It’s a lens, plus a rear-view mirror and a telescope: Javed Ahmed, Schneider Electric India

Javed Ahmed, SVP – Global Supply Chain International, Schneider Electric India talks to Dataquest about the solutions the company has to offer for digital twin, and much more about the development and deployment of the technology.

What are the solutions you are offering for a digital twin?

We have a digital twin at two levels in Schneider Electric – Asset Twin and Process Twin. It helps us to plan, do, check and act throughout the lifecycle of assets and processes. In Asset Twin – AOA, Aveva Insight, Machine Advisor– we get data from sensors and PLCs and provide a real-time visual representation of assets, historic data, and predictive analytics. This helps in reducing unplanned downtime. In Process Twin – EcoStruxure Building Operations, Building Advisor, Aveva Process simulation helps in monitoring and managing building operations and manufacturing process.

How can India build a resilient supply chain by digitisation?

The resilient supply chain is the ability to respond to external changes in an optimal way. Digitisation is the foundational block to have data-enabled decision-making. When we have an end-to-end supply chain digitised it helps us make real-time decisions. It also helps us do simulation; optimisation and advanced analytics that helps us plan and respond to external changes. For example, network modeling, supply chain stress tests can be performed in a virtual environment.

How digital twin is crucial to the development of IoT technology?

Digital twin is the virtual representation of physical environment. To get real-time data from the physical environment it is vital to have sensors and systems continuously communicating the data to the cloud. IoT provides this unique opportunity to build a dynamic real-time digital twin. With emerging use cases, a digital twin is eventually propelling the development of IoT technology.

What organisational challenges do you see in India? How will digital twins play a role in identifying those issues?

We see a couple of challenges in manufacturing that can be identified and managed using digital twins. ‘Quality challenge’ is improving quality by detecting failures and causal factors in advance to reduce non-quality costs and improve manufacturing acceptability in the global market. ‘Downtime reduction’, by having real-time asset data and the ability to simulate and predict failures, helps us reduce machine downtime. ‘Productivity/ throughput improvement’,by simulating manufacturing process in the virtual environment and optimising by improving cycle time, reducing inventory, improving safety and ergonomics, helps in improving productivity and throughput yield.

Will the implementation of AI and data analytics in the digital twin enable us to gain more insights? How?

The digital twin is not only about real-time visibility (like a lens), it also includes history (like a rear-view mirror) as well as the future – to analyse, predict and foresee what will happen (like a telescope). Digital twin collects a huge amount of data, when we apply advanced analytics to it, we are able to detect anomalies, predict failures, and control assets and processes in real time.

In the supply chain process, digital twin coupled with AI analyses, simulation, and optimiser helps us respond to business changes such as raw material shortage, logistics route changes, or inventory alignment in an agile manner improving customer experience, service level, and reducing cost.