Nikunj Mehta – CEO of Falkonry explains Manufacturing Analytics

Nikunj Mehta – CEO of Falkonry explains Manufacturing Analytics

We had a chat with Nikunj Mehta, founder and CEO at Falkonry, discussing their primary areas of focus, industry landscape, their unique value proposition, cost and duration of deployment of manufacturing analytics systems. Below you can find our podcast edited for clarity and brevity.

CEM:

Hi Nikunj, thank you very much for your time. I wanted to learn a bit more about how you view the industry and unique value proposition of Falkonry.

 

NIKUNJ:

Thanks for asking, Falkonry is pretty much focused on industrial predictive analytics and this is is a field that has gained prominence in the last, I would say, two years, but we’ve been doing this now for five years at Falkonry. The reason we are doing it is because of the strong understanding of the industrial sector and its need to improve on reliability, productivity, safety, as well as efficiency. What we saw was an industrial and analytical state which was predominantly dependent on highly trained people who can complete manual analysis one at a time. Now I won’t blame the people for this it is also a result of the lack of investment in technology in the industrial sector in general. But then companies like GE, Schneider Electric and the German governmental institutions made a lot of noise about the need for better data analytics technologies and greater software investments. I think the whole world has sat up and taken a look at what is about to come. I think this picture that was put together by AGC partners. Actually, does a pretty good job explaining what is the spectrum of analytical technology that should be considered by customers. So, first of all, think about manual analysis as people putting together either MATLAB or Excel spreadsheets to solve individual problems that get reported to them. Most companies that are operating at scale have professional manufacturing or process engineers whose job is analysis and they solve individual problems. They put together calculations based on their knowledge of the area or of their own systems design. They try to solve those very specific problems. Therefore, they are very intricately familiar with the systems that they work with. It works for some problems but typically the cost of solving any such problem can be in the million to two million dollar range. So, naturally you cannot do this for every problem because industrial world will present you hundreds of thousands of problems.

 

In the context of oil and gas distillation columns and you’re trying to create an equation to determine what temperature you should perform the distillation at or what volume you should use for the given fluid you’re processing. In those kinds of situations, people have developed virtual physical twins where an engineer has constructed a productized version of an equation that can be parametrized through specific physical character criteria that are relevant to a specific application of that physical twin. Then they can operate that physical twin in very regulated conditions so that its results can be relied upon for operation. Now it’s a better model than the manual analysis because it is productized and therefore can be used in more than one place. But, it is limited to those situations where a governing law or a governing set of equations apply and you can control these systems’ behavior in a very tight manner. We’ve seen these in oil and gas pretty extensively especially in the upstream processing. Now this is also quite common in some areas that, for example, are operating turbines for power generation and hence have very tight control parameters. Companies like GE, have made a pretty big deal out of those twins that they are offering because of the decades of experience they’ve accumulated and the number of models that they have developed over the last twenty-thirty years. And it’s a great model because it actually gives operators the degree of confidence to operate on the basis of manufacturer provided models. So it’s great. The problem is that it requires those tight controls of operation. And that is not possible for at least these days sixty to seventy-five percent of the systems that are being operated. The world is changing too fast for that. I’ll give you one simple example; turbines like industrial gas turbines may have been designed for base load and are now being used for peak generation. So therefore their operational state is going to be very different from how these turbines have been designed. And therefore there may be no great physical twins available for industrial gas turbines when they are operated at peak power generation.

 

So therefore now people have realized that a physical twin is not going to be enough and also that physical twins are also expensive because they have to be created by the manufacturer. So, for these two reasons now people have realized that they need to look at the virtual statistical twin. Now there are some companies, especially most startups that have come about in the last five years or so that have made a business out of creating virtual statistical twins. It requires data scientists to find the right kind of data to collect to create the right kind of data science pipelines and to put together whatever knowledge they can glean about the machines operation to figure out what is the right statistical twin which is basically an empirical model built from data to predict or evaluate the condition of a system. It requires a large staff of data scientists and some companies are able to do that for very specific problems. One example that I’ve seen is that there is a company that put together a patch to go on your heart so that it can continuously measure electrical pulses traveling through your body primarily close to your heart and from that it can analyze the heart behavior into one of twelve possible classes of arrhythmia or perhaps even a breathing congestion problem etc. Now that is a virtual statistical twin. It was put together based on specialized data being collected by data scientists for a very specific problem. Now that company that put together the patch has created a deep learning- based model that performs as well as or better than a cardiologist and so it’s a very valuable company but there is only one signal will being colleccted and that is the heart electrical field. It can only be used to do cardiological analysis of the heart. Therefore you can do these kinds of virtual statistical twins where the potential payoff is very very large and when you have the organizational brand to be able to attract the best people to put together that virtual statistical twin.

