This is the third in a multi-part blog series that focuses on Industry 4.0 in the manufacturing industry.
Certain technologies are considered to be “disruptive” when it comes to Industry 4.0. Some, such as IT-enabled manufacturing and increased computing capacity, can help create smart factories that are highly efficient with fully integrated data.
In fact, according to McKinsey, “Data is the core driver: leaders across industries are leveraging data and analytics to achieve a step change in value creation. A big data/advanced analytics approach can result in a 20 to 25 percent increase in production volume and up to a 45 percent reduction in downtime.”
So what does this look like in the manufacturing industry?
Digitally enabled disruptive technologies that are applicable to Industry 4.0 are those that are most likely to have a significant impact on manufacturing within the next 10 years.
Let’s explore them, as outlined by McKinsey:
Analytics and Intelligence – According to McKinsey, significant knowledge advances have taken place recently in this area. For a long time, robots could only perform simple and repetitive tasks. But, advances in AI and machine learning, as well as a massive increase in available data, has allowed for digitization and automation of knowledge. For example, “IBM’s cognitive system, Watson, is able to answer complex questions based on insights synthesized from vast amounts of unstructured data.”
Data, Computational Power and Connectivity – This group includes big data, the Internet of Things (IoT, or connecting any device with an on and off switch to the Internet and/or to each other) and cloud technology. These solutions allow for the large-scale use of sensors and actuators and for cost-efficient yet powerful storage, transmission and processing.
In the IoT, for example, “sensors and actuators are embedded in physical objects and interconnected by wired and wireless networks. The networks create large volumes of data that flow to computers for analysis, while all physical objects are able to both sense their environments and communicate autonomously among each other,” according to McKinsey.
Digital-to-Physical Conversion – The drivers of 4.0 relevance in this cluster are a combination of decreasing costs, expanding choices of materials and advancements in precision and quality. In addition to additive manufacturing (3-D printing) becoming more relevant, so too are technologies such as advanced robotics as well as options for storing and harvesting energy. “Significant advances in artificial intelligence, machine vision and M2M communication have been made within the field of advanced robotics, along with cheaper actuators,” says McKinsey. “The combination of technologies from these clusters not only enables the translation of the physical into the virtual world but also facilitates the link back from the virtual to the physical world.”
Human-Machine Interaction – This feature is coming about as we see more and more physical interaction between machines and humans, where they are working together in closer physical proximity and where machines can take the weight off tasks that are strenuous for humans.
McKinsey points to the Festo ExoHand as an example. This technology “functions as an exoskeleton, emulating the anatomy and physiology of the human hand. It is worn as a glove and can support straining manual movements by transmitting human hand movements to a robot’s hand. As a result, the worker can conduct a given task for a longer period of time and faster than before.”
In the next blog in our series, we will look at how finding digital inefficiencies shows new value potential for manufacturers.
Can’t get enough 4.0? You can FIND OUT MORE about the Industry 4.0 revolution and how MarkLogic is helping customers industrialize their data!
Also, check out previous blogs in this series:
Like what you just read, here are a few more articles for you to check out or you can visit our blog overview page to see more.
Learn about data bias in AI, ways technology can help overcome it, why AI still needs humans, and how you can achieve transparency.
Successfully responding to changes in the business landscape requires data agility. Learn what visionary organizations have done, and how you can start your journey.
Sharing data can be relatively easy. Sharing our specialized knowledge about data is harder – and current approaches don’t scale.
Don’t waste time stitching together components. MarkLogic combines the power of a multi-model database, search, and semantic AI technology in a single platform with mastering, metadata management, government-grade security and more.Request a Demo