Funis Consulting bridges science and innovation through scientific computing, such as computational chemistry and physics, advanced modelling & simulation, process optimisation and data science. We help organisations understand complex systems, optimise performance and make confident, insight-driven decisions. Data Handling Scientific & Technical Services Food Scientific Consultancy About Us Funis Consulting Ltd is a dynamic company based on the Maltese islands (European Union) and was founded by Robert Cordina who brings over 20 years of experience working with startups, multinational corporations, and academia. Driven by a passion for science, technology, and innovation, Funis Consulting specialises in custom modelling and simulation software development to provide businesses with precise consulting services as well as applications and tools for a wide range of industries, enabling such businesses in their decision-making, innovation, performance, competitiveness and efficiency. The first key area is Modelling and Simulation, encompassing both data-driven approaches and first-principles/physics-based methods. Our expertise spans machine learning, mathematical, chemical and physical modelling, process/parameter optimisation, and high-performance computing, allowing us to tackle complex challenges with precision and efficiency, using both custom and off-the-shelf software. Whether it’s data-driven modelling – such as data analysis, statistics, advanced machine learning, and AI – or physics-based approaches like chemistry models (reaction kinetics, Molecular Dynamics (MD) and thermodynamics), Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), Smoothed Particle Hydrodynamics (SPH), Discrete Element Modelling (DEM) or Discrete Event Simulation (DES), Funis Consulting develops and integrates bespoke software and modelling solutions using your software/programming language of choice to enhance decision-making, automate processes and optimise business operations which drives efficiency, innovation, and create real-world impact. Whether through advanced simulation techniques, machine learning integration, or GPU-accelerated computing, we help businesses remain at the cutting edge of innovation. The second specialisation is scientific consultancy for the FMCG industry through the use of modelling and simulation. With a strong background in chemistry and physics, we support businesses in key areas such as research and development, product renovation and innovation, product formulation, NPD and chemistry insight, ensuring your business stays ahead in a rapidly evolving industry. Our Core Specialities are: Build and integrate smart, custom software, from scientific applications to business tools GPU & High-Performance Computing (HPC) Scientific Computing and Mathematical, Physical and Chemical Modelling & Simulation Process / Parameter Optimisation through the use of models Code Optimisation (we are software agnostic for a seamless integration into your current systems) Making sense of complex data through Data Analytics & Visualisation tools / methods Advanced Machine Learning and AI integration in certain models Model-based Scientific Consulting specialising in FMCG and Food Manufacturing industries ranging from new product development to chemistry insights We offer flexible project structures tailored to businesses of all sizes, whether large or small. Our approach can be fixed-cost or time-based, depending on your projects’ needs. Plus, our first online consultation/meeting to see how Funis Consulting can help, is always free, with no obligation. Send us a message to discuss more about your project, by clicking here . Check out our FAQs and Services sections to know more about what we do. Follow our Blog for our insights into the world of Scientific Computing and Scientific Consultancy. Services At Funis Consulting Ltd, we offer a range of services across different industries, to help businesses stay at the forefront of innovation, efficiency and excellence. Through our modelling and simulation, as well as process optimisation services we tackle complex challenges using both custom and off-the-shelf software, depending on your needs. Whether it’s leveraging data-driven models or physics-based simulations, here at Funis Consulting Ltd we deliver the precision and reliability that your Company is after in order to stay ahead in the ever evolving landscape of innovation. Read More About Our Services Bringing Science, Technology and Innovation together Beyond the Numbers: Gaining Insights Through Data Analysis and Visualisation Collecting data is essential, but its true value lies in the ability to interpret and apply it effectively. Businesses must make sense of the data they gather to drive meaningful insights and informed decision-making. With the right analysis and interpretation tools , companies can transform raw data into actionable strategies. By leveraging advanced machine learning techniques and AI integrations, businesses can unlock smarter, data-driven decisions that enhance efficiency and innovation. Read