Imagine a world where smart systems, Internet of Things (IoT) sensors, and robotics combine to automate large areas of manufacturing, linking wired and wireless networks throughout the world in the making of products, and relying on both structured and unstructured big data to get the job done. McKinsey & Company describes smartmanufacturing as a "type of information system--through sensors and actuators embedded in physical objects...[where] processes govern themselves, where smart products take corrective action to avoid damages and where individual parts are automatically replenished."
Realizing the potential of a total manufacturing transformation with use of IoT big data, Germany initiated its Industry 4.0 government initiative to spur its industrial sector. Dependent upon real-time big data for driving and making decisions in the factory, Industry 4.0 represents, according to pundits, the "fourth industrial revolution" -- following the steam engine, the conveyer belt, and the first phase of IT automation. In this world, multiple factories, logistics providers, etc., will interconnect with each other in a system of people-originated and IoT big data automation -- all controlled by a central system "back plane" that is capable of synchronizing and orchestrating all events throughout the supply chain and giving everyone involved full visibility of what is going on.
To execute the smart manufacturing vision, enterprise systems must be modified so they can interface with and monitor IoT sensor-based technology, along with a host of disparate manufacturing, logistics, procurement, order, and other systems that must be integrated into a single back plane system. From an IT perspective, the task can be daunting. From a vendor management perspective, there can also be looming challenges, as some vendors will be more prepared than others to participate in the effort.
Equally important will be the need to bring together the big data team with the standard data team, because for Industry 4.0-style manufacturing to work, both big and standard data must work together and be tightly integrated. This means getting both data teams engaged in a joint project so information flows can be architected that draw upon both standard and big data to drive the automation needed to run the factories. If factories are spread across different suppliers and geographies, there will also be a need to import that big and standard data architecture to others who are part of the manufacturing supply chain.
The good news is that industry standards are emerging for big and standard data interfaces that will facilitate smart manufacturing information flows.
In industries like food and beverage, sensors that generate machine-driven information and automatic alerts already are widely used to measure the temperature and the humidity of containers that food products are shipped in, and also to track shipments from their points of origin to their final shipping destinations.
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