We’re just about wrapping up a wonderful week at TSW in San Diego where we met with tons of Support practitioners and discussed with them numerous ways to help them transform their Support organizations into Profit Centers. Now, we’re getting prepped for LiveWorx – traditionally the biggest Conference for us every yeat
LiveWorx is big – both on account of our wonderful integration with PTC, as well as dues to the growing popularity of Analytics in the IoT world. The basics – we’re going to be in the IoT Plaza as a Gold Sponsor in booth E8. We will be displaying a true cutting-edge IoT application in the Cleantech industry – will have to stop here before risking stealing the event thunder. Just come by our booth and see it in action.
In addition to the booth, we will be presenting at a couple of panel discussions. Stay tuned for exact details. However, the basic premise – and synergies – stay the same. Data needs to be collected and transported to our platform in a secure way and ThingWorx does this seamlessly and without fuss. From there we ingest, parse, transform and analyze this data through our Analytics engine. Finally, we can expose this structured data to ThingWorx Machine Learning algorithms to unravel powerful predictive outcomes – which finally can be consumed inside our Rules Engine or simply be used to conduct Predictive Maintenance on connected products. Again, all on display at LiveWorx!
Tags: LiveWorx 2016
IIoT promises to transform the industrial landscape. Customers expect the industrial product usage experience to match the retail, such as the Apple iPhone or Amazon shopping. So, when customers engage with machines, they expect equally intuitive and seamless experiences.
For example, a doctor’s interaction with a medical device that helps conduct surgeries is expected be as seamless as using an iPhone. Or, take the case of drilling operations in the outback of an oil field, every machine the operator works with is expected to be customized to the wear and tear conditions analyzed, designed, and manufactured for a particular work area or situation. Made once and used by all is a relic now.
With those being the expectations, how close are we to realizing the future? These are the trends of what’s to come in the next few quarters.
Trend 1: IT and Operational Technology (OT) is Converging, IIoT Is Playing the Matchmaker
Convergence means industrial machines and IT can now communicate with each other and with us less acrimoniously. This means machines won’t break down and cause delays, and front line executives will be freer to pursue greater aspirations. For decades, OT has been a closed loop system. There was stiff resistance (and there continues to be) to open up the factory floor data to the internet world. This is partly triggered by security fears. That mindset is changing at a fast pace.
IIoT is propelling this convergence with the wide availability of low power Ethernet and wireless communication devices. And, the introduction of standard protocols such as IEC 60950 and IoT-specific operating systems such as RTOS and RIOT.
Trend 2: IIOT is Enabling Product Manufacturers to Generate a New Line of Revenue Streams
IIoT has made software-controlled machine configurations more pervasive today. This is giving manufacturers immense flexibility to customize their offerings. As a result, product managers at both large and medium scale industries are developing niche service solutions for specific markets without having to develop a separate or unique product for those markets.
For the longest time, we’ve heard of mass individualization of products but haven’t been able to realize the potential until now. With software-enabled controls and a monitoring ability in place, manufacturers are beginning to build custom service offerings and are creating brand new revenue streams that just did not exist before. For instance, OTIS Elevators can sell a predictive maintenance package with each elevator sale. OTIS isn’t just product manufacturer now; it in turn has a bigger role to play in making a building smart and energy-efficient. As a result, OTIS can create avenues for a wider role as well as a new support-based revenue stream.
Being closely involved in the customer’s day-to-day life makes the product manufacturer more visible. This gives companies the chance to build a stronger brand loyalty by responding to product breakdowns more quickly and efficiently.
Trend 3: Robot Repair and Maintenance Is a Nascent Industry in the Making
The reach and applicability of the industrial robotics systems is becoming commonplace on factory floors. Simultaneously, robots are being deployed in industries such as healthcare, defense, industrial warehouses, mining, and more. These new robots as service agents are taking on tasks that demand high levels of repetitive work, physically demanding terrains, and remote environments.
The global industrial robotics market is expected to reach USD 79.58 Billion by 2022. The industry is evolving to new opportunities for robotics companies, technology providers, and systems integrators. New roles are being created that is driving the need for more human talent to train, repair and monitor the growing robotic workforce. Apart from this development, talent for developing AI and specific robotic technology, including robotic operating systems, are leading to create new opportunities for the human kind.
Glassbeam is gearing up for Conference season – we are going to be attending two major events in Q2.
First, is TSW – the marquee event of TSIA – an organization that caters closely with the needs of Support professionals. Support (and Services), as you might know, is one of our key horizontals – our key messages of predictive maintenance, reducing MTTR, automatic ticket creation based on log files, root-cause analysis and more – directly address the needs of Support practitioners. Besides, we have a strong relationship with TSIA – having jointly authored a white paper and co-hosting a Webinar in the past. We were at TSW last year and were thrilled with the response! Which is why we signed up again – we will be in Booth #40 showing you the latest and greatest in our machine data analytics platform. If you prefer to meet 1:1, just send us an email to firstname.lastname@example.org and we will accommodate your request.