 

That’s a problem because most companies are still industrial companies, they can not attract these kinds of people and their systems are very very complex and so explaining to somebody else what is the design of your system is very hard. People give up and this is happened to many of our customers. That’s where the full loading automation comes in. That’s a world in which software is able to recognize patterns from the sensor data automatically but it does not require that sensors be designed in a particular way. It also does not require that the data be collected in any predefined kind of a way. There is a good amount of responsibility division between the designers and operators of systems and the designers of the software that provides full learning automation. In this category the software can discover patterns from the data without too much guidance and therefore it does not require data scientists to begin with. Secondly, operators are able to put in their knowledge in the form of labels that they are able to fix over time and in a very sort of unmanaged sort of a way because you cannot manage the producing label for every moment in time. Now this kind of software can be deployed to any kind of a problem and therefore the majority of problems that we still have not solved will be immediately solved using the full learning automation software. It’s also a technology that creates incentives for existing staff within organizations to solve problems because that’s their job and ultimately they are the ones who are solving the problem but this approach of full learning automation is the basis for creating a scalable industrial predictive analytics practice within large organizations. We think about this as Six Sigma or lean manufacturing which did not get introduced by bringing in a huge team of consultants from an Accenture or PwC or an IBM Team. There was a small team of people from the Six Sigma coaching firm or the lean coaching firms. People visited their colleagues and peers from other companies and shared their experiences with each other to then become good at it across their entire companies and across their industries. We believe that full learning automation will be the same thing for industrial predictive analytics. Now of course the goal for many of our customers is to get to autonomous control and they know quite well that autonomous control may be possible in some of their business needs but for others they will still require a human in the loop. They are actually fine with it because full learning automation moves the needle way beyond their current state of practices. Therefore it creates huge economic benefits for them. So, this was a long winded answer but it addressed what you’re trying to understand about analytics in the industrial world.

 

CEM:

Great! If you can also tell us a bit more about the financial and time aspects of rolling out such a system. How long does it take to roll out an implementation and how it works on pricing?

 

NIKUNJ

Sure. Falkonry’s approach to its pricing is to give customers complete control over how much data, at what rate, and across what amount of computation they want to use. That is because in the early stages of a technology, you want to get the best possible results at the lowest computational and transmission cost. And only once you can prove to yourself that there is value in the analytics does it make sense to take into account what is the cost of the analytics itself. So, in other places people have charged based on the number of streams of data being collected or the volume of data being produced or even the number of users that are using it or the number of CPUs on which work is being done. So, we believe that all of these are incorrect for an analytical product, especially during the early days of that technology wave. On the other hand, all of our customers are aware that they need to solve problems. Problems that in their mind may be causing reduced availability or longer time to resolution, poor quality, whatever it might be. And so, each one of those problems, they have a sense of what impact can be created if the problem can be solved. So, what Falkonry has done is it offers a uniform pricing structure that is based on the number of entities, the number of signals, and the number of predictions that Falkonry is creating on a continuous basis. Remember, Falkonry is designing these models for real-time predictions. Therefore the predictions are not of one-off kind of a thing they are happening continuously. We make it easy for our customers by simply banding the signals into buckets of 25. Thereby, you don’t have to count for every signal. You just have to divide use your users between the low signal count or the high signal count. That gives you freedom to choose how many signals you want to use for solving any problem. Similarly you can use Falkonry to solve a problem on a custom designed system but you can also use Falkonry for a distributed fleet of assets that are all designed similarly. Now this approach is comparable to saying how many data scientists and how many software engineers and how many IT people do I need. On a per problem basis you’re going to spend probably half a million dollars to a million dollars a year for that type of a solution. So Falkonry’s pricing is going to be a fraction of that, plus of course the time that your own process engineers or manufacturing engineers are putting in, therefore, it presents a more attractive value proposition that allows the organization to keep more value for itself without diverging the benefits to Falkonry and having to negotiate a performance criteria on an annual basis etc. So the pricing approach we have developed allows both parties, Falkonry as well as Falkonry’s customers, to feel confident about the future to not be vulnerable to intellectual property or other proprietary trade secrets that we have to manage ourselves. And that is one of the unique things that Falkonry has navigated successfully in the growth of its business.

