What’s the ROI? Is the reward worth the effort? Is the juice worth the squeeze? Pick your favorite idiom, but they all say the same thing — is what you’re striving for worth the required expenditures to achieve your goal? THAT, my dear friends, is the name-of-the-game when it comes to IoT. Before organizations will invest the capital and human resources to deploy thousands or millions of devices all over the globe, collect and analyze floods of sensor/device data, and automate a business process, the business will want to know the ROI.
In it’s simplest terms, the case can be written as a mathematical statement:
OK, it may seem a bit obvious, but that equation MUST be true when architecting IoT solutions. Otherwise, what’s the point? If the cost of building and maintaining the solution exceeds any potential business value, then why build it?
OK, if you buy that, then let’s try to break this down even further. But before I do, let me make one caveat. Any time you build a cost model, you must make some assumptions. Whether its employee overhead, taxes, cost of a physical part, or whatever, — at some point — you’re going to take an average… or make an educated guess about some of the numbers in the model. Hence, your cost model is never going to be 100% accurate. Never. Building a precise cost model is not only impractical (in terms of time/effort) it’s nearly impossible. There are too many unknowns. So always bear in mind — the point of a cost model is to get a reasonable idea of any solution’s potential cost. That way, you can compare it to the business value (hence the formula above).
In this blog post series, I too, will make some assumptions. My hope is that you can use this methodology to build your own cost model….not to use my exact example. Make sense?
OK, let’s get started. I’d like to start with the CAPEX part (because I think it’s the easiest). The CAPEX for an IoT Solution (at the edge) starts to look something like this:
- Cost of every device at a specific location, or [N*DevCost]Location1 , where N is the number of devices
- Plus the cost of power, networking, housing/physical security at that location, or [N*DevCost + Pwr + Network + Housing]Location1
- Now, if every location has different devices, the formula could expand to , [ (N*DevCost + Pwr + Network + Housing)device1 +..+ (N*DevCost + Pwr + Network + Housing)deviceX ]Location1, Where X is the number of disparate devices in each location.
- Plus that same formula for all locations, [ (N*DevCost + Pwr + Network + Housing)device1 +..+ (N*DevCost + Pwr + Network + Housing)deviceX ]Location1 + …+ [(N*DevCost + Pwr + Network + Housing)device1 +..+ (N*DevCost + Pwr + Network + Housing)deviceX ]LocationY, Where Y is the number of locations
Follow me? In other words, you have to add up the cost of every type of device, plus the power, networking, housing/physical security for that device, at every location.
Now, here’s where it gets tricky. You’re probably wondering, well, how much power, networking, housing, is required for each device? Well, that’s device dependent, right? For example, if the device has 802.11 capabilities, then the device might be able to leverage an existing wireless network. Or if the device can be powered by a lithium battery, then AC power isn’t required, which alters the model.
And, btw, this is just the capital costs for the device, we haven’t even talked about the costs for the platform. I’ll get into more with my next post, but this is food for thought. And, I need your help. If you can think of other variables that add/subtract to the device cost, then please let me know.
For more information on IoT, please see Gartner’s document Preparing, Planning and Architecting for the Internet of Things
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