4 technologies that promise to turbocharge supply chains paid Oracle
are a natural fit. But many IoT projects fail because they’re focused “too much on internet and things, and too little on business outcomes,” said Harish Gaur, senior director of IoT and blockchain SaaS applications at Oracle.
The first step is to establish KPIs that determine the project’s business success. The next is defining the kind of data you need to collect for those KPIs, Gaur said, including real-time data from IoT sensors but also data from asset management, finance, manufacturing, and database systems. Two strategic elements of deploying IoT technology in supply chain management are the “digital thread” and “digital twin.” The digital thread is the connection of specific assets, data flows, and work processes that produce a business outcome, such as when unexpected downtime by an assembly robot triggers a recalculation of the production schedule. Digital threads map out how IoT data can be used effectively. “The data eventually has to drive workflows inside your enterprise applications,” Gaur said. The digital twin is a 3D recreation of a machine or business process. It provides a 360-degree view of the asset’s physical functioning and interactions with other entities. The digital twin is crucial to validating an IoT implementation by representing devices monitoring machine operations or steps in a supply chain. Physically installing IoT technology involves tactical decisions about the most appropriate and cost-effective hardware devices and communications gateways. An emerging factor is what’s known as edge computing—how much data processing can and should be done at the device level versus passing it off to applications in the cloud. Deploying and managing an IoT network as an integral part of a global supply chain presents unique challenges. Which assets and processes should be monitored—machines, robots, trucks, containers, items—and how many? Among the strategic issues to be addressed: “How do I scale from 10 to 10,000 devices?” Gaur said. “How do I plan for SLAs, whereby I need to deliver this information to my customers in a specific timeframe? How do I assure that nobody can peek into the data that’s coming from my devices?”Supply chains run on data, and incorporating an IoT network will up the amount of data quite a bit. Make the most of that tremendous influx by employing cutting-edge ML and data analytics applications and techniques, advised Bhagat Nainani, group vice president of IoT and blockchain app development at Oracle. Such “applied data science” will re-orient supply chain strategy “from simple reactive rules to automated decisions and predictions,” Nainani said. “And it’s much better to be proactive than reactive.” For instance, ML algorithms can detect deviations from normal behavior, helping supply chain engineers identify hidden problems and pinpoint them down to the misfiring machine or process. More data and more analysis make it possible to predict future states, like what are the chances that this elevator will stop working next week and which prescriptive actions can we take? This results in more accurate demand and operations forecasting, Nainani said.Augmented reality technology fits well with the IoT-enabled supply chain. An AR interface running on top of a 3D model, such as a digital twin, can display real-time inputs from cloud-based data analytics tools. It can speed up diagnostic procedures related to maintenance or quality control, among other functions. A variety of industries, including real estate and healthcare, already are using AR-enhanced applications. As for that capability gap, mobile AR applications are popular with a millennial workforce that’s comfortable with graphics-intensive technology .
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