The early promise of “Internet of Things” (IOT) offered refrigerators that would let us know when to buy more milk/ But the reality often failed the C3 test,(1) being more hype than help. However, with the growth of big data analysis IOT has come into its own.

Who is IOT for?

By its nature IOT is collecting data on the end user. For consumer goods this can be an invasion of privacy; for industrial goods this can be violation of production secrets. So the answer to “who is IOT for?” needs to be “for the customer.”

Predictive maintenance and IOT

I remember when I was 16 pulling into a friend’s driveway. His father who was in the garage looked up and said to me “your timing belt is going to break in about 5,000 miles.”(2) He knew it by the sound and he knew it by the data he had collected and analyzed. When a product ships, the designers know a subset of what they will know two, three, a dozen years in the future. IOT allows them to learn what the failure modes for the device are and then rollout those failure modes to the end user.


Model-Based Design needs to interface with IOT in two ways. What to upload and how to update. As part of the design process engineers now need to think about:

  • What data would be useful to improve performance?
  • What is the frequency of the data collection?
  • How much memory do I allocate for storing that data?

Put another way, your IOT strategy is the feeder to your DevOPs workflows.


  1. The C3 test, or “Chocolate Chip Cookie” test is when an operation is linear then a simple algorithm can determine when to order the next 1/2 gallon of milk. However, some things like getting chocolate chip cookies which require “milk for dunking” break the linear prediction (unless you have them all the time).
  2. He was off by about 300 miles.

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