Utilities: For electricity and water, there are already smart metering programmes being rolled out, but this is resulting in explosion of data in the grid, the question is now what to do with that data. For electricity this could be using it for reducing non-technical losses, grid optimisation or predictive maintenance. For water there is a big focus on sustainability in terms of reduction of non-revenue water, distributed water management and waste water recycling. Yet for all these elements to be achieved, they require analytics with ML and AI to derive intelligence from sensor and smart meter data and respond by optimising processes in real time or near real-time.
Manufacturing: In discrete manufacturing Industry 4.0 is the new mantra. Two key components of the Industry 4.0 that would deliver value are IoT Technologies and Big Data Analytics. Driven by these technologies, the focus is on achieving improvements in production quality, reducing wastage and cycle time, reducing unplanned downtime and improving energy efficiency in the manufacturing process. In the case of car manufacturing, lowering product quality defects leads to fewer warranty claims, which takes resources to resolve and sometimes results in large vehicle recalls. We also see that real time streaming analytics will play a very big role in manufacturing plants achieving the excellence that they aspire for. Establishing a strong omni-bus connectivity with ruggedized industry gateways integrating various standards and protocols that will ensure smooth data streams would be highly essential. High adoption of Robotics and VR driven interventions will also play a major role in driving improvements in multiple areas including diagnostics, operations and maintenance.
Oil and Gas: For process industries adapting analytics to deliver data drive intelligence to find the sweet spots to operate their processes at maximum efficiency and ability to predict in advance anomalies and failures is gaining momentum. Most facilities have changed over time, meaning their processes have gone beyond design or are being operated in unsteady state and assets have become old. On the other hand sensors have become cheap and establishing IP based connectivity has been achieved. Real Time Process optimisation with the use of AI is already a field in its own right, but now by leveraging IoT and ML algorithms, organisations will be able to identify new relationships in their data to improve their existing optimisation models.
Retail: The key aim for retailers is to increase average ticket size and frequency of visits, but to do this you need to understand the mind of the customer to provide them the next best offer in real time. Whilst things like their purchase history and preferences have been used for targeted marketing already, using IoT and AI, it can now be done in real-time.
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