Things connected to the
Viewed through a different lens, IoT is a result of faster and cheaper Internet connectivity trying to meet our insatiable hunger for data. The underlying technology is not a complex one – college students rig up IoT proof of concepts as projects; electronics hobbyists can buy simple IoT kits online to play with.
The action is all in integration, data and automation. It is about what you do with the data. In a
Start simple, but scalable
No matter what the scale of your manufacturing setup is, or which sector it is, if there is a sensor, then it can be an IoT. In its simplest form, data is just made accessible remotely, to be analyzed for actions by a human, or in its more advanced state, an AI does it all.
The going after low hanging fruit approach works just fine here. The biggest and most immediate benefits can be realized by
connecting the most volatile part of the production process to any sort of a monitoring system (even something as simple as a web-accessible dashboard).
Say for example, your shop floor has a machine whose output is temperature dependent and is prone to reach higher than recommended temperatures frequently. Typically, an alarm would alert the operator, who in turn would have to intervene to not only address the cause of the issue, but also make sure those in charge of downstream processes expect a disruption in the assembly line.
Now enter a simple IoT device that transmits the temperature data in real-time across the businesses, making it visible to anyone with the right privileges. Even before the threshold temperature is reached, all stakeholders receive advance warning of a possible disruption, the incident logged, maintenance crew (possibly not on location) alerted, and so on. This is an example of just one device communicating a sensor’s value in real-time bringing operational efficiencies.
Scale brings intelligence, and analytics
Scalability, interoperability, are critical considerations to any IoT solution. Continuing from the previous example, imagine such data being collected from all locations where the same machinery is used. Add to this all related data (such as load, continuous hours of running, ambient temperature, parameters of the cooling system, etc.) also being collected across the floors.
Thus, the ability to extract actionable intelligence increases exponentially. It becomes easy to find root causes by matching pattern, simply because there’s all of the data available time-stamped. Upgrades or decisions on equipment purchase can be made on robust data.
Taking the nightmare out of logistics
One can argue that GPS tracking and sharing of vehicles’ location is one of the early and simpler adoptions of IoT. Just that it wasn’t called by that name. Quite literally, the vehicle (the
thing) is connected to the Internet to be able to share its data (its location gathered by a sensor, the GPS chip). Checkpoints in the supply chain, from trucks, right down to weigh bridges in warehouses being IoT enabled gives data that can feed a host of other applications.
Combine this material availability information with data on demand for different end products, data on assembly line capacity and any machinery with downtime, etc., and you can achieve production efficiency and optimizations that were only a dream a decade ago. Implemented well, all of this decisioning can be automated based on set parameters. When this IoT visibility is extended into vendors’ supply chain and processes, prediction ability transparency increases multi-fold.
With data comes AI
AI acting on massive amounts of real-time data is how the shop floor and manufacturing as we know it can be revolutionized. Even if a machine learning AI solution is out of reach for your operations, you should actively consider at least a rudimentary rule-based application to suggest actions based on the data streams your IoT environment is generating. Even a simple application can bring out intelligence that you wouldn’t have expected to be available from the shop floor.
While AI has made into manufacturing with the early adopters, the opportunity is immense across sectors to reap efficiency from making sense of what their shop floors are doing. AI can predict defects in production even as they may occur, or predict a premature equipment failure, and in either case, instruct other locations to ramp up production, accordingly modify logistics arrangement, and so on. All this, in real-time, without human intervention.
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