More About Our Services Food Science Unlocked: Expert Consultancy for Smarter Solutions A strong understanding of the science behind food processes and manufacturing is key to developing successful products and staying competitive in an ever-evolving industry. Whether it’s creating a new formulation, refining an existing product, or exploring innovative food alternatives, a scientific-tech science-driven approach ensures that every step is optimised for quality, functionality, and consumer appeal. By applying the use of modelling and simulation tools coupled with expertise in food chemistry and physics, we help businesses can enhance product stability, improve shelf life, and meet regulatory requirements while delivering on taste, texture, and nutritional value. With the right scientific insight and technology, your company can navigate challenges more effectively and bring groundbreaking food innovations to market with confidence. Read More About Our Services Past and Present Clients and Partners Mdlz quote Vow_quote Mdlz quote 1/2

Keep up-to-date with our latest insights on scientific computing, modelling & simulation, data analysis and food science for innovative solutions. Follow Our Posts on Linkedin Follow Funis Consulting Ltd's Page on Linkedin 21st March 2025 Partnership News We are delighted to announce our new partnership with Smart Vision Europe Ltd. Read more news Follow us on Linkedin 13th March 2025 Partnership News We’re thrilled to announce our collaboration between Funis Consulting Ltd and Sweeft Analytics. Read more news Follow us on Linkedin 23rd September 2025 Partnership News We are delighted to announce a new partnership between MCBA Consulting and Funis Consulting Ltd. Read more news Follow us on Linkedin October 2025 News about FuniSoft Ltd's launch of ChromMine(TM)'s software. The Founder and Managing Director of Funis Consulting Ltd, is also co-Founder of UK-based company FuniSoft Ltd. FuniSoft Ltd has officially launched ChromMine(TM) software, a specialised and secure, cloud-based platform for the interpretation and visualisation of complex chromatographic datasets. For more information, visit the website on www.funisoft.com or the LinkedIn page https://www.linkedin.com/company/funisoft-ltd/. Visit FuniSoft Ltd website Visit FuniSoft Ltd Linkedin Page Read our Social Media Articles

< Back When Economics and Physics work together 22 Oct 2025 Economics and physics have more in common than you might think! Both of these fields try to understand complex dynamic systems, meaning where many factors interact and influence each other. By using tools originally developed for modelling in physics, we can better explore how economies work, how different factors affect outcomes, and how they evolve over time. Simulation-based methods allow us to test ideas, forecast trends, and imagine different futures before they happen. When we combine the structured thinking of physics with insights into human behaviour, we can build economic models that are not only more accurate, but also more resilient and adaptable. Economics is sometimes called the social science most similar to physics. Both try to understand how complex systems change over time, depending on many different factors that affect each other. Economists usually use statistics and econometrics to study these changes. But ideas from physics can also help, especially those that look at how things move, interact, and evolve in dynamic systems. Examples include models from fluid dynamics, particle behaviour, thermodynamics, and chaos theory. These often use equations and computer simulations to predict what might happen next. Why is this? Economies are complex systems that are always changing, i.e., dynamic. They include people, businesses, and governments (the main players or interacting agents), and things like money, labour, goods, and information (and the flows between them). Models inspired by physics can help us understand how money and resources move through an economy. For example, cashflow between different sectors can be thought of as liquid flowing through pipes. When something unexpected happens, like a new policy, a natural disaster, or a new technology, the economy reacts, just like when a pipe is blocked and the water pressure and flow change immediately. Thus, changes to the economic environment can be modelled in a similar way to how physical systems respond to shocks. The modelling approaches borrowed from the world of physics are differential equation models (calculating the rate at which things change with time) that can model things like GDP growth, inflation and underemployment dynamics over time, or agent-based simulations (inspired by particle simulations) where each economic agent follows simple rules, and then their collective behaviour emerges dynamically. Other modelling approaches are network theory which helps model trade networks, financial contagion and supply chains, stochastic models to model economic uncertainty, market volatility or risk (like random fluctuations in particle behaviour) and system dynamics