IoT - Revolutionizing Manufacturing

Raghuram Joshi, Senior GM, IOT & Cloud Implementations at Robert Bosch Engineering and Business Solutions Private LimitedAs Senior General Manager at Bosch Engineering & Business Solutions(RBEI), Raghuram Joshi heads the Enterprise Solutions Business Unit. Apart from driving revenue, market share & profitability, he has been instrumental in setting up the competence center for Bosch IoT platform(BICS), BPM and BRM products. He is currently spearheading a major strategic initiative for transforming IT from a functional focus to an enabler of innovation in Bosch in the IoT and Digital Transformation space.

How does IoT help in improving productivity, quality and reduce cost in manufacturing. What is beyond ROI in IoT?
Internet of Things (IoT) is an essential element of the ongoing transformation in Manufacturing sector, sometimes called i4.0, Smart Factories or Factories of the future. The fundamental aspect of enabling machines, equipment and tools, digitally is driven by a common thread of IoT. Enabling them to be managed in the digital realm creates new opportunities that were not possible in the past. This can, at the least, help machines monitor themselves and in a fully implemented scenario enable them to talk to each other. A very desirable scenario of efficient production based on highly networked and automated machines is now a definite possibility. In such a digitized setup, it is possible to monitor production flow, quality inspections and fulfillment in realtime. Automated actions can be initiated for preventive maintenance, compliance, asset management to name a few.

One of the aims of i4.0 to utilize the same production line to manufacture different variants can also be enabled thereby reducing time to market and enhancing customer centricity. Use of data analytics for predictive scenarios for maintenance, logistics, and supplier engagement can provide opportunities for further efficiency gains.

How can the Internet of Things(IoT) display Marketing ROI and analyze the potential ROI of the various use cases? Please explain how to gain ROI from IOT.
There are several instances where IoT has delivered tangible results, proving ROI is real. Once a digital platform is established, it is possible to intensify monitoring and take realtime actions in case of deviations. These can be further extended to automate compliance to a large extent. By eliminating human intervention for monitoring and automation, there is a positive side effect of improved quality and productivity. These can be measured against baselines and thereby tangible ROI gains can be demonstrated. Similar gains can also
be quantified in absolute terms for other use cases for preventive maintenance, asset tracking and logistics to name a few. There are other derived benefits like time to market, transparency and trace ability. Of course, these gains can only partially be explained in quantifiable terms.

Implementing IoT or digital technology opens up a new realm of possibilities in the non linear space. Traditional business models are linear in nature turnover directly linked to production. New revenue streams based on data economy open up opportunities that were not available in pure play engineering or product space. However, one has to keep in mind that to derive maximum benefits, the three layers of such systems Sensors, Software and Services need to be rightly explored. Each layer can offer its own monetizing prospects and justify ROI.

Implementing IoT or digital technology opens up a new realm of possibilities in the nonlinear space

Many IoT solutions are still basic, but we expect manufacturers to eventually implement more complex technologies, such as autonomous robots and augmented reality(AR) tools. What is your opinion in this regard?
Confluence of technology ranging from IoT, AI and AR/VR makes a very exciting and alluring preposition in the wider scope of manufacturing related implementations. One has to accept, however, that some of these technologies are still evolving and may need more maturity to be adopted in an industrialized scenario. Having said that, it is also true that there are already really good cases that have graduated to industrial grade like those in monitoring, compliance and logistics.

Automation in the manufacturing segment is not really a new phenomenon. Robots have been around to carry out mundane and repeatable tasks for a pretty long time. They are increasingly being considered in hazardous environment and thereby reducing the risk for the employees. Then, of course,there are obvious advantages from robots of reduced errors, increased efficiency and through put.

Advancement in Artificial Intelligence, mainly machine learning, and Sensors would lead to higher adoption as we move ahead. Machine learning, can add the needed skill diversity to robots that are essential in variant management. Greater adoption of kinaesthetic sensors will see robots moving into newer spaces which have been restricted to human intervention until now.

While batch size of one, which is one of the goals of i4.0, a large part of the answer to making it economically viable would be with robots.

Barriers around cyber attacks, determining ROI, integrating the IoT into a factory create challenges for manufacturers as they begin to upgrade to the IoT. How do manufacturers overcome this barrier?
Let me ponder over two aspects Integrating IoT in a factory and Security.

Firstly, introducing IoT and thereby connecting existing machines and equipment is a typical challenge of dealing with legacy systems, it gets compounded by the fact that we are operating not just at software levels. There is rarely a case of ‘one solution fits all’ and implementing it at a wider scale is an effort and cost intensive venture. Secondly, the basic need to create digital twins to create a larger digital ecosystem is again an intensive exercise as the building blocks in existing setup are not designed for digital integration. Thirdly, making retrofit frameworks to deliver usable outcome needs a reasonably good application of data analytics.