IoT, AI/ML and Digital Twins Boost Efficiency on the Shop Floor

The Internet of Things (IoT), AI/ML and digital twins are technologies with the potential to dramatically transform manufacturing, making processes more efficient and sustainable, and providing greater agility to cope with a volatile and unpredictable business world.

IoT sensors can give manufacturers access to unprecedented amounts of data generated by production assets and supply chains. But the challenge is understanding that data and reacting to it in a timely fashion.

NTT DATA’s Connected Shop Floor accelerator, which is based on SAP Digital Manufacturing, makes it easier for manufacturers to collect, share and analyze the data produced by IoT devices and integrate it into a truly data-driven manufacturing environment.

The accelerator is enhanced with AI/ML technologies to help analyse and understand what real-time production and sensor data means and, if required, enable systems and machines to perform actions based on a real-time analysis of the data and without the need for human involvement – if a machine’s temperature starts to suddenly rise, for example.

By feeding the data produced by the Connected Shop Floor accelerator into a digital twin, production managers can see exactly what it is happening on their shop floor with the data displayed in a way that is visually intuitive and easier to understand.

What Are Digital Twins?

Digital twins represent virtual counterparts of physical objects or systems, mirroring their real-world counterparts in a digital space. When integrated with IoT sensors and AI/ML, these twins become dynamic and responsive, facilitating a continuous exchange of data between the physical and digital domains.

In manufacturing, this convergence offers valuable insights and control over assets, so enabling manufacturers to anticipate maintenance requirements, track device performance, and detect possible problems on the shop floor by analyzing data displayed as a virtual representation.

Digital twins provide manufacturers with a comprehensive view of the production process. Simulating and optimizing workflows in the digital realm allows for the identification of potential bottlenecks and inefficiencies before they impact physical operations. This fosters continuous improvement through scenario testing and optimization.

Consider the example of a manufacturing plant using digital twins to simulate different production schedules and resource allocations. By analyzing performance metrics from the virtual representation, manufacturers can fine-tune processes for maximum efficiency, reducing waste and enhancing overall productivity.

Boosting Sustainability Using Digital Twins and IoT

Manufacturers can reduce their energy consumption, minimize waste, and lower their carbon footprint by optimizing processes and resources through continuous monitoring and analysis using these technologies.

Digital twins can simulate energy usage across different production scenarios, identifying opportunities to reduce energy consumption. IoT sensors can monitor resource usage in real time, allowing for better management of raw materials and reduction of unnecessary waste.

By employing sensors to monitor equipment conditions, digital twins can monitor and forecast maintenance requirements, allowing for proactive interventions. This minimizes downtime and extends machinery lifespan, resulting in significant cost savings.

Sensors embedded in production machinery collect data on temperature, vibration, and usage patterns. This data is then analyzed in real time by the corresponding digital twin, which triggers maintenance alerts when anomalies or potential issues are detected. This proactive approach prevents costly breakdowns and ensures efficient equipment operation.

Data-driven Supply Chains

IoT sensors are increasingly being integrated into supply chain management systems to optimize logistics and inventory management, so helping reduce the carbon footprint of supply chains.

This is particularly important in industries such as automotive with closed-loop supply chains that need to orchestrate complex movements of supplies, components, returnable transport packaging (RTP) and finished goods. 

IoT sensors mounted in goods yards and warehouses, for example, can provide real-time data on the movement and condition of goods, while digital twins could provide a virtual representation of the entire supply chain.

This integration allows for improved visibility, enabling timely responses to disruptions and informed decision-making. For instance, if a shipment is delayed, using a digital twin the production manager could swiftly reconfigure the production schedule and adjust inventory levels to minimize the impact on customer deliveries.

Businesses are having to rethink and redesign their supply chains to become more agile, efficient, and sustainable, and to do this they are employing a range of advanced digital technologies: IoT sensors, digital twins, planning software such as SAP IBP and more specialized solutions. For example, NTT DATA has developed a NTT DATA Sustainable Packaging accelerator, hosted on SAP BTP, to help automotive companies optimize RTP management using carbon footprint data.

The convergence of digital twins, AI/ML and IoT in manufacturing is reshaping the industry by unlocking operational efficiencies, improving planning and decision-making and boosting sustainability.

 As the manufacturing sector embraces digital transformation, these technologies will undoubtedly help shape the future, fostering innovation and boosting resilience in an increasingly unpredictable business landscape.