The Internet of Things vision can become a reality – but only if companies approach IoT strategically, tying all of this data from multiple core systems together. The hard reality is that most IoT projects don’t make it past the pilot stage, as they languish in isolation and drown in data collected but never analyzed or used.
Imagine this: Data generated from sensors attached to machines on factory floors worldwide enables a supply chain manager to remotely pinpoint potential problems. The manager can even use AR to take a closer look. Meanwhile, AI algorithms sift through current and gathered data to predict and automatically schedule machine maintenance and let the service tech know exactly which tools are needed, which significantly reduces downtime.
Additional sensors monitor the temperature and other parameters of the products throughout manufacturing, warehousing and delivery to verify that all factors meet contract specifications. And through integration with its manufacturing and supply chain systems, the company automatically receives the agreed-upon payment via a blockchain ledger system.
No manual reconciliations. No back and forth necessary. Customer expectations are met. Payments are timely and predictable.
“Everyone’s trying to get to the pot of gold filled with emerging technologies like machine learning, Industry 4.0, artificial intelligence, IoT and more,” said Rick Jewell, Oracle senior vice president of supply chain management applications development, at the Oracle OpenWorld conference. “We’re living in an exponentially accelerated time of technological change. And the capability gap between those companies that are using these technologies strategically and those that aren’t is only going to widen.”
That’s why it’s important to focus on building cloud services for IoT (and other emerging technologies) that are tailored to specific business use cases such as asset monitor, production, vehicle fleets and customer service, as well as ensuring worker health and safety.
A large number of companies begin their IoT planning by focusing on the technical details of sensors, device management, protocols and connectivity, but starting with desired business outcomes in mind can then determine the needed KPIs, and consequently, what kinds of machine learning, analytics, rules and models are necessary to compute or predict those KPIs. Afterward, they can determine what data they need and where they can get that data – from manufacturing and customer-facing systems or specific devices.
Only at this point should companies focus on the technical details.
For example, a contract manufacturer of injection molding products could have started its IoT planning by focusing on improving customer service and reducing labor costs for monitoring robots and manually intervening in the production process. Today, it could be using an IoT asset monitoring cloud to get real-time information from sensors on industrial robots, and a mobile cloud service to extract information and send an alert to the appropriate engineer or line operator when a problem pops up. The engineers would now be focused on analyzing problems and designing products rather than reworking orders and responding to misdirected alarms. Isn’t that an improvement of the process and a better allocation of resources?
In its recently published top 10 list of strategic IoT technologies and trends, Gartner predicted that a variety of companies will move into adjacent businesses by buying, selling and manipulating their IoT data.
“What I’m seeing each and every day is that our customers aren’t playing in their vertical anymore,” says Beverly Rider, Hitachi Senior Vice President and Chief Commercial Officer. “Healthcare is looking at smart spaces. Smart spaces is looking at transportation. It’s all blending together to explore how they can use data and make it into a new way to view the entire world.”