Today, COVID-19 has proved to be a major disruption to global supply chains, and perhaps the human supply chain is most vulnerable. Engineering, Production and many other functional roles typically required to support plant operations now find themselves working from home and desperate to find ways to access advanced applications and other systems and continue to provide their expertise and guidance to keep manufacturing running safely and efficiently.
Today, COVID-19 has proved to be a major disruption to global supply chains, and perhaps the human supply chain is most vulnerable. Engineering, Production and many other functional roles typically required to support plant operations now find themselves working from home and desperate to find ways to access advanced applications and other systems and continue to provide their expertise and guidance to keep manufacturing running safely and efficiently.
Today, Covid-19 has proved to be a major disruption to global supply chains with perhaps the human supply chain being most vulnerable engineering production in many other functional roles, typically required to support plant operations. Now find themselves working from home and desperate to find ways to access advanced applications and other systems to continue to provide the expertise and guidance to keep manufacturing running safely and efficiently. I'm Peter Reynolds senior analyst and this is the digital transformation viewpoints podcast brought to you by arc advisory group. In each episode we'll ask the big questions of how industry is adopting emerging technologies and practices for sustainable manufacturing for better operational and engineering processes to blockchain. Today. However, our focus is advanced analytics for your remote workforce during the Corona virus outbreak. Here with me today is Michael Risse, vice president and chief marketing officer with Seeq corporation. I thank you for having me today, Peter. Oh Michael, thanks for being with me today. So asset owners, they tell us a lot. They have incredibly vast amounts of data collected and recorded and archived for various sources and many still they struggle to find meaning in the data. And provide insights and decision making within a reasonable amount of time. So within the context of digital transformation, they're looking to extract value from data that they already collect. Why do you think this is the case today?
Michael Risse:Well, first of all, what I hear customers refer to as drip data, rich information poor. So those are those asset owners with all that collected data and recorded from sources that you mentioned and they're struggling with getting the insights out so they're dripped, they're data rich, information poor. And the key challenge here is that data generation is ahead of analytics innovation or has been ahead of analytics innovation, right? If you ask most engineers what they use, most of the time they're using spreadsheets, it's 2020 spreadsheets came out first in 1985 very popular in the nineties that's 30 years ago. And yet at the same time you've got cheaper sensors, cheaper conductivity, you've got the cloud for storing it. You've got all this innovation on the data generation side and you don't have the equivalent innovation on the analytics and insights side. And that's setting up, or we already see it's a challenging situation as you said, but that's a situation that's only going to get worse with more data and as more sensors come online. Right?
Peter Reynolds:Yeah. You know, I often hear that described as inputs and outputs, the sensor collection, whether it be from P LCs or D CS is, or SCADA systems or even, you know, modern IOT, I OT collectors, you know, they're all inputs and until you start addressing it through analytics, it' s, t hat's the, t he output side or the out come si de. So it brings me to ano ther po int, m a ny of the industries, let's ta ke the energy and chemicals, i nd ustries and maybe the hybrid industries like pulp and paper here focusing on collections and gathering instrumentation data like pressure flow level temperature, maybe data quality or something like that. It seems like, you know, the focus has been on single variable single vari ant app roaches, but now we hear more about the emergence of things like big data and it seems data science has made an impact in i nd ustrial manufacturing through advanced analytics software. And I know process engineers have alw ays been good at analyzing this data and performing the basic statistical analysis. But it just seems like today it's much easier to connect. It's muc h easier to get data context and actionable insights quickly. So what do you think about this?
Michael Risse:Well, I think many customers would like it to be easier. Certainly that's our focus is Seeq as a software application company and we make advanced analytics and that's absolutely our goal, which is to deal with,, the reality of, of three requirements. The first reality is, as you mentioned, the big data and all the data, and it's already there and that's not going change. It's going get worse. The second thing is that the engineers, the process engineers have expertise, education, experience. Those are the people who know what they're looking for. Those are the people who have the ability and the need to find the insights to improve the outcome. Any conversation that doesn't start with the process engineers or do esn't include the process engineers is a nonstarter. We, you have to respect their position in the pla nt, t heir expertise, their abilities. And so that leads us from the data to the engineers to the third thing, which is something needs to change in the innovation space. The analytics applications that those engineers use. And I would differentiate or use an example. You know, as consumers we're, we have an incredible innovation. We can tap, we can use Google, we can use Uber, we can use Alexa, we can talk to our whatever, our phones or we can talk to our refrigerators. There's all of this innovation that's been captured by the consumer world. What can we bring in that data science, big data,, innovation into the industrial world to then enable these engineers to find those insights i n that data. So it's a three part story. The data, the engineers, they've g ot t o be in the equation. And then better applications and application experiences to find the insights more quickly.
