There is a long and fascinating discussion to be had about how the economics of many industrial sectors are evolving. The short version is hardware differentiation is no longer economically viable, margin is going to zero, and pivoting to sensor analytics and exploitation is broadly seen as the primary means of generating margin going forward. I've had this same conversation across several industrial sectors. Anything tangentially related to transportation (automotive, logistics, telematics, aviation, and all related supply chains) is a good example.

As to what these companies want to do with sensor data, it is often considerably more interesting than what people imagine. Many of the applications have an operational real-time or low-latency tempo. (I can't be too specific here.)

For my purposes, I put "medium-sized" on the order of $1B annual revenue. As to why a company would literally spend 10+% of its revenue on sensor data infrastructure, it is difficult to overstate the extent to which getting this right is viewed as near- to medium-term existential for these companies. The CFO has run the models and this is their best chance at survival.

Here is the interesting thing: to the extent they've been able to put this sensor data infrastructure in place, it has been successful at generating margin. If they could bend the infrastructure cost curve down a bit, most would spend even more on it. I've seen the financial models at several companies, there is a tremendous amount of money to be made in this transformation.

I just fail to see how sensor data gives you an edge or (ultimately) a higher margin.

Is this about optimizing things to a precision that is impossible when humans try to decide whether something is too much or too little of something?

They are leveraging their privileged hardware positions to enter adjacent high-value data markets that have little to do with their core business, from which they can generate considerable margin. Essentially, the hardware business becomes a loss leader for a sensor platform and data business. Many hardware companies are in a position to capture data models that would be difficult to acquire any other way, if they can stop thinking of themselves as hardware companies.

In the short-term, big tech companies can't replicate what is possible for these hardware companies. Longer term, I would expect these data sources to be commoditized as well.

Your comment has no examples of how these margins will be generated.

Medium-sized can be in the $10 million per year range.

Let's take plastic injection molding since it's such a good example of a really broken industry (there are a small number of excessively competent injection molders and a vast legion of incompetent ones).

You're shooting a part every couple of seconds (or faster), and that injection molding machine has lots of knobs to dial in. Temperature of incoming plastic pellets, water content of incoming plastic pellets, dye feed rate, plastic feed rate, mixing chamber temperature, feed screw motor load, initial injection pressure, plateau injection pressure, release injection pressure, actual pressure inside the mold, time spent cooling--I can go on and on and on.

Most injection molding problems generally get solved one way: increase injection time. It's fairly straightforward to adjust, isn't likely to make things go wrong, and the people on the line don't get paid to experiment. They've got 100K parts to shoot in 72 hours, and an hour lost is a thousand or so parts they're going to get yelled at for. Better to dial the time up 10% and take 79 hours rather than experimenting for 7 hours and not shooting or waste a bunch of plastic.

Of course, if this is your only hammer, you can see where this is going. Every single time something goes wrong, that mold gets another 10% added to its cycle time. And it never goes the other way without "A Pronouncement From God, Himself(tm)". Eventually, your entire business is running at 50% productivity because all the molds are shooting so slow and you think you need to build another factory when what you need to do it fix your molding times.

Now, back to sensors--the problem is that nobody with incentive has a way to identify AT THE TIME IT CAN BE DEBUGGED that "something is going wrong". Someone on the line dialing up the injection time should cause an immediate dump of ALL the data on that machine (probably a week or more+) up to an engineer who can go through it looking for anomalies. Even better would be for the machine to flag to an engineer any "likely anomalies" (an increase in incoming plastic pellet water content should get flagged, for example) so that they can be corrected before they affect the injection process and cause failures/wastage.

This is, of course, all predicated on logging an enormous amount of data and being able to run an analysis against it in almost real-time.

Handling this data is non-trivial.

Thanks for this. A long-ago summer job was running those machines to make things like mirror rims and taillights. I still have a couple of the fantastic blobs produced when changing over from one job to another.

One of the things that struck me then is how much the line workers were treated like furniture, when many of them were quite sharp. They took such pride in getting things done well and at speed, in continually improving. I really wish I could put that kind of data in the hands of a couple of the people who trained me. Just an app on their phones. Spending 40+ hours/week on a machine means you really get to know it. I'd love to see how many of them would get great first-pass analysis and remediation.

I don't understand why such a system needs to look at every data point. Are the failures so rare that you can't get away with sampling?

Usually the cause that requires high sampling is vibration. But that can be condensed with FFT stuff easily.

Honestly from working in plastics and sensor design for 15 years it usually boils down to engineers not willing to let go of information because they envisage potential future issues. Its easier to imagine problems in a meeting than to imagine and deliver solutions upfront before the problem ever happens. Also, a lack of care for the economics of doing such sampling.

That's not to say there is an easy fix. These same people are the ultimate end customer who have the final word on such engineering environments.

I'm in a small energy company and log 50k/s events for several million points (temperature, pressure, voltage, current, power, ...). And most of what was said is true for us but we dont pay millions per year to our vendor. Its not cheap though and will pay millions over years easy. Horizontal scaling is not what these databases do as mentioned (thinking of Wonderware, IP.21, Honeywell, PI, ...). I have some hope for AWS Timestream for cloud but still think price will be high and they only ingest near live data so nothing older than what fits in memory. Most of the open source like influx, timeseriesdb, prometheus, lack features I expect but they are getting closer.

Check out VictoriaMetrics, with 50k/s ingest rate you can just use one single core machine (300k/s/core).

Cant tell from website but most metric solutions do not do bad quality/status markers. This is fundamental to IoT data. Next most dont do time weighted averages correctly even timeseriesdb does this badly.