When I talk to people especially tech people about SensrTrx they always say “Oh, that’s like IoT for manufacturing” and then they start asking me if I have heard of this sensor or that IoT company. Then I get asked if Amazon, Microsoft, and Google are competitors. What is interesting about IoT today is that everyone is focused on data and sensors without context.

And sensor data is meaningless without CONTEXT.

Consider this example: You buy a fancy new energy monitoring sensor and put it on a mill or lathe in my manufacturing plant. You get some amazing graphs showing energy usage. Like the one below.


Looking at the graph you notice that you use more power from 1 to 4 every day. Great, but now you want to know why? And you ask the following questions:

  • What part are you running on that machine?
  • Who was operating it?
  • How many parts did we produce?
  • Did we have any quality issues?

How are you going to get that information? You can walk the plant floor and ask the operator and he might remember what happened last week. You can look in your ERP system to find out what parts should have been running on that machine. You can talk to the supervisor of that department.

Talking to all of these people you find out that they were running the same parts they do every day and nothing out of the ordinary was happening.

Ok, but there has to be a reason for the increase in power usage during that time. So now what? Now you go talk to maintenance and ask them why the power usage is spiking during that time period.  He says he did not get a call about anything for that machine during that time.

The only way you can find out what is happening is to stand by the machine and watch the operator. And you don’t have time to do that.

Context Makes Data Meaningful

What you need is CONTEXT around this event. You need a system, like SensrTrx, that augments the data and includes what you need to diagnose these issues. Answers to the questions above and more:

  • What was the load & rpm of the spindles?
  • Did we produce any scrap?
  • Were there any system alarms?
  • Did we change tools?
  • What shift was it?
  • Who was operating the machine?

You need all of this information to quickly and easily understand these issues without spending half your day talking to everyone in the factory about what happened and why.

This is a very simple case and many of the experienced manufacturing people reading this will know that the tool is probably wearing out during this time and it takes more power to make the cuts. Then the tool is changed and the power usage decreases again.

But if you think about issues with scrap, machine availability and why it is happening. Knowing the detailed state of your equipment, who was running it and when can help you spot trends and fix them. But without the context is this impossible.

To learn more about how to get context and actionable insights from your manufacturing data Request a Demo Today!

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