— MS Partner Apps (@MSPartnerApps) May 21, 2019
We just finished our first experience with exhibiting at Field Service USA. It was a great experience and we met a lot of companies interested in our technology.
We showed a demo at our booth of one of the ways to use our AI. For an unconnected device, we install a special script which creates a dynamic QR code showing the status of the device at any point in time. We call this an “Issue Fingerprint”. When there’s a service event, the user selects a function on the device that displays the QR code, then scans the code with a mobile device and submits it to the AI in the cloud, which returns a root cause and solution to the mobile device. You can see a simple demo of how this works here.
Listen to RevTwo, Momenta, and Senseye discuss how Artificial Intelligence (AI) and Machine Learning technologies are accelerating the evolution of next-generation solutions in the industrial IoT sector. In our complimentary webinar, we explore the “state of the union” around AI – the investment climate and key developments in research and business applications. Watch the free recording!
I was talking to a customer last week about the workflows they use when a customer contacts the support organization for service.
The most common workflow I see is something like this: customer calls the help desk, the agent asks a bunch of questions, and if they can’t resolve the issue then the agent gets a specialist or an engineer involved. They may open a remote access session to connect to the device (IF it’s connected…) If the problem still can’t be resolved, then they dispatch a field service engineer to the customer. While onsite, the engineer either triages and resolves the issue on their own or they talk to their own help desk to help get things sorted out.
This particular customer said “we don’t necessarily do that. If the help desk can’t resolve the issue, then we ask the customer to send us a set of log files for us to analyze.” Then if they can identify an issue through the examination of these files, they dispatch the engineer.
Going through log files is time-consuming and complicated. You have to look at the files sent to you by the customer when there’s an issue and then you have to compare those files with other log files that represent a healthy machine.
Last week I attended this annual event where there were approximately 100 field service executives from medical device companies. The event was 2 ½ days long and included interactive sessions on everything from remote service to retaining service techs (a bigger issue than you’d think!).
From my perspective, these were the main IoT topics that dominated the event and my thoughts on them:
I read this article in IoT World about connected health trends to keep an eye on for 2019. It talks about use cases for AI such as wearables, patient dashboards, surgical robots. It talks about some VS guy who a few years ago said that AI can do 80% of what doctors can do and that radiologists would be obsolete by 2022!
The overall progress of AI adoption in healthcare has been really slow. It seems to me that so many companies are trying to develop AI solutions for applications and use cases that are just too complicated or rely on an ecosystem of other technologies to make them happen.
We need to stick to the basics. No tech project or initiative will be successful unless there’s a business case to drive it. And in healthcare, the obvious business case for AI starts where IoT did: service and support.
Companies trying to deploy an IoT technology so they can remotely support their devices in the field are certainly still facing challenges today. More and more of their end users, particularly overseas, are resisting their attempts to connect to their devices on their network because of security concerns. But the business case for remote service and support still is strong. Reducing downtime, reducing 2nd service calls, improving MTTR and FTF rates still translate right to the bottom line for OEM’s and their customers.
So you’re thinking about trying out RevTwo’s AI technology? We are currently working with several potential customers on proving out our technology for them and would love to work with you.
IDENTIFY KEY ISSUES
To get started, talk to your service and support teams and identify 3-5 common service/support use cases that usually result in having to send a technician onsite to resolve it.
Sometimes our customers may struggle to come up with the right issues they want to address. One way to get your team thinking is to consider issues with one of these characteristics:
-An issue that happens often
-Something that was expensive when it happened
-Something that everyone in the organization would know about
It also doesn’t have to be something cut and dried like “the water pump failed”. Most complex issues are not that straight forward and usually have multiple root causes and symptoms. For example, it could be “my system is running slowly”.
Complex products have complex problems!
Unfortunately, the only way to solve such problems today is with highly trained personnel and time. Current methods include:
1) On product data and event logging
2) Remote product access
3) Remote field tech support
4) IoT cloud connectivity
All with varying success and their own unique set of challenges. For example:
Product logs are useful but finding the right information in the right log can often be time-consuming and frustrating.
Remote Access, which provides skilled technicians with access to physical products, is becoming less and less available as the cybersecurity threats of today’s world multiply and the risks associated with a data breach grow. Even with access, technicians still face the challenge of identifying the right diagnostic information and using their expertise to parse its meaning.
Remote Field Technician support provides a more experienced product expert at the disposal of field technicians who have varying skill and training level, to aid in the troubleshooting process while onsite. While this helps to prevent site revisits, it increases the costs of already expensive support activity.
Walk-up data acquisition is built upon two pillars:
1) Physical proximity to the product, provided by the customer themselves or a field service technician.
2) Mobile invocation. A mobile support app is a very convenient vehicle for accessing data. It is easy to distribute, provides a high degree of security, and is easy to use. RevTwo uses mobile apps to provide walk-up data acquisition.
Walk-up data acquisition is ideal in situations where the physical product does not have access to the Internet, because it is in a secure environment, or because Internet access is not built into the product.
In a walk-up scenario, product data can be obtained in one of three ways.
Ad-hoc data access works by forming a transient connection to the product itself. The underlying communication technology for these transient connections is usually Wi-Fi or Bluetooth, but other mechanisms could be developed. The general process is to:
1) Identify the connection available for a particular product
2) Form a secure connection to the product
3) Request diagnostic information via that secure connection
4) Obtain that diagnostic information
5) Shut down the connection
6) Use the information to perform an AI analysis