Interconnected manufacturing systems and devices powered by the Internet of Things (IoT) is continuing to automate a broad swath of manufacturing activities, linking wired and wireless networks in the development of products to enable smart manufacturing where processes govern themselves and smart systems take corrective action when needed, according to TechRepublic.
As McKinsey & Company’s Markus Loffler notes in a discussion on the topic, the emergence of IoT could drive the fourth industrial revolution following the steam engine, the conveyor belt, and the first phase of IT automation. This stage represents “a new wave of technological changes that will decentralize production control and trigger a paradigm shift in manufacturing,” says Loffler.
While this raises the possibility for all components in the manufacturing supply chain to be interconnected with one another, it will also likely make logistics and the supplier network infinitely more complicated, says McKinsey’s Andreas Tschiesner. “Although lean manufacturing can certainly reduce inventories, manufacturers will need to coordinate with more and more suppliers – often globally, and with longer transport times, more manufacturing steps, and significantly more parties,” he adds.
Fortunately, the use of analytics with IoT data can help manufacturing leaders to better understand the interdependencies between systems, processes, and third parties and help to simplify these complexities. For instance, a plant floor manager for an electronics manufacturer wants to gain insights into the energy efficiency of systems and devices used in manufacturing, including pumps, thermostats, chillers, air-handling equipment, and other devices, including those operated by third-party assemblers.
Predictive analytics can enable manufacturers to mash up data from multiple sources into a single location, enabling the plant floor manager and other decision-makers to quickly identify deviations in the performance of manufacturing components. The plant floor manager can then drill down on the data to determine the root cause behind excess energy consumption for a particular device and then take corrective action that can prevent a costly breakdown while improving the energy efficiency of the production line.