Peter Reynolds:It seems like cloud computing is playing a bigger role here in helping companies developed analytics. And you know, even thinking today, you know, we're in the middle of the Covid-19 crisis and the current pandemic and, and companies that have invested in cloud services such as email, let's take office three 65 as a, as an example, companies that were already cloud enabled with their, their email, they packed up their resources. They shut down badges at to e nt er plants or o nl y essential workers were entering facilities, a just about every engineer on the planet, whether you're on a pro j ect team or you're on an operations team, they're all working from home. And when I speak to the companies that are, that are using cloud services to support the collaboration tools, they seem to be not missing a beat. And they're still in any, e ven some cases,, ce r t ai n industries their businesses up, you know, such as petrochemical and pulp and paper and some industries are certainly seeing some wins from what's going on. But wha t i n ter ms of, you know, big data and analytics has, is clou d changing this discussion in any way. What do you think?
Michael Risse:Well, I agree with your assessment on the cloud, you know, cloud arriving, cloud impact, but your example actually tells an interesting story, right? What were they, whose side of the house is it when they're enabling remote employee access? That's the it side of the house. The it side of the house has been ahead so far in cloud adoptions on the OT, on the manufacturing side of the house, what we see, we see a lot more cloud on roadmaps than we see an implementation. It is on every, most, every customer's roadmap, but it is more likely to be a conversation or a roadmap item than a, okay, we've already moved to the cloud, we've already got a data Lake, we've already got these things in place. So again, you know the consumers are ahead on the innovation side. It is ahead so far on the cloud side. Now that is changing in the roadmap. I certainly see a number of leading customers who are moving workloads to the cloud and it's really both sides of the equation. One, it's the data. Let's collect and assemble the data that we've gotten, these different plants. Let's do cross company or across plant comparisons, roll-ups, best practices. And then the second part is the data side. Let's take advantage of the elasticity, the rental model and the time to insight enabled by a cloud analytics place so you can get your analytics up and going tomorrow and have insights by the end of the week by connecting to the data, whether it's on prem or in the cloud. So a couple of different stories. There is obviously, one is the manufacturing side of the house seems to be lagging the IT side of the house and that implementation, we do see obvious opportunities both on the, on the data side and then on the analytics side to take advantage of cloud benefits and drive better outcomes.
Peter Reynolds:It's interesting and I think about, it's not just email and you know, we've seen cases where core systems like SAP a re moving to Amazon web services o r, Azure just because it's either either more secure and it's elastic, it's portable, it's got a licensing model that, that matches their business needs t o be right. That's on the it side of the house. But when you start thinking about manufacturing,, t echnologies and data that resides in plant, I think it's been a little lukewarm and in terest a nd using cloud solutions for manufacturing is Covid 19 really the push these day s, y ou think it has changed customer buying perceptions?
Michael Risse:It's moving something I will tell you that the acceleration of discussions, right? It used to be, well, we can get to this and it's a plus or minus and today it feels just like we're living in an extreme time. And whether it's oil and gas prices, which are extreme, or the working conditions where you've got people working from home, which is extreme, or you mentioned a couple of the industries where their sales and offerings are up. Like if you're, imagine you're a toilet paper manufacturer right now, you're probably pulling out all the stops to make as much as possible and recover from all the purchasing. So it's an extreme time and normal business practices are definitely being re-examined to adjust and accommodate the extreme time that we are in. Again, it could be while prices could be Covid could be a spike in demand. It just feels like there's a lot of re-examining of how things are working and what people have been doing and the speed at which the cloud has been adopted. That is quickly changing people's perceptions and people's actions.
Peter Reynolds:You know, I had a discussion with an executive,, recently, and this was in a hybrid industry. This i s one of the companies that had migrated and brought some of their applications to the cloud. So a t t he e nd was that, was that on the it side o f the OT side?, I guess this was, this would be probably both. Ok ay. Ri ght. Ye ah. So, they've got plant applications in the cloud as well. Bu t, but the interesting thing is we started to talk about a new way to work and, a nd I think that there's a thought process that's taking place where we' re a, t here's a workforce in tr ansition we don't know how long Covid 19 is going to go. You know, certainly it could be for a long time, but what I think companies are learning is they've, they've moved their employees to home offices. They're, they're using tools in there and they're getting the job done, but they're also not burning fuel in their car. There's a productivity improvement. So this might be, you know, something that's really, really positive, you know, going forward. But to move to this model, you know, a workforce in transition how are they, how the organization is going to be challenged because, you know, after all the engineers are not in the same place as the IT shops, right? Say you're an engineer, your email works and, and you and you need access to manufacturing data. What are some of the challenges that the engineers and your customers will face in getting access to data and how you think you can you would be able to help them overcome those challenges?
