Prashanth Mysore discusses smart manufacturing 

Smart manufacturing can be defined as combining innovative technology and people to improve decision making, which results in an increase in manufacturing output, efficiency and quality. One of the primary goals of smart manufacturing is to support larger business strategies in the manufacturing sector, such as mass customization and on-shoring/near shoring, which can be used to secure market share. 

• Smart manufacturing has a whole host of use cases, with some of the most common being:
• Giving manufacturers access to data-driven decision-making, where insights from machines, sensors, and systems are used to continuously improve processes
• Providing interconnected visibility across operations, with systems communicating and optimizing across departments and job function within the factory and extended supply chain
• Using AI and predictive capabilities, to detect possible disruptions, such as anticipating equipment failures to reduce downtime or waste 

Ultimately, smart manufacturing is not just about automation, it’s about creating a resilient, responsive, and intelligent production ecosystem that can adapt to market demands and operational challenges that empower manufacturers to make better and more informed decisions.

Adoption of smart manufacturing technology 

The smart manufacturing market is broadly defined and spans everything from automation equipment to software. Generally, this space is growing at a strong pace globally, driven by rising demand for operational efficiency, supply chain resilience, and the need for digital transformation across sectors like automotive, aerospace, electronics, food and beverage, and pharmaceuticals. However, adoption is uneven across regions and industries due to several practical and strategic factors. This varies by country and by industry. 

In one research report, the global smart manufacturing market is valued at between $300 billion–$400 billion (2024 estimates) and is projected to grow at a CAGR of ten to 15 percent. Adoption is strongest in developed economies such as North America, Germany, Japan and South Korea, where markets are more mature, and government incentives exist, or where a changing workforce is accelerating implementation. Emerging economies are increasingly adopting smart technologies, but infrastructure gaps and capital constraints slow full deployment. Again, these numbers are large and cover a wide array of assets that manufacturers can deploy to deliver smart manufacturing. 

New start up manufacturers who are more nimble and lack the burden of heavy IT infrastructure and OT infrastructure, are leveraging smart manufacturing technology to advance their business plans. These companies have the potential to succeed and alter markets where these manufacturers compete. 

While some technologies are easy to implement for businesses, there are several other smart manufacturing technologies that are being adopted, but at a much slower rate. 

What challenges are there with the implementation of smart manufacturing technology? 

As with the implementation of any new system in a business setting, there are several challenges to overcome, and that’s no different with smart manufacturing. From training to integration to data to culture, there are several considerations that need to be taken into account when investing with new smart manufacturing technology. For example: 

1. Legacy systems and infrastructure that are difficult to retrofit or integrate

One obstacle with the implementation of new smart technology is existing infrastructure. Accessing data and rationalizing the data from legacy systems can be time consuming. Mapping business process and data flows for changing and improving decision support can be complex and can consume valuable time of experts, especially when re-imagining new business processes and decision criteria. 

2. Workforce

Many businesses in the manufacturing sector are experiencing a workforce drain from skilled engineers and factory workers leaving due to retirement or other more appealing opportunities. This combined with stretched IT and OT team staffing creates conditions for project failure. Manufacturers need to incentivize and empower the teams working on these projects. The institutionalization of human knowledge and know-how is key as smart manufacturing technology needs this human element to be effective. 

3. Providing a hard ROI on initial investment in new smart manufacturing technology

As with any investment in new technology, it’s essential to justify the ROI for the investment. This can be troublesome for smart manufacturing technology, especially if clearly defined business goals are not set and communicated. There are countless projects that fail due to the lack of business outcome focus with the right business metrics and clearly defined expectations. 

Common issues that smart manufacturing can address 

Many manufacturers are looking at how they can augment their workforce with automation, robotics and AI, especially in countries where manufacturing labor and skills shortages exist. 

Even in countries that aren’t short of skilled workers, companies are looking to improve productivity and efficiency and empower their workers with smart manufacturing technology to make better decisions in a timely manner. Solutions that streamline or automate business processes to improve productivity or improve product quality are in demand. 

For example, AI supported quality control helps to minimize quality issues with products and increases efficiency vs 100 percent human checks. This results in reduced cost of quality and increased customer satisfaction. Along with this, the likes of digital twin technology are essential for simulating and optimizing how to expand, product mix, shift line capacity, and increase factory throughput. 

What trends or big developments are just around the corner? 

The biggest developments within smart manufacturing look to be set around digital twin technology, powered by AI. This will help manufacturers better align manufacturing processes, capacity and assets, inventory, and resource to meet market demands, streamline execution, and take decision making to new heights. 

The focus of digital twin technology has always been about building resilient, adaptive production systems; but the future looks ready to take this one step further by optimizing operations dynamically using AI. Human decision making will improve through the help of AI, where AI can offer recommendations based on large and complex data sets from live factory data applied to the factory and supply chain model. 

All of this means improved decision making, at considerably faster speeds, as factory management will be empowered to act quickly, with greater confidence in all aspects of manufacturing, from new product introduction to process engineering to scheduling to execution to maintenance and everything in between.  

Prashanth Mysore  

www.3ds.com 

Prashanth Mysore is Senior Director, Global Strategic Business Development at Dassault Systèmes. He leads initiatives in Digital Manufacturing, Manufacturing Operations Management, and Supply Chain solutions. He is a recognized expert in Industry 4.0, Industrial IoT (IIoT), AI-powered smart factories, future-ready supply chains, and the workforce of the future empowering people with digital skills and collaborative tools to thrive in tomorrow’s manufacturing landscape.