The DeepTech Effect in Manufacturing

The second edition of DQDeepTech had a session on the DeepTech effect in manufacturing. The panelists spoke about the role of deeptech in the supply chain management and shop floor management.

Prajakt Raut, Managing Partner, Supply Chain Labs, Lumis Partners, said deeptech is an advanced aid of technology for enhancing the value. There are manufacturing priorities such as product, time-to-market, employee safety, etc. After the pandemic, most suppliers have made their supply chains resilient. The kind of technologies that companies require now, need collaboration among multiple stakeholders. That's the single biggest change. Industries, including manufacturing sector, are now looking at deploying technologies.

Synchronization among multiple stakeholders is now very important. Technologies such as IoT, 3D printing, blockchain, AI/ML are now being employed. Use of these technologies enhances the company's values. IoT is used for predictive maintenance. You can now layer this with blockchain. That authenticates participants to be in the ecosystem. Use case specific things are now done by companies. Optimization has now been taken to a wider level. Procurement has been automated so that they can efficient operationally. Lot of AI and analytics are getting into the system that were previously not possible. Some people are able to build strategies that will help the company. Layered with AI, the system can the organization on the most optimal way.

Synchronization between multiple stakeholders and use case context across the organizations is now happening. Lot more people are building more nimble solutions on top of their automation and ERP systems. Supply chain start ups come in here, and they build nimble solutions and specific solutions within the ERP. This makes the organization's automation lot more resilient.

SP Arya, Director and Managing Partner, BizTek Advisors, added that deeptech is not new. They have been using this on shop floor work. With latest technologies, such as AI/ML, IIoT, blockchain, etc., there are lot of solutions. Deeptech has to be the convergence of 2-3 technologies. Managing shop floor is a complex task. Deeptech is all about that, and catching up fast. It will go into trillions of dollars in the years ahead. Everyone has to join in and bring deeptech concepts to speed up shop floor management effectively.

Manufacturing requires people on the shop floor. More solutions are being targeted with less manpower. Organizations have been forced to automate due to pandemic. Manufacturing steps are being automated, and even the shop floor. They are getting higher outputs with less manpower. We have to create a balance as to not affect the jobs too much, and not compromising with product and quality.

Manish Jha, CIO, Addverb Technologies said they are building the manufacturing education system (MES) that controls the planning the shop floor. Manufacturing industry has challenges such as time taken on shop floor and product quality. We have to meet the challenges and deliver product. We applied deeptech into MES. We are optimizing the resources. We know when and where to do quality checks. We control product quality. We can find out which resources are efficient. We are also applying deeptech in supply chain and material movement in warehouses. We ensure the supply chain is efficient for the next step forward.

Deeptech can play a role in business continuity. We were able to trace supply chain problems. We can predict output of the factory using the supply chain data available, without coming to the office.

Bernard Rolfe, Prof. of Advanced Manufacturing, Deakin University, Australia, spoke about how additive manufacturing can be a powerful assist for regional and rural Industry 4.0. The Industry 4.0 is the next transition where mass production migrates to mass customization. This has new fabrication techniques using IIoT, AI, and robotics. Today, rural and regional population are decreasing, relative to large urban population.

We have analyzed the synergy of Industry 4.0 and additive manufacturing, and reshape the future of manufacturing. Creating easily accessible information, on-demand, during full lifecycle of components and products is a key benefit and challenge for Industry 4.0. We can get improved (predictive) maintenance benefits.

Industry 4.0 data spectrum is broad and has IIoT and cyber-physical systems (CPS) such as components, subsystems, systems, and products with integrated sensors and actuators. Cyber security can ensure safe, secure, and accuratee sharing of dynamic data throughout the supply chain. Vehicles are migrating toward self-contained mobile clouds of sensors and actuators. We also need to integrate tagging technology. In the next industry cycle (5.0), we may see smart materials with memory, processing, etc.

Dense tagging makes it difficult to counterfeit products. Product refinements also become automated. There is continuous design and evolution cycle. Cyber security can be enhanced with auto-immunity applied for malware protection. Additive manufacturing allows us to put in sensors. Consistent and continually refined data has to be acquired for wear and tear of product, repair in different additive manufacturing processes, etc. This is also a major distinguishing factor between Industry 4.0 and previous revolutions.

Industry 4.0 has some intelligent features. Industry 4.0 software-oriented architecture (SOA) is integated by the IIoT electronic devices to afford real-time access to production data, and development and use cycle. Additive manufacturing also has exclusive features to enhance Industry 4.0, like mass customization, or personalized design. Another advantage is light-weighting, thanks to topology optimization. All of this is also using IIoT for optimization. Key impact of customer feedback into the design and production cycle leads to reduction in manufacturing costs and waste.

Industry 4.0 assists additive manufacturing in future. There is visualization of data or knowledge with simulations. We can do even more efficient work in future. Big Data within additive manufacturing can be obtained by embedding multiple sensors around the build chamber. Additive manufacturing and automation offers a gateway for rural and regional businesses to compete against metropolitan businesses.