Probably, you have already heard from more than an industrial expert said, he has a feeling of IT companies/department are pushing so hard AI-based solution with a proper analysis of the shop floor complexity, behavior and real demands. In the last years, the words machine learning and artificial intelligence are risen as one the most important flag into the industrial transformation. Production and logistic scheduling optimization, remaining useful life, condition-based monitoring, factory digital twin automation, on process quality control or workforce optimization are some of the most common offers on the IT/AI-centered company’s portfolios nowadays, but the question is it want the manufacturing industry really need today? Are the foundations well create in the manufacturing industry around the world? Are we really working in an industry revolution?
Recently a podcast presented by Stefan Jockusch , vice president for strategy at Siemens Digital Industries Software answered some of the previous questions. According to Dr. Jockusch, his vision of a smart factory is abuzz with “independent, moving” robots. But they don’t stop at making one or three or five things. No, this factory is “self-organizing.”
Let’s go deep in his definition of self-organized factory:
- It will self-organize itself to do something different. In other words, the AI-based solutions will have the freedom and flexibility to orchestrate the entire production, logistic and maintenance planning in order to dynamically allocate the optimal resources, uptimes and material work differently when I come in with a very different product.
- Embedded solutions with AI, which support real-time decision-making. According to the author and current trends, AI presence has been increasing exponentially in the last decade, so, it is not a secret that everyday more industrial products will embedding more and more AI solution in their core, so every provider, no matter how small you are in the value chain have to look for the big picture, were you component has to interact with others AIs in a regulated and ethical ecosystem.
- AI must fine his own way to do things in order to elevate the human knowledge to the next level. Today, most AI is helping humans make better decisions, but is this the plan? It is clear to us that AI could make thing faster, predict events better and act considering million of option in milliseconds, something that human being limited to do, but again, the role of AI is support human decision making, learning from human behavior or it is to explore new universe or solutions that the human brain it is impossible to figure out.
- AI application on an edge device that’s sitting right in the factory to monitor that machine and make an accurate prediction when it’s time to do the maintenance. Today, big tech companies are promoting cloud-based solutions as one of the symbols of the digital revolution, but it was this original thought for manufacturing? Please, don’t judge me wrong cloud computing is one of the really wining of the IT companies to provide services in a massive scale, but manufacturing is an especial scenario, were security and time-to-response are crucial to avoid massive disasters.
- We are just at the starting to really understand what optimization of a process can do for the enterprise as a whole. Today, the mention of word “optimal” capture attentions in business presentation around the world. Optimal resource planning, optimal fleet management, optimal energy consumption, etc. are some of the most common slogan in commercial offers from IT companies, but again, if we move to manufacturing, are we really working with optimal configuration today? In our modest opinion, industry application is using optimal more as a pre-sales slogan that a real industrial application. Manufacturing is a complex and dynamical environment, where the status quo is moving faster form a condition A to B, so any particular use cases were solutions achieve optimal frontier, but we are still far to say manufacturing industry is running under optimal conditions.
Before to conclude our weekly post, we want to share a last reflection. Looking to the future of the AI in manufacturing industry, we see two potential path: (1) a close and solid industrial sector, how will be open to adopt only validated solutions to improve the current best practices, with the objective to produce more, faster and reduce cost and unplanned downtime or (2) a transformation of sector to an open and collaborative universe where companies assume the challenges and production loses in order to learn a new way to do thing, breaking with the best practices known nowadays. If we look to the history in comparison with other sector manufacturing always select the path No.1, probably you do not agree, but even the first and second revolutions provide new tools and methods to the humans, but never cross the barrier of the human understanding. It was always a controlled and well calculated transition from one production philosophy to other, but if we are talking of AI, for the first time in the history of the humanity, something con arrive to conclusion that a human being is impossible to figure out, so the last question is: are we ready to learn this time from the machines?
In future posts, we will be continue writing about technology and business trends for enterprises. Furthermore, we recommend consulting the following literature to continue your digital transformation journey:
- Designed for Digital: How to Architect Your Business for Sustained Success, MIT review
- The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives, by Simon & Schuster
- Artificial Intelligence: The Insights You Need, by Harvard Business Review
- The Year in Tech, 2021: The Insights You Need, by Harvard Business Review
- The Deep Learning Revolution, by MIT Press
- Competing in the Age of AI, by Harvard Review Press
The objective of this blog is to provide a personal vision of how digital transformation trends will be impacting in our daily activities, businesses and lifestyle.
Industry 4.0 and Smart-mobility expert, his research interest includes Industry 4.0, Smart-Maintenance, Process Optimization, Machine Learning, AI engineering and Cloud-based solutions.