30 minutes with Mika Perttula, Co-Founder & CEO - Fluid Intelligence

1. Can you provide us with an overview of Fluid Intelligence’s core services in the field of oil analysis, condition monitoring, and sensor technology, and how these services contribute to the clean-tech industry?

A: Our core service is Fluid Eye®, an AI-based services platform to optimize fluid, machine, and process performance. This is complemented by our expert team services. Fluid Eye® services comprise of the following components - Carbon analysis with simulation and reporting, Health Scoring based on lab data, Real-time monitoring for fluid health and quality, and Fluid Reconditioning products and services. All the services are powered by our proprietary Oil Data Management solution. Fluid Eye® delivers a holistic enterprise and multi-fleet view of the assets’ performance and carbon emissions with benchmarking. The platform also provides multi-organization services management, oil data management, and integration capabilities to our partners.

Combining Real-time Monitoring with lab-data-based Health Scoring, Carbon Life Cycle Analysis, and Optimization Advice the service generates powerful insights into existing fluids & machines performance. These insights are used to optimize fluid performance, maximize its lifetime, and in this way minimize CO2 emissions and costs.

2. What inspired you to enter the field of Heavy industry and industrial Fluid management?

A: Initially, our founding team met back in 2016. Our founders Mikko Oksanen and Viktor Laitinen had a long experience solving heavy industry customers' operational reliability problems and optimizing industrial fluids’ performance. They witnessed many times unnecessary oil changes, maintenance work, and failures that could have been prevented with a proactive maintenance strategy. Their experiences of wasted resources and strong industrial domain expertise were combined with our other co-founders' (Eero Juustila CTO & CDO and myself) digitalization expertise and this way Fluid Intelligence’s story got started and materialized as a Fluid Eye® service. We all share the same vision that industrial fluid performance management leads to notable sustainable and operational benefits and is an area where our actions directly contribute to heavy industry's Net Zero targets.

3. Fluid Intelligence aims to significantly reduce waste oil streams and enhance operational efficiency. Could you elaborate on this process which is supporting the sustainability goal while benefiting heavy industries and logistics?

A: That’s a great and very important question. It may sound complicated but is fairly straightforward at the end of the day. As my colleague, Mikko says many times, minor steps lead to major changes and gains. Behind the process is open-mindedness and willingness to act early. Fluid Way is based on three main steps – Monitor, Analyze, and Optimize.

First, it all starts with monitoring fluids and the machine's performance with real-time and lab data. Some considerations here: Quality of the data is key for every later analysis. With poor or partial data you get poor analysis. We’ve paid a lot of attention and effort to this and developed an oil data management platform with my colleague Eero’s lead to ensure high-quality analysis. Also, not all companies are digitally native and willing to take advantage of real-time monitoring. Still, we can extract a lot of information from existing lab data. Especially with larger assets we generate enterprise and fleet analysis that lets you see the forest for the trees and find the most problematic assets instantly. We can also benchmark a single component’s Health Score to industry peers and gain insights into how you can perform better. And all this can be done without real-time data or sensors. So, there are options for how to start fluid performance optimization. Second, analysis is performed in our Analysis Engine. We base our analysis on existing customer data and our proprietary reference databases. Analysis can be a direct decision tree type rules model, multi-variate analysis, or algorithm-based analysis. Most of the analyses are automated but for example in Carbon Lifecycle Analysis, you can simulate future options. Third, our Advice Engine generates Optimization Advice automatically based on the analysis. It delivers written root-cause analysis with action recommendations and follow-up tasks for work orders. By repeating this process constantly and acting timely we find ourselves in a position where fluids and machines start performing better and we can start maximizing fluids’ lifetimes. With this we’ve been able to achieve -80% fluid-based CO2 emission reductions with our customers lately.

4. The integration of AI and sensor technology is the key aspect of your Fluid Eye® industrial fluid asset management service. How does this combination enhance the accuracy and reliability of oil analysis and condition monitoring?

A: Compared to traditional oil analysis online monitoring delivers you a real-time heartbeat of the fluid and machine performance. It can be high-level indicative real-time measurement complementing lab analysis and a really good cost-effective option for many use cases. Alternatively, we can go as deep as real-time chemical parameters analysis. It’s always case-dependent and relates to your business case. What is the asset criticality, fluid volume, downtime frequency, maintenance cost, etc. Context awareness is another point. There’s a range of sensors measuring various parameters. Having knowledge and expertise of various industrial use cases, typical failure scenarios and so on it helps us to select the right sensor set for each case, and this way we increase the accuracy and reliability of our fluid asset analysis. Over time we have developed a strong oil data management model and reference databases. As AI analytics requires quality data this helps us to increase the accuracy and reliability of our analysis. We can identify anomalies very early in a cost-effective way.