and feedback loops which can model competition between firms, sectors or nations. Using models from physics and life sciences in economics has several advantages. They can show complex, non-linear interactions and how new patterns emerge. They also allow us to test different scenarios, connect small-scale behaviour (like individual choices) with large-scale outcomes (like national trends), and even run real-time simulations to test policies before applying them. However, there are also challenges. Human behaviour is much harder to predict than physical forces. Data can be limited or noisy, which makes it difficult to fine-tune models. And if models are too simple, they may miss important cultural, psychological, or institutional factors that also shape economic systems. By borrowing models from the physical sciences, we can better understand how real economies behave and change over time. Human behaviour is harder to predict than physical forces, but these mathematical and simulation-based methods still offer powerful tools for forecasting, testing policies, and exploring possible futures. Combining the structured approach of physics with insights into human behaviour can help create more flexible and resilient economic models. Previous Next

< Back Taming the Giants: Large-Scale Modelling and how can Surrogate Models be the right move 09 Jul 2025 Large-scale models can take ages to run, slowing down decision-makers and frustrating the users. Surrogate models offer a solution to this challenge. Surrogate models are simplified, faster alternatives trained on input-output data from the original model. While they aren’t physics-based, they can mimic complex models closely and deliver results far more quickly. Large-scale modelling is developing and using computational models to simulate systems which are very complex in nature. It’s all about managing complexity, as you have lots of variables and many scenarios with often very time-consuming computations. These systems would normally require the processing of large amounts of data (or variables) within wide ranges to represent real-world systems at significantly large scales, both temporally or spatially and so they require substantial computational power or time to solve. There are two types of large-scale models. The first type involves machine learning or statistical models trained on vast datasets - think on the lines of predictive models trained on millions of datapoints or high-dimensional data. Such models are used in many fields, ranging from finance, marketing, and bioinformatics. The second type is complex mechanistic or first-principles (physics-based models) which are based on physical or chemical laws and rules, and are often formulated as systems of differential equations and these can be used in engineering, environmental modelling, climate science, fluid dynamics or food process simulations. Let's take Climatic Modelling for instance, these simulate the Earth's atmosphere, oceans, land surfaces and ice and such models use fundamental laws of physics to predict how climate variables like temperature, rainfall or wind patterns change over time. Since these must cover the entire globe over decades or even centuries, they require huge computational resources. Another example would be Computational Fluid Dynamics (CFD) in Food Processing, designing process such as spray drying or extrusion in food manufacturing. CFD models are used to simulate how fluids, such as air, steam or liquids, move and transfer heat or mass. These models are based on the Navier-Stokes equations and require fine-grained spatial and temporal resolution to capture key details. Running a single scenario can take hours or days especially if the geometry and chemistry is complex or the material properties are complex and vary with conditions such as temperature or pressure. So, if you've ever worked with large-scale modelling, whether that's handling vast datasets or complex, physics-based models, you’ll know that solving or training these models can take anywhere from a few minutes to several weeks, if not more. This time lag can be frustrating, especially for end users who may not fully understand why the results take so long. Often, this becomes a barrier to adoption. However, the good news, is that there is possibly a way around this! Surrogate models, are simplified mathematical versions of your original model, constructed using the outcomes of simulations from that full-scale model. By running the original model under a variety of starting conditions or inputs, you collect a range of outputs. Provided the underlying model is robust, these input-output pairs can be used to train a new, much faster model that mimics the behaviour of the original. While this surrogate model won't be rooted in physical laws, it will be built on sound data generated from a model that is. That being said, two critical questions arise, the first being how many original simulations you need to execute, and will it take so long that building the surrogate model becomes no longer practical or feasible? The answer to this depends on several factors, mainly the complexity of your model and the breadth of the input space that you want to explore. If you're dealing with many variables across wide ranges, the effort required might be substantial. Still, it could be worthwhile. Surrogate models can offer results orders of magnitude faster than the full models. Building one isn’t always straightforward, but if it makes your work more accessible and widely used, it might just be the right move. Previous Next

