IoT is something I have been talking about a lot lately and am pretty excited about it. In fact, most people that get it and see its potential are excited about this new wave of disruption. However, when I speak with most people, there doesn’t seem to be a very strong understanding of what the IoT is and what its implications are. It’s very easy to write it off as another buzzword that gets analysts excited, but if you’re not learning you are missing out on earnings.

Many people ask “isn’t the IoT like that thing that lets your phone work out what the weather is going to be?” Well, that’s a very basic example; the short answer is “yes”, long answer is, “not really”.

While it is impossible to tell exactly, the estimate in 2015 was that there were 15 billion connected devices in the world. In 2018 that is expected to reach 23 billion and it’s predicted that will reach 36 billion by 2021. IBM just announced a computer smaller than a grain of salt which costs less than 10 cents with the power of those PCs we had back in the mid 90’s - way more than enough to run calculations on some pretty complicated sensors. With that kind of computer it’s hard to imagine a world where they are not being installed into almost everything.

Okay fantastic, but backup, what is the IoT and connected devices? IoT stands for the Internet of Things, many different devices talking to each other to give us better information, make better decisions and, in special circumstances, predict outcomes. A connected device is simply one of those devices sending that data in. It can be anything from your phone to an AC system, TV, fridge or more interestingly, installed plant & equipment, facilities, and infrastructure. It’s pretty much any asset you can think of.

While this is a huge disruption for all field service and contracting organisations, if you are in a business primarily focused on reactive or preventive servicing of facilities and equipment, you should be paying attention.

Just think, if you could use that information to recognise a problem with a customer’s machine before they even know, you can have a tech on the way before they have even realised there is a problem. Using machine learning to analyse all the data coming in from your customers’ machines, you can predict problems or performance issues and prevent them from ever happening. Forget spending 95% of your time looking at perfectly fine equipment on your planned preventive maintenance trips and 5% on high-value work when you do see problems. This technology is allowing service companies to flip that on its head while, at the same time, providing better data, service, and insights to your customers.

Forward-thinking organisations are already on the front line with this, looking at the IoT as a way to combine live information, machine learning and bring better customer service to generate massive revenue and drive down delivery costs. These insights are giving companies the ability to learn more about how their customers are using equipment and facilities to help them provide better customer service, at a lower cost, with better information.

How to best start thinking and preparing for this change.

Step 1 - Decide, what are you trying to address / what do we really struggle with at the moment?

  • This is important to think about from the outset in order to make sure that you employ the most appropriate strategy to tackle what is actually important.

  • Think about what questions we most struggle with within our business or to answer for our customers (or specifically how we could improve our service to our customers).

  • What kind of data will we need to answer those questions, help give us that insight or to specifically improve that customer service issue?

  • Where can we get that data from?

  • Who specifically should we be feeding that information to. Is it data that only the system needs to know about to help make decisions, is it something that our internal staff need to know about or is it data that our customers are always asking about that we don’t have a good answer for or can’t get a hold of easily?

Step 2 - Think about how this can disrupt or improve your business model.

Like many other industries, moving to a software-as-a-service model has seen huge disruptions and benefits where the customer can subscribe for a service which could include live information, streaming, sharing and, of course, the physical inspections and work. If you are getting live data streamed into your simPRO system which is helping to predict failures, showing trends and live readings of what is actually happening, as well as being equipped with rules to automatically trigger different outcomes based on different events, could you perhaps change what you include in your monthly service invoice? For example, if you can more accurately predict issues and fix them before they cause any downtime, could you include an insurance or guarantee of availability? It’s been said for over a decade that “data is the new oil”, so is the data you are gathering and sharing valuable enough to warrant changing the monthly service fee with your customers to include that extra visibility? How is it going to be reported on? Do you need to make all those costly visits to check perfectly fine equipment?

Step 3 - Work out a plan to actually start getting that data.

So this may seem obvious, though you need to actually start receiving data from devices in order to get any actionable insight, apply machine learning and automated outcomes, and provide live data to the appropriate stakeholders. So think about where you need to get the data from and what kind of data. That will help to then decide which type of sensor can be installed or whether it’s potentially some equipment replacement that needs to happen.

Step 4 - Insight

Another buzzword? Actually, it’s not. Once we have data we can start making data-driven decisions. I cannot tell you the number of times my instinct has said one thing (which is important to listen to) but the data disagrees. Making data-driven decisions has to be a priority. We can then start using the data to drive the insights and see if we are answering those questions we started out with.

Step 5 - Implementation

There is no point in having all this shiny new data and decision-making ability if we aren’t putting it to use in our business. So with that, let’s now start looking at ways we can automate easily identifiable scenarios (like the temperature has fallen out of range for minutes, let’s raise a job or send someone a message), then start moving into higher value stuff. For example, Mr. Customer, did you know that we have noticed a humidity and vibration build up on your equipment that 95% of the time leads to a failure within 3 weeks? Would you like us to come and service that now for you or should we just replace it?

Also, by the way Mr. Customer, if you would like to see it, you can log onto our customer portal and take a look at the performance of your equipment that we are looking after. Let me just send you that username and password!

Step 6 - Rinse, repeat

Head back up to step one and tackle the next thing or improve on what you have already done!

We’ve said it many times, but continuous change management and improvement is the key to continuously improving your business, your bottom line, and your customer service. We are in an exponential period of change, the IoT is coming, and fast. Do you want to lead the pack here or will you be slow to adapt to change? Now is the time to start thinking about how you are going to embrace and prepare your business for the IoT.