Industry 4.0 and the Impacts of Machine Learning on the Manufacturing Industry
The manufacturing industry is deeply impacted by the rise of machine learning projects. Originally coined by the German government in 2011, Industry 4.0 refers to the idea that the world has undergone the process of moving into a Fourth Industrial Revolution. Industry 4.0 is now widely accepted as the next paradigm for production, and if you’re not already embracing the new revolution, then you are behind.
What, exactly, is the Fourth Industrial Revolution?
Many outside of the tech industry are familiar with the First and Second from history textbooks but may not know where modern society now stands. The First revolution was a move from hand-made products to machine-made, the Second was marked by continuous processes and assembly lines, and the Third represented the widespread use of computers and robotics for industrial automation. Now, the Fourth – Industry 4.0 – is where automation, data connectivity, and machine learning (ML) completely change the way we look at and operate within an industrial world.
In the manufacturing industry, where humans and machines have had a hands-on relationship since the First Industrial Revolution, a move to a machine-dominant environment can seem like a loss of control. To some, it may look like a threat to job security, but this hasn’t been the case. Machine learning can provide better insights to the existing factory operators, introducing new levels of product consistency and security. This, in turn, results in greater resiliency for manufacturers during periods of market uncertainty.
What Machine Learning Can Do For the Manufacturing Industry
Machine learning is one of the main technological advances that is allowing Industry 4.0 to gain a foothold in businesses and on factory floors. Machine learning is essentially a form of artificial intelligence that allows systems and algorithms to automatically improve based on experience.
Thanks to ML, there has been massive progress in the manufacturing space in the form of optimization. By optimizing systems in factories to be compatible with ML, manufacturers are able to create what is called a “smart factory”.
Using smart devices, machines, and systems, smart factories are digitized factories that continually monitor production and collect data. This data collection provides manufacturers with advanced analytics, allowing companies to make more informed decisions.
Ultimately, one of machine learning’s most discernible impacts in the manufacturing space is likely its capacity to increase efficiency without significantly changing existing resources. An example is real-time error detection. Utilizing smart devices on the factory floor, smart factories can assess product quality instantly. Video streaming devices that are integrated with ML are able to analyze a product throughout the entire production process.
Then, the system can quickly review frame by frame and detect any potential defects. Using ML, visual analysis, and live process monitoring, engineers receive actionable insights in real-time to correct those defects.
In the manufacturing industry, this unlocks a rate of inspection that most factories could not previously afford when performed manually. The reduced overall time of inspection means a quicker production line, equating to more business.
Besides just providing improved visibility and statistics, this ability to communicate problems in real-time allows factories to significantly reduce both downtime and scrap which currently occur under infrequent human-based monitoring.
Additionally, machine learning solutions can provide predictive insights, allowing factories to transition from reactionary environments to ones that stop errors before they occur. Predictive maintenance is when machine learning allows for early detection of potential breakdowns in the manufacturing process. By alerting the team to potential issues, it reduces costs because preventative repairs are far cheaper than fixing a fully broken machine or a process that has already generated tens of thousands of dollars of scrap. It also lessens the economic impact of a major break.
While it may be great to talk about these changes and their potential, in theory, there is ample also market proof that smart factories can have a massive impact on business. For example, Harley-Davidson’s recent foray into operating a smart factory saw a 21-day production cycle reduced to just 6 hours. This shift has allowed the motorcycle manufacturers to reduce production costs by $200 million. Harley-Davidson’s accomplishments are not a unique case study. Other manufacturers like GE, Audi, and Siemens have reported similar successes. This isn’t to say that smart factories only benefit massive companies, though.
ABB demonstrated that modest and achievable improvements, such as 10% improvement in OEE, can almost double the profit for most factories.
Industry 4.0’s Impact on Today
It is hard to find an industry that has not been impacted by the worldwide COVID-19 pandemic. Manufacturing is no different. However, digital transformation and applied machine learning in the industrial sector can help mitigate in mitigating the effects.
Industry 4.0 tools such as enterprise monitoring platforms enable teams to remotely monitor multiple factories as data is collected without the need of human hands. These data are then transferred and stored in the cloud so the information can be analyzed and viewed on dashboards from anywhere. The benefits are obvious. During a time when experts are suggesting social distancing, being able to reliably monitor the health of a factory without the need for a crowded floor is safer for employees and more convenient for companies.
Some older factories and companies have resisted the Industry 4.0 adoption because of inherent distrust in technology and prefer their tried and tested methods. Despite the unique challenges of 2020 is a hardship for many, the advantages an Industry 4.0 environment presents in a world that is operating remotely have become ever more and more apparent. While businesses, at the moment may be, are focused concentrated on weathering the storm and conserving cash, But, with that said, don’t be surprised to see players thriving in traditional hold-out industries by embracing machine learning and other Industry 4.0 tools like remote monitoring platforms.
The potential for Industry’s 4.0’s mass expansion, coupled with the numerous success stories from smart factories already penetrating the market, goes to show that machine learning’s impact on the manufacturing space moving forward cannot be understated. Nor are these impacts small or incremental.
Adopting Industry 4.0 techniques and technologies will bring about immediate gains in efficiency and savings that measure in the as well as savings that don’t just measure in the millions but rather the hundreds of millions. The continued advancement and adoption of Industry 4.0 make the manufacturing space one to watch for massive growth.