< Back Discrete Event Simulation: A Smarter Way to Understand Process Dynamics 20 Aug 2025 How much it is that new machine worth? Thinking of buying a new machine for your production line? Before making a costly investment, Discrete Event Simulation (DES) can help you test the impact virtually, by simulating current versus the proposed set-ups and compare the two. It helps you make data-driven decisions about capacity, not guesses and it answers questions such as whether the performance gain is worth the spend. In manufacturing, every minute spent in idle time, queuing or in inefficient resource usage, affects throughput, quality and profitability. Discrete Event Simulation (DES) is a powerful technique that models how a process unfolds over time by tracking individual "events", such as for instance the arrival of materials, the start of a batch or a machine breakdown. Unlike standard or continuous simulations that track variables at every moment, DES jumps from one event to the next and so this method is especially well-suited for systems that involve waiting lines, scheduling decisions, resource competition, or shift patterns. With DES you can identify bottlenecks by understanding where queues build up and why, you can conduct scenario testing by comparing different layouts, staffing models or batch schedules, you can carry out downtime analysis to assess the ripple effects of delays or failures, and you can support decisions to provide data-backed insights for capital investments or process change. For industries with variable demand, perishability, and complex multi-step processes, such as food, pharma and logistics, DES is truly transformative. Let's say we have a production line. DES would break the system down into discrete events such as the product entering a station, a machine starting/stopping, a worker becoming available, a batch changeover, a conveyor breakdown and a waiting queue forming. Each event changes the state of the system and happens at a particular point in time. Take for instance yoghurt production, where you have the filling station where tubs get filled, the sealing machine, the labelling machine and the packing station. After you have set up the logic, you can then simulate the arrival rate of yoghurt tubs, the processing time per machine, the downtime or maintenance events, operator availability, queues forming between stations and bottlenecks or underused equipment. The questions that Discrete Event Simulation helps you to answer are "What happens if I add another labelling machine?", "How much is the downtime before output drops by 10%?", "What if operator shifts are staggered?", "Is packing keeping up with filling?"... Basically, you can answer these questions without trial and error. It can help with decision making when a company is, for instance, trying to assess whether it would make sense to purchase another machine. Since with DES you can simulate it in a virtual environment and compare the current set-up to the proposed new setup, it helps you measure the impact of the overall output, the utilisation of the new machine, the changes to queue lengths or waiting times, the ROI from improved throughput and whether adding a machine just shifts the bottleneck elsewhere. Bottom line is that DES helps you make data-backed investment decisions by showing whether the performance gain justifies the cost, before you spend a cent. Previous Next

< Back Discrete Event Simulation Meets Process Modelling 27 Aug 2025 When a production line hits its limits, it is tempting to invest in more equipment but what if the real solution is hidden in the process? By combining Discrete Event Simulation (DES) with process optimisation you can identify bottlenecks, reduce idle time, improve flow and resource use and test scenarios without disrupting operations. In many cases, you will see throughput gains and cost savings without adding a single machine! This is because sometimes the smartest investment is simulation. Process modelling tells you how something works. It focuses on what happens such as unit operations, material transformations, mass and energy balances and quality parameters. Discrete Event Simulation (DES), on the other hand tells you when, how often and for how long it works (or doesn't). DES focuses on when it happens and looks at things such as queue times, delays, capacity utilisation, operator shifts and schedule adherence. So, on their own, each of the methods offer insight, but combined they create a more complete picture of dynamic systems in food and manufacturing operations. This