Michael Risse:Well, what's interesting is we've been working on Seeq for the last several years and there was just an approach in the company, a strategy, a philosophy of both being agile and modern. And there were a number of people who don't like the modern world because, you know, everybody's always talking about being modern in the fifties, sixties, seventies, eighties whatever. Part of the modern piece w ould look, this is going to be cloud ready. This w as going to be remote ready. This i s g oing t o be virtual ready. And so a number of aspects of our product, whether they're web based applications or whether there's real time collaboration between two people in different places, something you might see in Google docs a s an example. Or there's the ability to get to those underlying data sources in a secure way, you know, with it support for access to the data that most of the things we've been working on have a new set of urgency in this environment. Because if it's, if it's secret set up, then it's going to handle those challenges out of the box, it's set up to be promoted, set up to be virtual. It's set up for browsers, it's set up to access the data sources you need. It's set up for things like virtual shift handovers. I can create a document, create as a report available to you and you know, in a virtual model. So whoever's coming in or picking something up is going to be able to see exactly what, t hey need to because there's knowledge capture in there that again supports the idea that I don't see you but I pass to you and therefore what I did is more transparent to you and i t can be taken advantage of. So we've seen this i n part of this actually. Interesting. Y ou t alked about the organizational change. I mean what about the workforce change? The model that we were designing for with these expectations it's certainly because we thought COBIT was coming. We thought it was the millennials. We thought it was a new generation of employees that are going like, look, this is how I work and where I'm going to work. And yes, it needs to be done securely. Yes, i t admin needs to be locked down and yes, it needs to be appropriate. But there are a number of considerations for employee flexibility, employee distance, operating centers, for example, when major cities s o t hat t hey can look at, you know, data and how things are going and, and conduct best practices, you know, on remote locations or share those with t he remote locations or t he dirty distant and dangerous plants where a lot of these things happen. So we w anted to be modern, we wanted to be millennial ready and those challenges you described w ere both within our design principles,
Peter Reynolds:You bring up some good point and we've(workers) always been collaborative,, engineers and operators and maintenance and supply chain people. They all work together and t hey're, they're all centered around either, you know, one facility, it may be a remote center of excellence,, o r it could be at the plant. And I, and I think most of the collaboration, at least around the, you know, the re al t i me d ecision making, it still happens in a, in a pl an t oday, you take a, you know, a central control room or, or an operating center. So the engineers and the maintenance staff, they're just used to seeing, you know, the operator, you know, the operators and, in the charge of a production or a facility. And thi s co uld be a refinery or a c h emical plant or a, o r it could be a facility that makes beer. But that, tha t co llaboration channel has completely changed. And now engineers at home maintenance person is at home and so are the other roles. So do you see the tools helping to bridge those, those gaps in real time?
Michael Risse:Absolutely, and you know, for a lot of that expertise, I mean, again, typically it's conducted on, on spreadsheets. It's funny the journal petroleum engineering just had a recent article or blurb about, I think it was the CIO or CIO from BP who said, you know, you have to hide spreadsheets from him because he's like, no more of that. Right? All of that IP that's getting locked into that spreadsheet are held to that person because that's practically where a lot of things happen. And now it can be transparent. And now I can explain or one person can explain to another what they did and why they did it. And now the underlying assumptions can be presented and captured, right. For use by other organizations. And all of this push remote transparency in a way also ties to technical transparency of the underlying data analytics assumptions models. So it's a physical change maybe from working remotely. What's more interesting i s, is the IP change that's forcing the transparency o f okay, here's what people knew. O r people thought, O h that now needs to be expressed and reused.
Peter Reynolds:Interesting. Okay. So Michael, we're coming to the close of the podcast today. Do you have any last thoughts for the audience?
Michael Risse:Well, I guess one thing that we do here with respect to the cloud, and we talked about both the ingest side with data creation and the out the out-jest side with analytics and just encourage people to keep those two conversations separate, right? It moving data should not be a prerequisite to analytics and insights. There may be great reasons to move into your data, consolidate your data, but analytics can start tomorrow with your data, where it is and then support you as your data moves. Or if your data moves, whether it's to the cloud or to a central location. But there are immediate opportunities for analytics with your data, where it is in silos, historians, wherever it is that can really bring in a new experience with the data where it is, and then you can have your strategy for where the data will go, but it's something you can start immediately. I think that surprises people because there's so much language around, well, if you want this new insight, the first thing you need to do is something with the data and that's just not what we see in our successful customers. Your data's is fine, your analytics need some help. Your engineers are wonderful. They may be on prem or working remotely and you've got too much data. That's the summary. So let's get started.
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