IoT and Data Analytics at Manufacturing Innovation Summit 2018 : Hitachi Consulting’s director of consulting services – Andy Baker – explored at this year’s Manufacturing Innovation Summit how predictions via the Internet of Things and data analytics can help optimise manufacturers’ supply chains. Manufacturers are increasingly using insights derived from IoT and data analytics to transform their operations and drive positive changes in their business processes.
For example, equipment sensors, OEM components and IoT devices are gathering information at a phenomenal rate. Data management tools are helping provide real-time alerts, asset performance reports, servicing and audit information, as well as higher-level industrial control systems.
Baker explained that the advancements of inter-related and supportive technologies are driving new innovations, such as automation, additive manufacturing, robotics, IoT, cloud, machine learning, artificial intelligence and blockchain in both manufacturing and logistics.
Smart products are capable of tracking, monitoring, remote control, self-diagnostics and maintenance alerts, which, if implemented, could cut total equipment downtime by 50% and increase production by 20%.
The goals of smart manufacturing are: decrease defects and warranty claims, support additive manufacturing, decrease lot size, shorten time-to-market, balance demand and supply, improve customer experience, improve labour effectiveness, and improve resource consumption. These benefits can be achieved through the application of industrial IoT, predictive analytics, virtualisation and simulation, digital product modelling, collaborative robots, social media-based communication, and cloud.
Smart logistics, including product-as-a-service, digital supply chains and predictive modelling, could improve accuracy in sales planning forecasts, stock management and automated order processes.
“A Hitachi distribution centre increased productivity by 8% through dynamic scheduling of orders based on data aggregation, analytics and machine learning,” Baker commented.
“The Hitachi Omika Factory also improved lead-time by 50%, in a high-mix, make-to-order production operation, through dynamic scheduling. By aggregating customer order data and operational data sets, bottlenecks are predicted and reduced or avoided. As a result, the factory increased both productivity and flexibility. In both cases, the digital solution is an integral part of the Kaizen system,” he concluded.
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