There are a few key industries that serve as pillars of the United States economy: transportation, agriculture and manufacturing. Moving things, growing things and making things. Each of these industries is poised to evolve enormously from new technology. Some have already begun adopting new and improved methodologies.
Those involved in industrial production have long been involved in installing sensors on factory equipment and taking measurements. Useful for predictive failure analysis, plant maintenance and safety issue mitigation, this production-based Internet of Things (IoT) technology has been called “machine to machine” communication or “M2M” for short. The concept of embedded technology is not new to the industrial sector, but the applications of such data gathering are still being fully realized.
Manufacturing was the catalyst for a global economic overhaul in the mid 16th and early 17th century. Industrial innovation allowed for the (semi) automation of previously manual tasks, increasing the efficiency of production exponentially. It’s no surprise, then, that new technologies such as sensors and networks are eagerly adopted by manufacturing to improve yields, reduce rework and produce more goods faster.
The new domain of IoT involves embedding sensors, software and connectivity directly into the products being manufactured. Here again, the results can be used for predictive failure analysis, maintenance and safety issues, but at the individual device level. Indeed, IoT can be used both in the device itself as well as in the factory where it is produced.
For example, imagine a well-known appliance manufacturer is able to determine, within seconds, the impending failure of a critical machine on the production line. They could dispatch workers to repair the device before it reaches critical failure, or schedule work off-shift to minimize the impact on the day’s output.
Similarly, imagine they are working to embed sensors in the more failure-prone elements of the appliances. By offering connectivity through home internet, products can link back to customer service agents who can, if necessary, proactively ship replacement parts and schedule service calls for appliances which may soon fail. Thus, the manufacturer is able to not only produce a superior product but to produce that product in a superior manner.
This is merely the tip of the iceberg. Since we know that IoT is all about data, the data collected from the production process as well as from devices in use by consumers can be very valuable if collected and analyzed. For instance, if appliances from a certain factory consistently failed in a predictable way, some analysis might reveal a way to improve the production process at the factory to reduce or eliminate the chances of failure after the appliance was shipped.
This feedback loop of data from the user to the maker is valuable enough alone that hundreds of manufacturing companies are implementing systems to collect and analyze data from shipped products and from factory production, all in the name of improved customer service.
Pressing that concept further, one can begin to wonder, “Who else would benefit from knowing about predictable failures in a product?” Certainly, that group would include those who own businesses that might be affected by the predictable failure modes. In our appliance example, bakers who used ovens produced by the manufacturer could avoid settings that would break the equipment, suspend their own production until repairs were made and schedule those repairs proactively so as to not reduce their own business results.
The concept of knowing, sharing and warning your own consumers about potential impacts to their own businesses as a result of live data collection and analysis quickly becomes a competitive differentiator: A baker who was able to maintain output thanks to advice and consultation with the maker of their ovens is far more likely to purchase new ovens from the same manufacturer over those who offer no such help.
Data is at its best and proves most valuable when it is collected and applied as part of a cohesive process. If data on the production of any given item can be leveraged to enhance that production cycle, combined with data on product performance also feeding into that same cycle, you will be able to create and deliver a superior experience.