipment, process, or system. It mimics the qualities of its physical counterpart and helps decision-makers gain deep insights that are applied back to the machine, equipment, or process for reducing downtime while upping production. Digital twins are increasingly transforming asset operations across manufacturing. Factory owners use the technology across all manufacturing phases, from product design to completion.
- Warehouse Management: One of the notable applications of machine learning and artificial learning is in the field of warehouse management. Modern AI/ML models can be trained to automate almost all aspects of warehouse management. For example, manufacturers can use machine learning models to predict customer demand and stock up their warehouses to keep up with that demand without exhausting their warehouse ecosystem.
Robots in the warehouse can perform regular, mundane tasks, such as tracking, picking, and sorting items, thus enabling the top management and officials to focus on more critical tasks to optimize their warehouse operations and improve their sales and profit margins. In addition, since machine learning solutions help manufacturers collect data in real time, they gain improved visibility into their supply chain management. They can use those critical insights to build a competitive and sustainable supply chain model.
- Self-driving vehicles: According to a Mckinsey report, “Highly autonomous vehicles are expected to make up 10 to 15% of global car sales in 2030 with expected two-digit annual growth rates by 2040. The efficient, reliable, and integrated data processing these cars require can only be realized with AI.” Manufacturers can use machine learning models to automate everything from assembly lines to conveyor belts, thus accelerating time-to-market. ML algorithms also enable self-driving cars to identify objects on the road, interpret road signs and conditions, and recognize crossroads, thus contributing to road safety.
- ML in design: AL/ML-powered technological solutions, such as Generative Design software, transform how auto manufacturers conceive and design their cars and parts. With the new-age software solutions, automakers can rapidly access thousands of designs for their future vehicles and auto partners and choose the best ones to streamline their operations and accelerate deliveries. Auto giants, such as Nissan and Volkswagen, are already using machine learning technology in their operations to create awe-inspiring designs in the blink of an eye.
- ML for connected factories: A connected factory or ecosystem is the ultimate realization of the fourth industrial revolution, where every aspect of the manufacturing facility is available for analysis and improvement. Modern-day shop floors are increasingly leveraging machine learning, artificial intelligence, IoT, big data analytics, and advanced robotics to unlock new levels of efficiency and transparency across processes to achieve higher profitability. Connected or intelligent factories use innovative technologies to allow a seamless transfer of data and insights between people, sensors, and machines to uncover patterns and insights that were not available before and that can be leveraged to inform business practices and innovation.
- ML for visual inspection and quality control: Whether a company is manufacturing electronic devices, automobiles, or smartphones, production quality and yield are critical to success. Poor production quality controls can lead to product recalls, revenue loss, and resource wastage. The American Society for Quality estimates that for many organizations, this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually, for larger manufacturers. Smart manufacturing facilities can employ the best machine learning techniques and computer vision technology to augment their capabilities effectively, monitor every aspect of their production process, and automatically initiate correction measures.
Harnessing the true potential of AI/ML with Kellton
Machine learning is increasingly introducing a multitude of capabilities to all businesses across the manufacturing industry. By harnessing the enormous potential of machine learning and other 4IR technologies, such as AI, big data analytics, and IoT, manufacturers can take their businesses to the next level. A McKinsey survey notes that by strategically implementing these 4R technologies across operations, manufacturers reap benefits like:
- 10-20% reduction in quality-related costs.
- 10-30% improvement in throughput.
- 15-30% increase in labor productivity.
- 30-50% decrease in machine downtime.
At Kellton, we enable our clients to rearchitect their digital landscapes and build highly connected ecosystems with our advisory and consulting practice focused on empowering them and positioning them to generate greater value from their technology investments. With a global team of 1800+ strategists and technologists that drive real business value for our customers, we can help you kickstart your journey toward unleashing the true potential of Machine Learning in a highly strategic, focused, and cost-effective way. Connect with us to explore how we can help your business grow with new-age digital technologies.