Manufacturing Innovation Summit 2018: The role of IoT & data analytics

Posted on 26 Jun 2018 by Jonny Williamson

In his keynote at this year’s Manufacturing Innovation Summit, Hitachi Consulting’s Andy Baker explored how predications via the Internet of Things and data analytics can help optimise manufacturers’ supply chains.

Andy Baker, director of consulting services (IoT & data analytics) at Hitachi Consulting, speaking at Manufacturing Innovation Summit 2018 – image courtesy of The Manufacturer.
Andy Baker, director of consulting services (IoT & data analytics) at Hitachi Consulting, speaking at Manufacturing Innovation Summit 2018 – image courtesy of The Manufacturer.

Following a packed day of roundtable conversations, Andy Baker – director of consulting services (IoT & data analytics) at Hitachi Consulting – took to the stage to deliver his Manufacturing Innovation Summit 2018 keynote.

According to Baker, manufacturers are increasingly using insights derived from IoT and data analytics to transform their operations and drive positive changes in their business processes.

Equipment sensors, OEM components and IoT devices are gathering information at a phenomenal rate. Data management tools are helping to provide real-time alerts, asset performance reports, servicing and audit information, and higher-level industrial control systems.

However, these capabilities are at the lower end of what’s possible. Applying data analytics, for example, can help generate operational status anomaly reports, greater operational insight asset control decisions, predictive maintenance schedules, optimised production lines, and improved distribution channels.

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Global trends

The advancements of inter-related and supportive technologies are driving new innovations in both manufacturing and logistics, Baker continued. He cited technology innovations including: automation, additive manufacturing, robotics, IoT, cloud, machine learning, artificial intelligence, and blockchain.

He also cited social innovations such as: as-a-service, connected, automated, shared, and co-creation.

The combination of these technology and social innovations is enabling a digital, connected world… increasingly populated by connected products and services.

Smart products – capable of tracking, monitoring, remote control, self-diagnostics and maintenance alerts.

Implementing predictive maintenance models with advanced analytics could cut total equipment downtime by 50% and increase productivity by 20%, Baker highlighted.

Smart manufacturing – to ‘enable timely decision-making through communication and continuous monitoring of machines, people and manufacturing processes’.

Smart manufacturing aims to: decrease defects and warranty claims; support additive manufacturing; decrease lot size; shorten time-to-market; balance demand and supply; improve the customer experience; improve labour effectiveness, and improve resource consumption.

A broad shopping list of benefits which can be largely achieved through the application of: industrial IoT with sensors and actuators; predictive analytics; virtualisation and simulation; digital product modelling (‘digital twins’); collaborative robots; social media-based communication, and cloud.

“Manufacturers have the opportunity to dramatically reduce costs through the systematic use of data and insights from production processes,” Baker added.

Smart logistics – including product-as-a-service, digital supply chains and predictive modelling. Data-driven supply chains can improve accuracy in sales planning forecasts, stock management and automated order processes.

“Business face several challenges around logistics and distribution,” Baker explained. “These can include changing customer demand; shorter lead times; complex supply chains; high transportation costs, and missed opportunities for generating more ‘revenue per delivery’.”

Much like smart manufacturing, these logistics challenges can be overcome by IoT and predictive analytics-driven monitoring of customer usage, automated invoicing, more accurate sales forecasts, truck location and delivery confirmation, order book modelling, and more efficient route planning.

“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.