Bigger is LiveWorx 2016, where we are a Gold Sponsor. This is easily our biggest Event of the year – and is a reflection of all the cool things we are doing with PTC. At LiveWorx we are planning in showcasing a very interesting IoT use case for the smart grid industry – but saying more will definitely steal it’s thunder. Suffice to say – it will be demonstrating how mining unstructured log data (that is typically generated and transported) from IoT-connected devices. To get a glimpse into what we will be presenting, please plan on attending a Webinar – “The Next Frontier in IoT Analytics with Machine Date” that we are hosting with PTC next month.
Come join us!
I must admit – the impetus for this post is squarely another post written by Judith Platz at TSIA.
Judith writes about issues rampant in the Customer Service world. Rising costs, lower-to-flat revenues, increasingly entitled customers demanding better, faster and more personalized services – all inevitably leading to decreased CSAT. All this leading to ‘unsustainable’ customer spend – in an era where product revenues are often flat or declining – and therefore pressurizing Support organizations to do more with less.
Why this piqued our interest is that we address these issues head on in a paper we co-authored with noted TSIA Analyst, John Ragsdale, last year. We talked about these seemingly ubiquitous problems in support groups and how the Glassbeam solution – with it’s unique ability to transform even the most complex unstructured data – and the ability to glean valuable business trends embedded deep inside the data. Specific capabilities that are used by Support Groups today include:
Reduction in MTTR – Glassbeam’s Rules & Alerts Engine and Explorer coupled together act as both a Log repository and Knowledge Repository. Quickly scanning through support diagnostic files to call out known issues and provide guidance on best practices. Glassbem Explorer and it’s powerful search capabilities enables TAC engineers to easily drill down to the root cause of an issue as well as correlate multiple log files to determine cause and effect..
Proactive Support – When log/data collection is automated, our versatile platform can actively auto-detect previously identified patterns in log files and create tickets in your CRM. This not only helps your Support team become more proactive in their operations but also improves overall support operations as well as provides new support offerings resulting in revenue opportunities.
Empowerment of front-line Engineers – Using features like ‘Section Viewer and Knowledgebases, support engineers can easily and confidently resolve even complex Support issues – thereby reducing, or even eliminating, costly escalations to higher support tiers and/or Engineering groups.
Predictive Maintenance – By using our Rules engine, even the most complex anomalous conditions can be modeled, captured and acted upon. This Rules engine is further refined by state-of-the art machine learning algorithms.
Value-added services – Our ‘HealthCheck’ services help you create new revenue-generating services for your end-users by exposing important operational parameters like capacity, performance etc. These services increase both revenue and customer satisfaction and can act as a game-changer in your efforts to transform your Support organization from a cost center to a profit center. Incidentally, we held a Webinar by that very title – Transforming your Support organization from a cost center to a profit center – with, you guessed it, TSIA last year.
To summarize, customers continue to prefer self enablement solutions while internally organizations continue to push an ecosystem where you can create a 360 degree customer view. In my opinion neither of these are possible with out tapping into the logs collected at the device. After all there is very rich data on the devices waiting to be tapped into… Remember, Machines don’t lie!
We can help you transform your Support organization. Please email me at email@example.com and I’ll be glad to help you discover ways to achieve this goal.
Continuing our discussion on Edge Computing and Analytics ….. Remember we said that a key benefit of Edge was Local Decision Making. Typically, that will preclude access to the install base data. However, there is a wealth of information which can be gleaned from the install base data (such as machine learning output). It seems a shame to not be able to utilize that on the edge.
Lets take an example of predictive analysis based on a machine learning model. Lets say there is an IoT device (perhaps a medical device) which contains a servo motor (moving a catheter through the arteries). The motor has a finite life and is dependent on hours of “typical usage”. Lets take a simple case where “Typical usage” is dependent on real usage, location, temperature and humidity of the environment in which the machine is being used.
Clearly the Edge does not have the data for all the devices in the install base, but the core does. All the elements mentioned above are captured on the edge and sent to the core. The core also gets the historical servo motor replacement data from a CRM system.
Combining these data sources into a tool like Thingworx Machine Learning, a multivariant regression analysis model can be created which will map the remaining life to the predictor variables listed above. End of the day, all statistical models are represented in the form of an equation. The equation for a multivariant regression model is:
Y = m + n1X1 + n2X2 …….
Where Y is the “remaining life” and X1, X2 are the predictor variables. m is the intercept and n1, n2 etc are the slopes for each predictor variable. Based on historical data, computing the equation for any machine learning tool is a piece of cake. But wouldn’t it be nice if this equation was available on the edge, so that for each incoming observation you can locally decide how much life is remaining? How easy would it be to plan for replacements if you could do that?
Right, that’s where Glassbeam comes in. As I mentioned before, Glassbeam’s Rules Engine is a Message Bus based Streaming engine which applies the rules as data streams are processed. So, this is how we do it:
1. Define a rule which implements the equation derived by the machine learning algorithm
2. As data comes in, pass it to the FSM (Finite State Machine) applying the rule
3. If all conditions match, an alert will be triggered if the remaining life is less than a threshold.
We just implemented a decision taken by the core at the edge. This is part of Glassbeam’s feedback mechanism – an inherent component of Glassbeam’s Edge solution.