is because you can simulate not only the steps in your process but the timing and resource implications under different conditions. Some examples are when simulating a bottling line where temperature profiles (process model) interact with shift patterns and downtimes (DES) or when modelling a bakery's dough fermentation (process kinetics) alongside batch scheduling and equipment cleaning cycles. It can also help evaluate how small changes in batch size affect overall equipment effectiveness, energy use and delivery times. This hybrid approach is increasingly useful for digital twins, capacity expansion planning and investment decisions, especially in food, where shelf life, throughput and variability make timing critical. Insight emerges not just from what happens but from when it happens and to what extent and that is where process models and DES complement each other. Let's take the example of a biscuit production line where we want to improve throughput while reducing energy use and bottlenecks. The current situation is that you have a production line with dough mixing, baking, cooling and packaging stages. The oven is a bottleneck, running at maximum capacity, and so packaging machines are often idle waiting for a product and as a result energy use spikes due to a "stop-start" behaviour. So, DES in this case simulates the line and models every step including the details such as batch sizes, machine speeds, delays and shifts. It shows where and when queues form, machines go idle, or capacity is underused, and it tracks performance metrics like overall equipment effectiveness, throughput and idle time. Process optimisation on the other hand, when applied, adjusts batch size, buffer sizes and timing to reduce idle time, it tests different staff schedules or machine speeds and it identifies oven usage patterns that smooth out energy consumption. The result may be increased throughput without new equipment, reducing packaging downtime, lower energy costs by optimising baking cycles and data-backed confidence in changes before implementing on-site. Previous Next

< Back Modelling: A Thoughtful Approach to Understanding and Innovating 02 Jul 2025 At its heart, modelling is about making sense of complexity allowing us to simulate outcomes, test ideas virtually and optimise systems before we even step into the lab or the production floor. In this article we walk trough how we approach model building, not in heavy tech-jargon but in plain, accessible language. If you are curious about how modelling could support your work or simply want a better understanding of what it actually involves, take a look. Creating a model, at its core, is a way to understand the world. It is a process that allows us to translate complexity into clarity and help us make better decisions, especially before heavy investment is made or before important decisions are taken. It also helps uncover hidden behaviours and relationships thus predicting what might happen next and also provide the best outcome possible or to use the best variables possible for a given solution or scenario. Modelling starts with the purpose that you are trying to achieve. A model should never exist in a vacuum or in isolation, as it is built to answer questions and to solve problems. These could be how a product will behave under certain circumstances, simulating a process to reduce waste or testing what happens when you tweak one or more variables in a production line. Or even understanding how proteins behave in food systems, building economic models or understanding how manufacturing processes respond to different settings and mechanistic variables, or maybe even how a new formulation might perform even before stepping in a lab, or creating a first prototype. In essence modelling becomes a powerful ally, with the first step being understanding what you are trying to find out or what is the problem that you are trying to resolve. Once the purpose is clearly defined, the next task is to get to know the system, and this mostly comes with breaking it down. Understanding what goes in, what comes out and what happens in between is essential. In a food context for instance, this might involve ingredients, temperatures, moisture levels, microbial interactions and how all of this connects to each other and how these evolve together. After understanding the system, we then choose how to represent that system. Some models may rely on known scientific principles, often called mechanistic models. Other models are driven by data, picking up the patterns that might not be obvious to the human eyes. The best results sometimes come from blending the two creating a hybrid approach to modelling, by combining the two approaches (first-principles and data-driven models). The choice depends on the nature of the problem and the data available, as well as whether you have more knowledge of the system as well as quality data. Data is of course essential. It may come from experiments or previous studies. This information becomes the fuel for the model through shaping the model, calibrating