The Ultimate Convergence

One of the benefits of a part of Hitachi, a leader in Industrial IoT, is that you are exposed to and participate in a lot of work around next generational solutions that can bring significant impact to not just people's work, but more significantly their lives. Right now in the technology industry though, it can be a case of not seeing the forest for the trees, meaning that there is so much going on in individual areas, we may miss what it potentially means for the total solution.

I see this playing out in the image below;

I'm going to use a conceptual example of heavy machinery maintenance, to help explain the areas I have articulated in the solution. In fact let's use the example of the big Unit Rig trucks from my previous article talking about Hitachi's Lumada (https://www.dhirubhai.net/pulse/really-big-trucks-thing-called-lumada-matthew-hardman?trk=pulse_spock-articles).

Sarah the Mechanic

Enter Sarah the Mechanic, she works on ensuring that a mine's Unit Rigs are operational 24x7, any sort of downtime, for any period of time will mean massive loss in revenues for the company. Today she has one of the massive vehicles coming in as it had been reported that there has been some excessive vibration reported in the vehicle which could lead to a future unexpected impact.

As she approaches the vehicle in the workshop, her work phone vibrates indicating that it has recognized her proximity to the vehicle. [AUGMENTED REALITY]She slips her phone in to a set of display googles that lets her see the vehicle through the screen, upon doing so, the data from the vehicle tracking system overlays any areas she needs to pay particular attention to. [SENSOR DATA] The system alerts her on the display that the left front wheel is out of alignment, and [DATA ANALYTICS] what is more interesting is that this vehicle only received an alignment a month ago, and normal usage would not warrant early maintenance,

[BOTS] Sarah uses her voice to ask if there had been any significant impacts on the front left wheel since last check, and the system responds that in fact there was such an occasion and specifies the time and location it occurred. She then asks to see the event, and a video overlay pops up on the screen showing the video from the mine where she sees a replay of the collision, which seemed to occur on one of the new tracks the truck was following in the mine. With the data received she notifies the mine crew to check the track at the specific location and remove the offending rock.

The End...

Explaining the parts

As much as all of this seems like it's sci-fi, its absolutely achievable today, or at least the parts are, what it needs is for verticalized specialist to orchestrate the pieces together.

Augmented or Mixed Reality

Key to next generational solutions will be how people interact with them. What's more the best sort of interaction is one that is going to be hands-free and voice activated. If our hands are free there is more we can do be doing interacting with the physical world around us, rather than switching between input devices. Augmented reality is nothing new, its been around for a while, but its becoming much more of a reality with these industrial solutions so people can look at overlays of data to objects rather than changing their field of view between a screen and an object, it means they can get to a problem faster than ever before. The devices that can be used, need not be expensive devices either, we see companies like Microsoft and their Hololens devices which can be a great example of a head mounted high end experience, but we can also scale these back to phones inserted in to a Google Cardboard headset, these would make them much easier for companies to budget for and replace.

Data Analytics

This is a field that probably has the most focus for a lot of customers today, as they try to make sense of the data they have captured. While we think about data in the forms of rows and tables, increasingly data analytics sources will increasingly come from unstructured data feeds, videos, streams of data etc. and it will be critical to ensure that these data sources provide value to the applications and thus the people interacting with them. Understanding the metadata behind these unstructured data will yield massive insights and information for organizations, and with that organizations need to think more about their data storage strategies to handle the massive increases in the amount of data they leverage. Pentaho, one of our Hitachi companies is a key example of an analytics platform that can help customers take all of this structured and unstructured data and make some out of it.

Bots

Being able to talk to someone to ask questions and get answers is something that us human beings do on a regular basis, but what tends to happen is that we need to go back to a system to verify or validate the answer, or worse, there is no one there to ask the question to. Having a persistent automated presence will be critical to applications and the emergence of bots will help to address this. More and more we will see specific bots being created to address certain functions. This in itself could result in a challenge of having too many bots, and maybe just like humans, bots will develop reputations so that the bot you engage with is know for providing good information. In addition maybe we will see our own personal AI assistants talking to bots on our behalf to find the answer, something a long the lines of "Hey Siri, can you find someone who knows something about molecular biology?", and then they will be introduced to the conversation.

Sensors and IoT

The ultimate data collection agents will be sensors placed throughout our environment and the things we interact with. Some will have a single function use, and some will capture multiple streams of data. There have already been instances where Hitachi Chemicals used RFID tags painted on to the backs of bees to track location and environmental conditions to help analyze why populations rise and fall, so enabling sensors for data collection on large scale machinery will be a simple task, and as the costs to create them come down, it will be a simple task to get them on to as many things as possible, and the larger ones will be able to capture a huge array of data that can be analyzed.

There are probably many other areas that can be incorporated in to this list, Machine Learning would definitely be one which I think could be a part of the next gen solution as well. Its exciting to see each individual area develop and grow, but whats more exciting are the solutions that are being enabled because of it.

Let me know your thoughts on the technologies that help make the ultimate solution via comments below...