 

CEM:

Clear, thank you very much indeed. Can you also give a real-life example? Maybe from an industry that you are most experienced in. What would be the annual costs of working with Falkonry?

 

NIKUNJ:

So, every problem or need that  Falkonry solves is typically a million dollars or more per year and the costs of operationalizing Falkonry in the context of that kind of a problem or use case is going to be in the low six digits. So, typically within a year you get your payback and you are spending on a recurring basis a very small fraction of the value you are creating. Because of volume pricing because a lot of customers try to solve many problems at once they are able to get an even better leverage.

 

CEM:

Can you also elaborate a bit on the time to set up? I know it depends a lot on the data integration issues and so on but I also know that you have you have tackled these problems before so if you give an idea about the average case.

 

NIKUNJ

Sure, first of all, most benefits are visible in the analytics and people have been collecting

data some way or the other. So, what we suggest to people is to validate that the analytics produces value without bothering to invest in data integration. So that way, they are able to develop a good understanding of the benefits within two to three weeks from the existing data collection practices they have in place. In some cases where they are customers of one of our partners, we have a data integration offering available to them and they can avoid any long-term costs of data integration. Now, once they have validated the value in the analytics then they have to start figuring out what data they want to bring in and what the systems of record of that data are and how to integrate it with Falkonry. Falkonry worked, for example with one customer who was bringing together ERP, manufacturing execution system, as well as machine operational and sensor data. There were three different systems that were being used whose data was to be pulled together for pattern analysis and predictive analytics. In that case, the customer worked with a vendor of their manufacturing execution system, this was a company called Vegam Solutions. Vegam Solutions built an extension to their manufacturing execution system that used Falkonry client development kit and within a matter of two months they connected all of those systems to Falkonry, so that a user of the manufacturing execution system could very easily select what problem they wanted to solve and the data related to it and bring it into Falkonry so that all of the pattern recognition could be done on it. They, as a user did not need to know how  complex this integration was or change the way they work from one problem to another.

 

CEM:

Just to understand what you mean by starting with the analytics without the data federation or without the full data integration. You mean you start with analyzing their available data so your model is available from day one, for predictions or did I misunderstand?

 

NIKUNJ

That’s correct. So, most of our customers are already collecting data and would like to have a model on day one. Now that does not cover everybody. So, for some customers they do not have historical data and they want to start collecting data and analyze it. To answer your question specifically, our customers want a model on day one and they are happy to use historical data that they have collected for that.

 

CEM:

This is a great solution for relatively large entities we discussed before in manufacturing but is there also going to be a lighter version for much smaller entities?

 

NIKUNJ:

So, there are simple solutions that are available for two types of situations. One, where the data patterns are not complex and you’re working with one or two parameters. So, we try to stay away from that space, because there are other technologies like rule-based systems or streaming analytics that are well suited to those kinds of problems and where the costs are very very low because of strong open source offerings. So, we try not to indulge ourselves into working in those markets. Those are also areas that are less dependent on subject matter experts and can be codified directly by software engineers. So, there is no data scientist needed in that context either. There is also another category where there are certain known patterns that occur in one or at most two variables that people are trying to keep an eye on, on a continuous basis. That’s also a problem area at Falkonry could be used for but there are other alternatives that our customers often use and they use it while they are also our customers. So, we see that such complementarity is beneficial to the market. So, we are happy to support our customers who want to use both solutions. Now, Falkonry is by itself not a very expensive solution. You know, the starting cost on an annual basis are not very high, considering the value that somebody will be creating using that kind of technology. And we certainly are open to working with governments as well as with educational institutions to make available to them some of these technologies for public benefit as well as for educational benefit. In those kinds of situations, profit making is not our our objective.