and giving it meaning. After this, you build. You build models using tools such as Python, R or specialised simulation software and we begin translating the system into something the computer can work with which could be an equation, a set of rules or a learning algorithm. We test the model and compare its behaviour to reality, refining it as we go. It is an iterative process often requiring a fair bit of tweaking and creative thinking. Once a well-built model is ready it can offer a new lens for decision making. It can help companies predict outcomes without trial-and-error, reducing costs by optimising processes, accelerating innovation timelines and making informed strategic choices, especially in environments where real-world experimentation is time-consuming, risky, or expensive. Here at Funis Consulting, we see modelling not as a technical afterthought but as a cornerstone of innovation. By combining scientific understanding with modern computational tools, we help clients simulate, predict and optimise across a range of domains, from food systems, to industrial processes and beyond. While every model we build is unique to the problem at hand, the goal is always the same: to make the invisible visible and the complex comprehensible. Modelling is about numbers and equations but it is also about insight and foresight. Increasingly, modelling is becoming a quiet but essential driver of smarter business in a world where agility and accuracy matter more than ever. Previous Next

LinkedIn Published Articles Purchase Decisions and Why Preference Models help with Smarter Product Design Read Article When to Hire, When to Hold: Making Smarter Staffing Decisions through Marginal Analysis Read Article From K-pop to K-beauty...now let's talk about K-food! Read Article Strategic decision-making under resource constraints - moving "beyond the curve" Read Article When Economics and Physics work together Read Article Glassy state or plastic state? Moisture control is key for consistent quality in food products. Read Article Of Randomness, True Probability and Simulation Read Article Training and Testing your Model Read Article The Importance of Choosing the Right Visualisation for your Data and your Audience. Read Article From Molecules to Markets: How Simulation Bridges Science and Business Read Article From Chemistry to Code: How Modelling, Simulation and Data Science are Transforming Formulation R&D Read Article Good data means good models. Bad data means misleading models, predictions and decisions. Read Article Discrete Event Simulation Meets Process Modelling Read Article Discrete Event Simulation: A Smarter Way to Understand Process Dynamics Read Article Discrete Element Modelling (DEM): Getting Granular with Simulation Read Article Smoothed Particle Hydrodynamics Read Article Modelling Chemical Complexity: Coupling Reaction Kinetics with Computational Fluid Dynamics (Part 2 of 2) Read Article Modelling Complex Chemical Reactions in Dynamic Systems through Reaction Kinetics (Part 1 of 2) Read Article Taming the Giants: Large-Scale Modelling and how can Surrogate Models be the right move Read Article Modelling: A Thoughtful Approach to Understanding and Innovating Read Article Making Manufacturing Smarter: Addressing Production Challenges through Modelling and Optimisation Simulation Read Article Of Force Fields and Simulations: Whether it’s All-Atom (AA), United Atom (UA) or Coarse-Grained (CG), a good Force Field is a cornerstone in Molecular Dynamics Read Article Process Modelling & Simulation: Calibrated system infrastructures for when failing is not an option. (Part 1 of 3) Read Article The Science and Value of Finite Element Analysis (FEA) in Food Packaging: Food packaging plays a crucial part in complex supply chains (Part 2 of 2) Read Article The Science and Value of Finite Element Analysis (FEA) in Food Packaging: Packaging is more than a mere container for your food product. (Part 1 of 2) Read Article What bends and what breaks under pressure? And which of the two will happen in a specific circumstance? Finite Element Analysis (or “FEA”) knows best. Read Article Making sense of Flow: How Computational Fluid Dynamics (CFD) can help bring fluid behaviour to life Read Article Sustainable Food Systems through Data Modelling techniques Read Article Process Modelling & Simulation: Calibrated system infrastructures with friendly-to-use, intuitive human-centric interfaces (Part 3 of 3) Read Article Process Modelling & Simulation: Calibrated Dynamic and Steady-State system infrastructures (Part 2 of 3) Read Article Data: Patterns and Clusters in Visualisation (Part 2 of 2) Read Article Harnessing the Power of Optimisation Read Article My Phython, what's your Numba? The Power of Code Optimisation. Read Article Fat Bloom in Chocolate Read Article Data: The Importance of Data for Strategy and Decision-Making (Part 1 of 2) Read Article

Funis Team Robert Cordina Founder & Managing Director Read More Audrey Cordina Sacco Head of Growth & Operations Read More Arthur (Turu) Vice President of Recreation & Wellbeing Read More