Calculating Internet of Things ROI — important tool

Just came across this video while researching how to calculate ROI on Internet of Things investments for the e-book I’m writing, and felt compelled to share it.

That’s because it may be hard to calculate ROI fully and accurately for IoT investments if you aren’t thinking in terms of what my friend/patron Eric Bonabeau always pounds into my head: what can you do now that you couldn’t do before?

In the case of the IoT, there are  several things, such as “predictive maintenance,” that weren’t possible before and thus we don’t automatically think of calculating these benefits. It will require a conscious change in figuring ROI to account for them.

According to Axeda CMO Bill Zujewski, there are 6 levels of M2M/IoT implementation, and there are both cost savings and revenue enhancements as you move up the curve:

  1. Unconnected: this is where most firms are today. No M2M/IoT investments.
  2. Connected, pulling data for future use: No return yet.
  3. Service: the investment begins to pay off, primarily because of lower service costs.
    1. Cost reductions:
      1. fewer repair visits  Now that you’re harvesting real-time information about products’ condition, you may be able to optimize operating conditions remotely.
      2. first-time fix rate increases: Now you may know what the problem is before you leave, and can also take the proper replacement parts.
      3. reduced call length: You may know the problem in advance, rather than having to tinker once you’re there to discover it.
    2. Higher Revenues:
      1. Greater customer satisfaction. Customer doesn’t have to pay as much for repairs, down-time is reduced.
  4. Analyze: Putting data into BI and other analysis tools to get greater insights. For example, understand what are bad parts, when they’re failing.
    1. Cost reductions:
      1. fewer service visits: instead of monthly service you may be able to switch to quarterly.
      2. lowering returns
      3. improve product design
    2. Higher Revenues:
      1. Increase product up-time: due to better design and more effective maintenance, longer mean-time-to-failure.
  5. Data integration: begin to integrate data with business processes.
    1. Cost reductions:
      1. warrantees (especially for industrial equipment): fewer claims if you can monitor equipment’s operations, warn owner if they’re using it improperly.
      2. recalls: reduced.
    2. Higher revenues:
      1. pay-as-you-go leases: as we’ve discussed earlier, you may be able to increase revenues by leasing products based on how much the customer actually uses them (which you can now document), rather than selling them.
      2. increased sales of consumables: you’ll be able to know exactly when the customer needs them.
  6. Reinvent the customer experience: According to Zujewski, this is where you “put machine data into the end users’ hands” through a smartphone app, for example, that gives them access to the information.
    1. Cost reductions:
      1. reduced calls to call center: the end user will be able to initiate service and troubleshoot themselves.
    2. Higher revenues:
      1. increases sales: your product will be enhanced, leading to more successful sales calls. You also may be able to charge for some of the new data access services that make the product better.

Zujewski concludes by saying that all of these changes combine into 4 major benefits:

  1. world-class service
  2. business insights (such as better understanding of how your customers are using your products) from all the data and analysis
  3. improve business processes: integrating data allows you to improve the way you perform current processes
  4. highly-differentiated offering due to to the apps and information you can provide users. “You end up demo-ing your apps vs. just the machines”

I was really impressed with this presentation, and it makes sense to me as a framework for calculating ROI on Internet of Things investments (I want to think about other benefits of the IoT that were impossible before to see if there are any other factors that should also be calculated).

I’d be really interested in your reaction: is this a valid methodology? what other factors would you also include?

Agriculture and the Internet of Things

Posted on 21st October 2013 in agriculture, environmental, Internet of Things, M2M, management

I’m particularly interested in how very traditional businesses will make the transition to the Internet of Things (Exhibit A: see my post from last year about the incredible way the Union Pacific Railroad has been able to switch to “predictive maintenance” by stringing sensors all along its tracks).

What could be older, and more basic, than agriculture?

Lance Donny, the founder and ceo of On Farm Systems, gave an overview of the potential of the Internet of Things to radically increase productivity and cut costs in agriculture at last week’s GigaOM Mobilize conference, saying that agriculture is a “sleeping giant,” when it comes to real-time data.

At present, the industry is handicapped by the high cost of sensors — they can run hundreds of dollars apiece — and lack of infrastructure — there’s no wi-fi, and, more often than not, bandwidth on the farm is limited.

Despite that, On Farm and other firms in the field (ooh, bad pun) are already helping farmers, especially with the critical issue of managing water use. He said there are already 14 million “connected farms” in the US and Europe, and by 2020 there will be 70 million connected devices on farms (interest in the technology is also increasing in developing nations.

Donny mentioned that a big issue with really serving farmers’ needs is that since they’re operating in a moving tractor, “you can’t give them too much data,” but must pay a lot of attention to the user interface, and only give them limited amounts of actionable data.

This “precision agriculture” yields tremendous volumes of data, and one of the problems facing IT firms in agriculture is that there are no common platforms (On Farm uses ThinkWorx), so adding a new data source from another provider requires contacting them directly and then connect to their API.

He said that reducing water usage by pinpointing when it is needed and how much is the biggest challenge, pointing out that 70% of fresh water usage is for agriculture —  even a 5% reduction in use could have tremendous implications not only for farmers, but a world with inadequate water supplies.

Donny said that Monsanto’s recent purchase of Climate Corp., which underwrites weather insurance for farms, for nearly a billion dollars “started a data war” in agriculture. He said that the goal will be to combine enough real-time data so that farmers would have 90% or more accurate 5-day weather forecasts in order to manage water better.

If the IoT is emerging as a priority for as basic an industry as farming, can other mainstream businesses be far behind?

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