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What Manufacturers Need to Know About Networks, Analytics, and IoT


IT departments at large manufacturing companies support more than just front-office communications. They also monitor and maintain machine-to-machine (M2M) connections. Integration between production machinery and enterprise resource planning (ERP) systems is critical, too, and it’s not just Operations that wants more accurate analytics. For smaller manufacturers, learning about the Internet of Things (IoT) could mean sampling two “flavors.” For their IT departments, there are multiple M2M factors to digest.
In a recent article for TechRepublic, Mary Shacklett writes that there are two types of IoT communications in manufacturing environments. Some companies use hard-wired, IP-based networks that interconnect machines and send information to the cloud. Others install Ethernet cables, or use wireless technologies such as Bluetooth to support localized communications between nearby machines on the factory floor. If your manufacturing company is thinking about M2M, which IoT flavor would taste better?
For networking professionals, the choice is more complex than choosing chocolate or vanilla. That’s why Shacklett suggests participating in corporate strategy sessions about Big Data. It’s also important to look beyond the local area networks (LANs) that IT departments directly control. Vendors don’t have exclusive control over M2M network typologies, but global telecommunications carriers and providers of cloud services still speak with authority. Listening to what they have to offer is the start of a successful implementation.
Shacklett also reminds IT leaders that achieving end-to-end network quality of service (QoS) probably means adjusting all of the original OEM QoS settings on vendor-supplied equipment. If your own IT personnel don’t have the time, skills, or training for this work, consider hiring a network QoS consultant. Finally, IT departments may need to hire vendors to enable analytics. Ultimately, satisfying top managers means giving them the manufacturing data that they wanted in the first place.
Image Credit: © tashatuvango/Dollar Photo Club


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