The Rise Of Connected Manufacturing And How Data Is Driving Innovation, Part I
This interview was conducted by Cindy Maike, VP Industry Solutions
The shift towards Industry 4.0 is improving manufacturing efficiency and the factory of the future will increasingly be driven by technology like the Internet of Things (IoT), Automation, Artificial Intelligence (AI), and Cloud Computing.
Already, significant shifts within the industry point towards a more technology-driven approach across everything from supply chain planning to smart factory devices and even wearable tech that employees use as part of their jobs.
The impact of technology on the sector is substantial, a recent survey by PWC found that 87% of leaders within the industry believe that smart factory technology will help accelerate innovation and design improvements.
To find out more about how the evolution of tech might impact the sector I sat down for an interview with Michael Ger, Managing Director of Manufacturing and Automotive at Cloudera.
In Part I of this series below Michael discusses the impact of Industry 4.0 and how it is helping create a more connected future.
Hi Michael, thank you so much for joining. To start us off, we’ve heard so much about the convergence of IT and OT (operational technology) and the impact of Industry 4.0. Can you tell us a little about what this means for the manufacturing industry and why it is so important?
From a data management perspective, I think core to the new notion of Industry 4.0, is really the convergence of IT and OT data.
Operational technology is sensor data that reflects the operational performance of a machine operated piece of equipment. This equipment generates its data from a sensor.
This sensor data exists on a network and has people with very specialized skills who get this network operational and keep it running.
These people are very different organisationally to IT and operations. What this can mean in the real world is that machine-generated data like this is separated from other IT and operational data.
The result is that these operational data sources can remain siloed within the plant and never make it over to the IT side of the house.
Consequently, key decision-makers within a manufacturing context never have access to the data sets that can help truly optimize manufacturing processes.
This is where data lakes come along, helping to bring all these types of data into one place to dramatically improve manufacturing analytics.
What level of adoption are you seeing in manufacturing regarding Industry 4.0?
What we’re seeing in this area is that there is huge interest within manufacturing in terms of moving towards this technology, however, we’re still in the early stages.
Key to moving towards Industry 4.0 type solutions is the mastering of the data management lifecycle; sensor data has become so important because sensor prices have come down by two thirds over the last decade.
Suddenly we are awash in petabytes of sensor data, and this has implications for how fast the technology is adopted.
Realistically, in terms of broad industry adoption, we’re still in the early stages as companies are discovering how best to ingest all this data, bring it into a data lake, blend sensor data with enterprise data sources such as ERP and supply chain systems, and finally use this data for advanced analytics and machine learning.
There is high interest within the sector but we’re definitely still in the early stages.
How does the adoption of this type of technology vary by manufacturing segments, which ones are more predominant than others?
While the adoption of Industry 4.0 technology is occurring across every manufacturing segment, the pace of change is being determined by business imperatives specific to each segment.
Within the automotive industry, the desire to reduce product recalls is driving companies’ adoption of this type of technology.
Recalls cost the industry $22 billion a year, and there is a major need for industry 4.0 analytics to rapidly identify quality problems in the field and trace those problems back to the manufacturing conditions under which a car was made.
Next, automakers need to be able to figure out specifically which other vehicles were manufactured under the same conditions and then recall only those specific vehicles rather than the whole model year fleet.
In other industries such as hi-tech chip development and pharmaceuticals, it is all about optimizing yields since in both of these industries yield is a huge driver of profitability.
Consequently, whether the business is making a vaccine or chips, the objective is to maximize the output of vaccines or chips for any given input.
Is Industry 4.0 applicable to only machines or is there scope for businesses to leverage the concept of ‘Connected Workers’?
One of the recurring concepts of Industry 4.0 development is the idea of man/machine interface and how they can be much more closely aligned.
Workers are intrinsically part of the definition of Industry 4.0 and one of the most interesting concepts in relation to this new technology is the ability to track workers to improve safety.
For example, we have a Steel manufacturer that we are working with that is employing a wearable device on each worker because their plants are huge and the business needs to be able to track their movements through the plant.
They have had injuries in the past so they want to be able to identify safety issues faster, if a worker is idle for a long period of time, he or she may be hurt or injured and nobody knows about it so that is one way tracking data can help improve worker safety.
The other aspect that is important is computer vision, and being able to look at work zones, for example, a plant may have a robotic arm in it that swings around.
With computer vision and tracking geodata, companies can make sure workers never enter a specific geofenced zone, in this case, the danger zone of the swinging robotic arm, and therefore protect them from being hit by the robot.
There are all kinds of use cases where workers and worker safety are being positively impacted by Industry 4.0 technology.
What are your thoughts on how Industry 4.0 will change the workforce?
The primary change Industry 4.0 technology is driving is around the ability to alter and train machines how to behave in certain situations. During the third industrial revolution, when robots first came onto the scene, they were dumb, being programmed to do very repetitive tasks.
With Industry 4.0 there are now more exciting opportunities and companies are collecting data all the time, and vast quantities of data, that describe how machines are actually used in operations. That data can be used to train robots to make better decisions in real-time so those robots are now becoming smart.
Due to this data-driven behavioral training, robots can now do tasks that are much more sophisticated than before and can replace many of the manual tasks and people required in those operations.
The implications to people are that many highly repetitive jobs, once done by humans, are being done by robots. However, this is driving new, higher-skilled opportunities for people to program these machines, train them, and install and maintain these machines too.
There are exciting opportunities for people to do new roles while removing tedious tasks they’re also replacing them with higher-skilled jobs and more training.
It is all about retraining people for the jobs of the future and it offers people a lot of new potential when it comes to jobs.
To get more of Michael’s insights into how data and technology are shaping the future of manufacturing, keep an eye out for Part II of our in-depth interview where he’ll be talking about connected living, AI, and the evolution of autonomous vehicles.
The post The Rise Of Connected Manufacturing And How Data Is Driving Innovation, Part I appeared first on Cloudera Blog.