By Glenn Graney, Director, Industrial & High Tech Division, QAD
Whether its called Industry 4.0, Industrie 4.0, Smart Manufacturing or even China 2025, the basic idea is the same: The entire manufacturing value chain is experiencing a technology-driven transition in terms of capabilities and expectations.
It is not simply about replacing people with machines, but instead about how people, interconnected sensors, machines and artificial intelligence can work together more effectively.
The transition is not revolutionary in terms of the goals and aspirations of manufacturers. Manufacturers remain motivated to increase productivity, deliver quality products — delighting customers and achieving greater profitability and sustainability. What makes this transition distinct are the unprecedented technological capabilities that may potentially impact all aspects of manufacturing execution.
To remain competitive, manufacturers should plan to adopt new technologies rapidly. There are several key catalysts driving this Industry 4.0 transition in manufacturing:
- Pervasive connectivity – connectivity has permeated everything. It redefines the notion of interoperability. Systems that existed in isolated silos are now connected making the sharing of data a foundation for Industry 4.0 in much the same way the Internet became a backbone for a transformation in the way businesses deliver goods and services.
- Smart sensors and enabled data sources – affordable sensor technology has enabled access to data at the atomic level of devices via the Industrial Internet of Things (IIoT), not just for raw data but increasingly for intelligent messaging and services as well. For example, a smart sensor in a cold chain can send a warning alert when a food item exceeds the desired temperature. It can-+ then send a follow-up alert indicating the food item is no longer viable because the temperature exceeded the maximum for a specified amount of time. In contrast, a traditional, non-smart sensor would send all temperature readings on a constant basis with no suggested action. Data that was once too expensive to capture can now be economically gathered and used to make better, data driven decisions.
- Advanced analytics and big data storage – traditional business intelligence and data warehousing are transitioning to richer visual and collaborative predictive analytics models that tap into more dynamic, real time and non-explicitly related data. Advanced analytics can answer critical questions even before they are asked. For example, using machine learning, machine yields can be compared and optimized. Leveraging supplier item information, the system can determine if supplier product quality may be a significant factor. These could be done without explicitly running an analysis of supplier parts or machine yield.
- Accelerated and advanced computing – raw computing power continues to move at blinding speed with cloud and edge computing blurring scale and capacity into what seems potentially like an unlimited resource.
In many cases, the agents of Industry 4.0 combine one or more of these catalysts into new, innovate capabilities. Artificial Intelligence (AI) is already transitioning from an academic exercise to an impactful business proposition. The practical adoption of AI through machine learning results directly from enhanced connectivity, smarter sensors, advanced analytics and super scalable systems. AI is becoming a proactive element in advanced manufacturing, product life cycle management and enterprise asset management.
Proactivity is based on context and what it has learned, not simply based on established metrics such as performing preventative maintained based on accumulated run time or placing an order based on a predefined reorder point.
Today we need to know the questions we want to ask a system. Tomorrow, AI can tell us the questions we should be asking.
The foundational elements of Industry 4.0 also support traditional activities like product development and process innovation. The advances of additive manufacturing (3D printing) and other technologies are clearly supported by better data sharing and the power of iterations supported by advanced analytics, unprecedented computing power and the concept of digital twins.
A digital twin, also sometimes called a “device shadow,” is a computerized simulation of a physical asset that reflects the status of the actual asset, often fed by sensors. For example, a digital twin can help product development and process innovation by showing the unique attributes and performance of any given instance of a product. These attributes might include the type and level of use, the specific part revisions used within the product, the suppliers of those parts, the maintenance conducted, etc.
Industry 4.0 can extend beyond the manufacturer into the use of the product in the customer’s hands by leveraging the Internet of Things (Iot – distinguished from IIoT in this case because of B2C instead of just B2B inference). Charging a customer based on the real usage and thus the value of the product becomes possible. For example, GE charging for thrust hours on jet engines or HP charging per print rather than selling a printer. After-sales service can be optimized to reduce cost and improve customer satisfaction through informed preventive and proactive maintenance and leveraging this information to improve product design.
Most manufacturers are still in the early stages of laying the foundation for this revolution. There is no absolute right path toward Industry 4.0; it varies for every manufacturer. Possibly the greatest challenge wil be to balance the ability and motivation of each manufacturing enterprise to embrace various Industry 4.0 capabilities with the requirement to sustain current daily operational execution.
There is significant business opportunity in the revolution of that changes how things are designed, made and serviced. Those manufacturers who make rapid progress will reap early benefits and will fulfill the competitive promise.
Glenn Graney is the director for the Industrial & High Tech division for QAD. Glenn has 35 years of leadership experience in advanced manufacturing and the associated supporting automation, business and information systems. He has contributed to the successful design and implementation of solutions across an entire range of the most demanding of industrial environments. Most recently Glenn has been engaging with manufacturers on the exciting journey with the advanced technologies associated with Industry 4.0. He has both a BS & MS in Industrial & Manufacturing Engineering from Penn State. Contac: firstname.lastname@example.org