5. Could you share a brief success story where Fluid Intelligence’s solutions led to a significant improvement in the client’s operational reliability or cost savings through effective oil analysis and condition monitoring?

A: I’d like to highlight our energy sector customer Vatajankosken Energia here. They have a gas engine in a remote location. Previously lab sampling interval was counted in weeks with a quaterly oil change interval. The process was costly due to frequent maintenance work and traveling to this remote location. Also, machine reliability was based on the lab-analysis information. In the beginning, we ran a detailed lab- and real-time data correlation analysis to set up a baseline for production performance monitoring. Since then, Fluid Eye® has been monitoring gas engine production 24/7 for several years. We’ve been able to triple the lifecycle of their oils, reduce costly lab sampling rounds, and improve operational reliability with real-time heartbeat of the engine's performance. This has led to over 60% CO2 emission reduction and notable cost savings.

6. Predictive maintenance relies heavily on real-time condition monitoring and AI-powered analysis. How does Fluid Intelligence's methodology improve maintenance plans, resulting in less downtime and longer equipment lifespan?

A: We support our customers' and partners' journey to adopt proactive maintenance strategies. The foundation for this is being proactive and acting early. This approach is supported by timely monitoring, analysis, and actionable advice. All this leads to a situation where fluid-based problems are detected and overcome very early. It’s much more cost-effective to prevent potential failure scenarios early before escalation. This lets our customers and partners move from time-based maintenance plans and overhauls towards a need-based model and today we are witnessing less fluid-based maintenance downtimes. Machines also see longer lifetimes once we keep control of the fluid-based problems.

7. Fluid Intelligence also provides CO2 simulation and reporting. What kind of impact have you witnessed in terms of carbon footprint reduction?

A: Indeed, we have introduced an automated CO2 emission analysis and reporting model along with future emissions simulation capability. Looking back a few years and setting a placeholder to the date when we started to work with our customers and partners, we can see quite considerable impacts on carbon footprints. For example, our customer L&T, a leading Nordic circular economy company, has reduced their fluid-based emissions by over 80% since 2019 in their waste treatment facility’s hydraulic crushers. Oil lifetimes have been extending considerably and no oil changes coming in the foreseeable future. Parallel to this reliability and cost-efficiency have been improving constantly.

8. Considering the complexity of AI-based analysis, how do you ensure that your expert advice database and professional services align with the specific needs and challenges of different industries that Fluid Intelligence serves?

A: The foundation for our analysis and reference database development came from the long-term experience our founders had accumulated by solving operational-level problems within many industries. This deep context-based understanding has helped us to steer our service development past years to keep our analysis value-adding and relevant. Our fluid and reliability experts are constantly involved with our Oil Data Management, reference databases, Analysis, and Advice Engine development. This way we are constantly adding value to our customers and partners. My colleague Viktor puts it well, with Fluid Eye® fluid performance is analyzed by machines and overseen by experts.

9. What are your company’s future goals in this rapidly advancing technological landscape?

A: Our customers have commented many times that they don’t need any more data but solutions to their burning problems. This aligns well with our DNA and history of solving customers' and partners' problems on a daily basis. It also works as a guideline for our future goals and vision. We aim to strengthen the link between concrete operational reliability actions, digitalization, automation, and AI-based analytics, and this way deliver more relevant and value-adding advice. One day we may even see autonomous fluid performance optimization. By focusing on solving real-life customer problems, we keep our sight on the right issues rather than focusing on one specific technology development. Technologies and components develop so fast that mastering real-life customer needs to technical advances is where our team finds the most customer value. Parallel to this development we also push forward our Carbon Life Cycle Assessment models to better match future regulatory environments, automate reporting, and help our customers and partners to adapt to this rapidly changing environment. More precise visibility of existing and future emissions accelerates carbon footprint reduction activities significantly.

10. What do you believe are the most critical qualities a leader should possess, and how do you embody these qualities as CEO?

A: That’s a broad question. Listening, vision, empowerment, and removing roadblocks are the first words to think of. Listening to your team, customers, partners, and market as a whole is a must-have quality for a leader. And understanding how things affect the direction where your organization is going. A clear vision and goals set together with your team is another important aspect that helps an organization to keep its focus and adapt to this constantly changing environment. Empowering your team and experts to do their work the best they can is yet another important theme. And removing roadblocks that may prevent them from achieving their targets. And back to listening, making sure you are bringing value to your customers and partners every day as otherwise, you’ll become obsolete soon. In a small and dynamic growth organization, these are not always easy qualities and topics to foster, but I’m positive we are heading in the right direction every day.