Intelligent software embedded across the manufacturing value chain enables industrial equipment manufacturers to analyse machine performance in real time to pre-empt potential problems and limit costly downtime. Similarly, car manufacturers can analyse diverse streams of data to develop relevant, location-based vehicle services and customised offers for individual car drivers. The connected car of 2016 will be all about new data-based services.
On-demand analytics platforms allow the swift compilation, formatting and cleansing of assorted data streams — both structured and unstructured — that comprise Big Data. Less time needs to be spent on integrating and reporting, and more can be spent analysing critical information that can generate deep insights into customers, operations or channels, allowing manufacturers to seize new opportunities as they occur, or even to get ahead of them
However, a number of hurdles are limiting widespread adoption of analytics, including a widening talent deficit while issues such as governance and funding can slow down adoption. Global complexity of organisations is also a challenge, but there are already some successes. Companies that draw on multiple levels of connectivity and the power of Big Data analytics are offering better, more reliable products. Receiving early feedback about the performance of products, and especially new products, is critical to rapidly delivering the most reliable product to market.
Caterpillar, for example, has built a telematics solution that allows its customers to monitor their fleet in real time, identify maintenance needs swiftly, and replace parts fast. It collects a vast amount of real-time data from machines, which proves invaluable in optimising resources as capacity, fuel and operators.
As the excitement continues to build among consumers around the IoT, industrial companies are quietly reinventing and reorganising themselves. Those that fail to realise the impact and extent of the IIoT risk being left behind.
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