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How the IoT Is About to Transform ERP for Small and Midsized Manufacturers

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Data are increasing in business operations. Companies hear this from every vendor, customer, regulator, parent, child, and anyone else in their lives. It’s just true and accepted: The world is generating more data.
What to do with these data is a problem not many small and midsized manufacturers are dealing with right now. Most are ignoring the deluge and hoping some magical cloud service will come to tell them what to do with it all. A few are constructing data storage areas in their small server rooms. Even these comparatively forward-thinking manufacturers don’t have a great plan for using the data, however.
Where Do the Data Come From?
Most of the data manufacturers receive are from production machines or their own manufactured products. Sensors are built into almost every production machine today. This has been the case for a long time, but the difference now is that the data those sensors generate are immediately available externally rather than having to be downloaded periodically using a specialized device plugged into a port on the machine. These smart machines are designed to send their real-time sensor data somewhere. The other massive inflow of data is from manufacturers’ own products. Even products that don’t include smart sensors often have bar codes or radio-frequency identification chips that generate location and usage data.
Taken together, this information is a large part of the current data in the Internet of Things (IoT) that manufacturers have to think about.
Certainly, the IoT contains a lot of other data, including weather station data, customer locations data from mobile phones, and satellite sensor data. The small and midsized manufacturer isn’t yet to the point of using all the external data: It’s struggling just to know what to do with its own IoT data.
Analytics for the Future
A few small and midsized manufacturers storing these data have pilot programs or side experiments, with one or two engineers trying to make sense of what data really are being generated. That is the first step in understanding. The next step is to explore the data. Most data scientists spend a significant amount of time graphing, charting, and running basic statistics over much of the data coming in. Realistically, they must transform the machine-generated data into human-readable units or representations.
This preparatory process leads to the business doing something with the data to improve manufacturing, warehousing, distribution, customer relations, and (potentially) every department in the company. Analytics is where companies get value from their IoT data.
Understanding and Action


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