 

CEM:

Vey clear. This is not that much business-related but I wonder where the name Falkonry comes from.

 

NIKUNJ:

That’s a very nice question especially because this is also a topic which is very dear to my heart. I know a lot of people all around the world have heard about falconry in their own languages because there has been a relation created between a human and a raptor bird, in many many parts and many cultures around the world. Falkonry derives from that original concept of training, a falcon to hunt for the benefit of a human. And so, that’s where the world Falkonry comes from. The reason I picked that name is because Falkonry in terms of its analytics, is just as powerful as fast and as sharp as the bird falcon is when it is going out and hunting. It’s able to figure things out on its own. You just have to train it to attend to one type of problem and not another type of problem. Falconry is also, if you look at the word origin, a place where you train the falcon, in that same way Falkonry software is where you create these analytical falcons and then you set them free to go hunt problems in the wild meaning on real-time basis. So you know falconer is the person who is doing that type of setup, in our case that’s a subject matter expert and so Falkonry, Falkoner and Falkons are the terminological basis for what we are doing in our business.

 

I’ll say one more thing: In our world of industrial work, falconry and falcons are a strong message to our customers that it is industrial grade, it is the best of the best and therefore it creates a strong brand perception. Our customers ask all the time, how did you come up with this name, it sounds really cool and we generally receive very favorable feedback for this.

 

CEM:

This is a really nice name for the business, then. I have also spoken with vendors in robotic process automation landscape. We were talking with the WorkFusion leadership team and I really appreciate their free as in beer offering. I am just thinking five years down the road maybe, but even smaller companies will need to deal with more complex manufacturing processes. Would that be something that you think industry could turn into to or you think this will remain in the domain of large manufacturing entities that are ready to pay six figures digit fees.

 

NIKUNJ:

We think that Falkonry will build a bridge between both the small entities and the large entities. While our focus is currently on large entities, we have served smaller companies in 2015, as well as in 2016. Our focus right now on large companies is only so we can gain some scale while the industry understands how patterns can benefit them. So our objective is not to only have large customers, especially because the industrial world is going through so much change that many companies that are very small today are poised to grow very rapidly. So, Falkonry will also be meaningful for a lot of smaller customers that have a clear understanding of the problems they are trying to solve and the business value they are trying to create. So we are certainly desirous of working with small companies when they are clear that they can benefit from working with a vendor like us than to try and spend all of their core competencies on coming up with a custom solution for their own pattern recognition needs. We’ve come across such startups ourselves. One is in the agricultural context that is managing irrigation of crops based on soil and weather conditions. Another is doing railway transportation analytics for equipment in the distributed rail system and both of them came to the conclusion that it was just not worth it for them to hire and retain data scientists to solve so many little problems that come in their world because it would take them too much time and would require constant tweaking because their world is always changing. So, those are the kinds of situations where we think startups can benefit from this type of prepackaged technology and they can pay as they go just like they pay as they go for the cloud for example.

 

Naturally, Falkonry can work out a pricing scheme for start-ups where they take a lot more responsibility for the support they would require from us but also, because they are far more nimble and technically competent group of people, we expect to be doing less hand-holding as well.

 

CEM:

Thank you very much indeed. If there are any final comments you have, love to hear them.

 

NIKUNJ:

So, Falkonry’s primary benefit is to the industrial practitioner but the immediate indirect benefit is to the industrial world as a whole. As we know 40 percent of the global economy is industrial activity. So therefore we believe that by making that sector of our economy, our global economy more efficient we are actually reducing the dependence on natural resources. We are also making the industrial engineer a lot more effective and therefore providing opportunities for engineers all around the world who may not have access to the best data scientists and the best manufacturing design know-how to be able to do a good job operationally. So just in the same way that countries in Asia became extremely good at manufacturing even though they did not invent all of the manufacturing equipment themselves, we believe that Falkonry-like technologies will help them become extremely good operationally using data even if they themselves do not come up with the analytical techniques themselves. So both from a developing world perspective but also from a global economy perspective Falkonry is going to have a substantial impact to improve the natural resources and sustainability as well as the livelihood of people all over the world who are involved in industrial activity.

 


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