Industry 4.0 has spurred the digital transformation that was essential for vitalizing the production and manufacturing industries. It has bolstered the ecosystem with modern technologies and information, enabling them to square up with global challenges. The Fourth Industrial Revolution has triggered a new wave of technology which is greatly inclined towards using Internet of things (IoT) to optimize and standardize the manufacturing techniques to discern opportunities, enhancing productivity and value creation process. Industry leaders are playing a significant role in encouraging industries adopt and implement Industry 4.0 paradigms.

Image

Dr. Abhik Banerjee, a Research Fellow at IoT Lab, Swinburne University of Technology talks us through the potential of Industry 4.0 and how this shift towards automation will enable industries to perceive comprehensive growth and become robust enough to withstand the unforeseen challenges. In an insightful conversation, he briefed us about how Internet of Things Lab at Swinburne University of Technology intends to resolve to decipher and solve important challenges that exists in the society; spanning across industries, cities, healthcare, retail, and other sectors by leveraging IoT technologies to securely collect, integrate and analyse data in real-time. The IoT Lab has worked with many industrial partners to explore practical problems that are difficult to solve using traditional approaches. The focus is to spur the development of IoT devices and integrate such devices and their data, along with real-time data analysis and actuation. The research program is designed to support the cross-pollination of ideas with the industry partnered projects so that the research is driven by practical problems, and outcomes can be applied to real-world scenarios. Throwing light on which he comments, “The increasing complexities in Industry 4.0, has made room to manoeuvre and realize the potential of IoT. It gives me great pleasure to see that industries are also focusing on exploring the power of the Internet of Things.”

Industry 4.0 has incorporated latest advancements in technology, particularly IoT, Machine Learning & AI with traditional manufacturing practices to create value. Can you help us understand the essence of the complete IoT Stack, ranging from sensor to application in a pure and simple language?

Unlike what might have been thought earlier, IoT is much more than just connecting devices and sensors. As we start seeing greater IoT adoption across industries, we are also beginning to see increased appreciation of the impact IoT can have on a business, which involves not just gathering sensor data, but also analysing and making sense of all this data so that it can be used to drive business decisions.

In a wider context, IoT applications use all the available data and uses it to draw insights, derive and even automate decisions. To do so, the sensed data needs to be identified, collected and pre-processed on an edge computer before sending it to the cloud. As far as the choice of network is concern, one can either opt for a long range or for a low power network. The requirements for IoT data communication are unique, they mostly require small amounts of data to be transported instantaneously, but often from remote deployments which do not have sufficient network connectivity. With the end in view to boost connectivity and simplify the process, networks such as LoRa, NB-IoT are designed and implemented. Once the connectivity options are worked out, the focus remains on the type of analytics that need to be performed, as per the application objectives.

Predictive maintenance can simply be defined as a technique that uses data analysis tools and techniques to detect anomalies while conducting operations and possible defects in equipment and processes, to fix them before they result in failure. Kindly help us to have a wider understanding of predictive maintenance by some real time use case and the impact it created.

Predictive maintenance is the most talked about Industry 4.0 application which emanates from the need to reduce production downtimes. Production efficiency is an important manufacturing KPI and unplanned equipment downtime becomes an obstacle that impedes the productivity. Predictive maintenance plays a major role in addressing certain conditions by identifying maintenance opportunities well in advance. This can be done through analysis of data collected using sensors monitoring all aspects of machine operation. Not only does predicting faults in machines in advance reduce the likelihood of unplanned downtimes, but it also improves the overall efficiency of production plants by enabling advanced planning. For instance, a beverage making facility that was operating at 65% capacity can gain as much as 20% in efficiency, taking the overall efficiency of the production plant to 85%, which can have significant reduction in costs.

Can you throw some light on the significance of Product quality optimization and the role IoT plays in optimizing the process?

Product quality optimization is quite important since it directly relates to the production outputs of a manufacturing plant. All manufacturing plants aim to maintain consistency in the quality of its outputs, which is difficult to achieve especially in the process industry. If the target product quality are not met frequently, it can result in increased wastage or reprocessing of the raw material which eventually leads to increase in the cost. For example, companies that manufacture products like jam, jellies, fabricate carbon and other metals have to keep a constant check on the boiling point so as to achieve the desired quality. To cater to the requirement, innovators must focus on devising an effective Industry 4.0 solution to collect and analyse manufacturing data so as to identify how the machines can be controlled effectively, ensuring significant improvements in productivity which increases the likelihood of meeting desired product quality in every production run.

Improvement of factory productivity eventually leads to transparency and cost-saving which has an impact on the bottom line. Can you comment on this?

One of the factors that makes Industry 4.0 unique is that it involves the act of accumulating the extensive range of data including manufacturing data, sensor data, machine control data as well as process data, and leverage it for production improvements. 

At the present day, it appears that individual manufacturing domains are likely to have their own set of KPIs. However, one way that we can approach this is by analysing some publicly available manufacturing datasets and then interpreting them to build insights.

“Over the years, industries have realized the importance of fetching and processing heterogeneous yet quality data.  I am fascinated by NASSCOM CoE’s initiatives that aim to spread awareness about the transformation companies can achieve by adopting Industry 4.0. I look forward to working together in stimulating digital transformation across verticals, for which we can also explore and discuss insights from publicly available database for the usecases,” concludes Dr. Abhik Banerjee.

The interaction was a free flowing discussion between Amit Borundiya: Head of Technology Nasscom CoE IoT & AI and Dr. Abhik a Research Fellow at IoT Lab, Swinburne University of Technology, on Industry 4.0 from Researcher’s Perspective.