Manufacturing remains an important part of any national economy — even as the service sector plays an increasingly important role in advanced economies. The loss of manufacturing jobs is part of the phenomenon that spurred the rise of political populism; however, there is near unanimity among analysts that manufacturing has changed forever. People longing for the return of blue collar, factory floor manufacturing jobs will ultimately be disappointed. The digital age has spawned a new industrial revolution — often referred to as Industry 4.0 — characterized by automation, artificial intelligence (AI), the Internet of Things (IoT). John D. Lanza, a partner and intellectual property lawyer with Foley & Lardner LLP, asserts, “The rapid adoption of Industry 4.0 technologies leaves manufacturers with a choice: accelerate with the market or be left behind.”[1] Being left behind doesn’t feel like a very good option. Judy Cubiss (@jucubiss), Content Marketing Manager for Industries at SAP, reports, “According to MIT Sloan Management Review, companies with 50 percent of revenues from digital ecosystems achieve 32 percent higher revenue growth and 27 percent higher profit margin. It’s clear that digital transformation is achieving real business value for manufacturing companies.”[2] Manufacturers failing to transform are not only left behind they miss out on the benefits transformation brings. It’s a double whammy.
Manufacturing and machine learning
Like other aspects of the digital age, Industry 4.0 relies heavily on data and its analysis. Advanced data analytics used by manufacturers are generally embedded in artificial intelligence (or cognitive computing) platforms. Lanza reports, “According to a 2019 Global Market Insights, Inc. report, the market for artificial intelligence in manufacturing will grow to $16 billion by 2025.” He goes on to list some of the reasons manufacturers are implementing AI platforms. They include:
- Reducing the cost of operations
- Enhancing operational efficiency
- Aligning operations with customer requirements
- Analyzing processes in real-time
- Scaling operations without intensive capital cost
Lanza adds, “Achieving these goals is supported by two main principles: interconnection and information transparency. Interconnection refers to the ability of machines, devices, sensors and people to connect and communicate with each other. Information transparency provides operators with large amounts of useful information needed to make appropriate decisions. The interconnected nature of machines and systems in an Industry 4.0 environment combined with information transparency allows manufacturers greater insight into the current operating conditions and operational efficiency of the factory.” The primary AI technique being used is machine learning. Joe Zulick (@__joeZ), a manager at MRO Electric and Supply, notes, “Machine learning is not a device you can plug into a production line and make the production line operate better than it did before. Machine learning is a process that needs inputs from many devices to feed data to it so that data can be collected, evaluated, and used to develop knowledge about how a production line produces the products and parts it does. That knowledge can then be used to determine how production line can have a higher throughput of parts, operate at a lower cost, and run more reliably.”[3] Zulick lists three ways manufacturers are currently using machine learning to improve their processes. They are:
Predictive Maintenance: “Being able to predict disruptions to the production line in advance of that disruption taking place is invaluable to the manufacturer. It allows the manager to schedule the downtime at the most advantageous time and eliminate unscheduled downtime. … A PwC study, Digital Factories 2020: Shaping the Future of Manufacturing, predicts that the adoption of machine learning to enable predictive maintenance is expected to increase among manufacturers by 38% because of the ability to increase profit margin by eliminating unscheduled work stoppages.”
IT/OT Convergence/Network Security: “Machine learning will also drive many business model modifications in the manufacturer standard operating procedures. That’s especially true in the organizational makeup of the company. … The floor operators and technicians will be significantly impacted if the network is not reliable or for some reason gets hacked via a denial of service attack, which will bring production to a stop.”
Smart Manufacturing Digital Design and Innovation/Digital Twin Development: “The ultimate objective of artificial intelligence and machine learning is to enable the development of a digital twin of the production floor. … The digital twin would serve as a platform for running what-if scenarios to learn what we don’t know today. The digital twin can also serve as an end-to-end model to be used in designing higher reliability parts and adjusting the interactions between production line machines to improve performance.”
Benefits of machine learning in manufacturing
Louis Columbus (@LouisColumbus), a Principal at IQMS/Dassault Systèmes, asserts machine learning is revolutionizing manufacturing.[4] He backs his claim by discussing ten ways machine learning is being used by manufacturers. They are:
1. Improved maintenance. “McKinsey predicts AI-based predictive maintenance has the potential to deliver between $.5T to $.7T value to manufacturers.”
2. Better sustainability. “Manufacturers are gaining new insights into how they can become more sustainable using machine learning and predictive analytics that scale on cloud platforms.”
3. Increased cost savings. “Boston Consulting Group (BCG) found that manufacturers’ use of AI can reduce producer’s conversion costs by up to 20% with up to 70% of the cost reduction resulting from higher workforce productivity.”
4. Improved processes. “Discrete and process manufacturers who rely on heavy assets are using AI and machine learning to improve throughput, energy consumption, and profit per hour.”
5. Improved quality. “AI- and machine learning-based product defect detection and quality assurance show the potential to increase manufacturing productivity by 50% or more.”
6. Smaller skills gap. “Machine learning has the potential to reduce manufacturing’s chronic labor shortage while finding new ways to retain employees at the same time.”
7. Actionable insights. “Machine learning is helping manufacturers solve previously impenetrable problems and reveal those that they never knew existed, including hidden bottlenecks or unprofitable production lines.”
8. Enhanced personalization. “Machine learning can significantly improve product configuration, and Configure-Price-Quote (CPQ) workflows manufacturers rely on to build-to-order products.”
9. Smarter factory operations. “AI and machine learning adoption in manufacturing are predicted to eclipse robotics in the next five years, becoming the leading use case in manufacturing.”
10. Enhanced security. “Machine learning is revolutionizing how manufacturers secure every threat surface, relying on the Zero Trust Security (ZTS) framework to secure and scale their operations.”
Zulick writes, “If we dig deeper underneath the big-ticket items, there are thousands of other impacts that machine learning will have on smart manufacture and the industrial processes that make it all work.”
Concluding thoughts
Columbus concludes, “Using machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing.” Even though machine learning can greatly benefit manufacturers, it takes hard work to put the right system in place. Cubiss cautions, “Digital transformation can be a daunting task for manufacturers, but it’s important they view digitization as an ongoing journey that evolves over time. Companies must also understand the long-term benefits of operating intelligently, like improved efficiency and enhanced products.”
Footnotes
[1] John D. Lanza, “Adoption of Artificial Intelligence in Manufacturing Accelerating,” The National Law Review, 13 March 2019.
[2] Judy Cubiss, “Intelligent Machines for an Intelligent Enterprise,” Manufacturing.net, 6 February 2019.
[3] Joe Zulick, “How Machine Learning is Transforming Industrial Production,” The DELMIA Blog, 10 September 2019.
[4] Louis Columbus, “10 Ways Machine Learning Is Revolutionizing Manufacturing In 2019,” Forbes, 11 August 2019.
