122 FEATURE balance these two priorities without sacrificing their sustainability commitments, which is a huge challenge that technology can help resolve to an extent.” However, what approaches can manufacturers adopt to establish robust supply networks, operate factories that prioritise safety and security, and simultaneously provide more sustainable products and services? “Visibility is the first step to resilience; manufacturers need to develop always-on visibility into their operations across the supply chain and factories,” says Ladha. “Microsoft Azure IoT, the wider Microsoft technology stack and solutions from our partners can help unlock visibility across operations. Data analytics platforms such as Microsoft Fabric and generative AI capabilities, such as Microsoft Azure OpenAI Service in the cloud, help to democratise analytics, enabling manufacturers to develop resilient supply chains and operate safe, secure and sustainable factories.” To achieve the goal of offering more environmentally friendly products, the initial step lies in designing products with sustainability in mind. Various leading independent software vendor solutions are being used for product design and simulation, which are accessible through Azure’s highperformance computing and AI infrastructure. With this, manufacturers now possess the capacity to generate synthetic data for enhanced simulations, explore design variations for thorough analysis by engineers, optimise parts placement, and even employ inverse design for material exploration and discovery. “By creating digital feedback loops between designing and building products – and optimising operations with the help of AI and people – manufacturers can develop resilient supply chains, and operate safe, secure and intelligent factories,” says Ladha. “This allows delivery of more sustainable products and services.” Ladha explains that business resilience is also dependent on cyber resilience. “To uncover shifting attacker techniques and stop breaches occurring, organisations must be able to see across their applications, endpoints, network and users,” he explains. “With essentials like enabling multi-factor authentication, limiting user access, up-to-date systems and antimalware in place, decision makers can focus on more specific areas of concern.” These decision makers have various areas of focus, such as data governance, compliance assurance, and fostering an environment conducive to sustainable innovation. One notable approach that Ladha highlights is that a manufacturer’s planning process and operations can be enhanced through the utilisation of digital twins for factories. “Manufacturers are looking for a capability for remote operations,” says Ladha. “A single pane of glass where they can see all the enterprise-wide assets, their operations and their performance. They also want to be able to bi-directionally communicate with these assets. That is not only read the data from them but also change settings, parameters and configurations. “They are also looking for capabilities around ‘rewind and replay’. If something goes wrong in the production process, they want to be able to ‘rewind’, go back in time, to see what happened and why it happened. They can perhaps also use the graphical model behind Azure Digital Twins to figure out the dependencies and conduct a root cause analysis and ensure the anomaly does not happen again. “Third, is an ability for manufacturers to track each serialised asset with its own digital twin. This applies to both assets on the factory floor and connected products deployed in the install base. Creating individualised digital twins gives manufacturers a better visibility into the specific assets maintenance needs and conduct predictive maintenance without wasting useful life of the asset.” Ladha also says that manufacturers want to leverage digital twins as the cornerstone for simulations and conducting hypothetical scenarios. They aspire to use digital twins as a platform for digital verification and validation. Their objective is to establish trust in the quantitative assessment of design and production process modifications through simulation and modelling exercises rooted in the digital twin framework.
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