In this special guest feature, Eric Whitley, Director of Smart Manufacturing at L2L, believes that machine learning is so powerful precisely because it grows machine knowledge in a continuous feedback loop and becomes exponentially smarter. But what can it do for your business? This article will provide insights into the five benefits of machine learning for manufacturers. For over 30 years, Eric has been a noteworthy leader in the Manufacturing space. In addition to the many publications and articles Eric has written on various manufacturing topics, you may know him from his efforts leading the Total Productive Maintenance effort at Autoliv ASP or from his involvement in the Management Certification programs at The Ohio State University, where he served as an adjunct faculty member. After an extensive career as a reliability and business improvement consultant, he joined L2L, where he currently serves as the Director of Smart Manufacturing. His role in this position is to help clients learn and implement L2L’s pragmatic and simple approach to corporate digital transformation.
These past years have been difficult times for many manufacturers. A global pandemic that lasted nearly two years was immediately followed by a war in Europe, and rampant inflation as energy prices soared. Unsurprisingly, competition and margins are tighter than ever. Machine learning may be just the competitive advantage needed.
IBM defines machine learning (ML) as “a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy”.
Machine learning is so powerful precisely because it grows machine knowledge in a continuous feedback loop and becomes exponentially smarter. But what can it do for your business? This article will provide insights into the five benefits of machine learning for manufacturers.
Becoming the best smart factory
What does it mean to be a ‘smart factory’ or to engage in ‘smart manufacturing’? A smart factory is a highly digitized factory that uses a combination of AI, ML, cloud computing, and the industrial internet of things (IIoT), in conjunction with hundreds or even thousands of sensors. The result is real-time data gathering and analysis that can be achieved on an ongoing, super-fast basis.
Smart manufacturing, which is integral to Industry 4.0, connects machines, equipment, people, and processes in a way that expedites efficiency by making quick and smart use of data. An example is the analysis of movements on the shop floor which can facilitate more efficient placement of shop-floor assets — such as machinery, materials, and people.
Raise your efficiency
As already noted, a smart factory means improved efficiency. Improved efficiency rates can be precious for companies involved in complex manufacturing, such as those in the automotive, heavy engineering, food production, and plastics industries. An example of efficiency taken to the next level is ML for production (ML4P), which was a technology developed in Germany in early 2022.
ML4P fits fleets of often complex machinery with scores of sensors. These sensors enable super-dense, split-second data analysis that can instantly ‘read’ any aspect of machinery, from overheating to how individual components are operating.
Another benefit of ML4P technology is that it also enables production that is more efficient in energy and resources or material inputs. These efficiencies should be especially appealing given the far higher costs of energy and transportation that emerged in 2022.
Better data management
The amount of data in the world is astounding: according to the International Data Corporation (IDC), it is predicted that worldwide data will amount to 143 zettabytes of data by 2024. To put that into perspective, a zettabyte is equivalent to one billion terabytes or a trillion gigabytes.
Modern factories are notable reservoirs of data. Maintenance management is a prime example of a function that can put smart data to excellent use. Industrial maintenance automation can provide automated, precise insights into equipment failure modes and frequencies, as well as automate the required maintenance scheduling.
It can also help pinpoint ‘downtime culprits’, which are often human-related, resulting in improved Maintenance Repair and Operations (MRO). The result is optimized inventory and asset management, which means cost savings as sudden breakdowns and expensive shutdowns are avoided.
Improve your data security
So much data also means a heightened risk of data breaches within production facilities, thereby necessitating the need for improved data security. Machine learning can greatly enhance data security, especially within a Zero Trust Security (ZTS) framework. In terms of data security, ZTS works on the basis of ‘never trust, always verify, enforce least privilege’. This rigid, uncompromising approach to data security controls access from both inside and outside the network.
It is machine learning that makes these heightened levels of data security possible. This is achieved by computer modeling that observes and analyzes all data-related activities by individual users who gain access, particularly to information sensitive to your business.
Thanks to machine learning, manufacturing has been enormously transformed with IIoT, by which machines and processes independently communicate with each other. Edge ML and TinyML allow machine learning to run on miniature devices, such as microcontrollers. For example, each unit on a production line can be checked via tiny cameras for even the smallest of defects.
Another example of dynamic manufacturing is the use of ML-enabled ‘digital twin’ technology, which is a virtual or digital replica (‘twin’) of a physical entity. Digital twin technology can greatly improve research and development (R&D) and help enhance innovation within a factory.
Renault has invested heavily in digital twin technology. The French auto giant has used the technology extensively in recent R&D projects and believes that it provides a competitive advantage in an increasingly unsure and volatile global auto market.
Ultimately, you need to ask yourself: do we wish to keep struggling in the face of ‘black swan’ events like unforeseen pandemics and wars, not to mention fiercer competition and tighter margins? Or do we wish to be a manufacturing firm that is smarter and more efficient, resilient, and profitable?
Opt for the latter with the help of machine learning customized for your processes. It may be one of the best decisions your company has ever made.
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