In the process industry, even minor variations in input materials can drastically impact product quality, customer satisfaction, and profitability. Companies typically rely on experience and intuition to manage these challenges—but there’s a more reliable approach. By leveraging process modelling and simulation, businesses can accurately predict outcomes, optimise their production strategies, and consistently deliver high-quality products despite variable inputs. Here’s how this solution transforms uncertainty into competitive advantage.
The Challenge: Input Variability Resulting in Unpredictable Output Quality
Within the process industry, companies are often tasked with producing consistent, high-quality products by mixing or combining various input materials. These inputs are often not constant: there can be quite some variability in their characteristics. Moreover, the quality of the output often depends heavily on the characteristics of the input. This presents a huge challenge for companies: being able to deal with varying input characteristics and quality, while preserving the quality and characteristics of end products, which might even be different depending on the customer.
Think of it as making your wine; the grapes you use (input) vary from region to region, and from season to season. These variations influence the characteristics (parameters) of the grapes, such as sugar level, acidity, tannin levels, aromatic components, skin thickness, ripeness, etc. The specific combination of input parameters of the grape will heavily influence the taste of the end product: the wine. If you’re a winemaker and you want to make sure that your wine has the right body, flavour profile, acidity, tannine levels, alcohol content, and so on, you must pay great attention to the input characteristics.
This is exactly the same problem that companies in the process industry experience. Currently, companies often rely on manual decisions based heavily on the expertise or gut feeling of domain experts. On top of that, companies want to make sure that they are operating in an optimal way, trying to minimise costs or maximise revenues.
This approach can lead to suboptimal decisions and inefficiencies, especially when simultaneously juggling multiple conflicting constraints and customer requirements.
How Process Modelling & Simulation Solves This Problem
To address the challenges mentioned above, we created a process modelling framework to optimise the process of combining inputs with specific characteristics to obtain an output that meets the client’s requirements through simulation. A process model is a virtual representation that accurately reflects a physical system or process by quantifying the mathematical or statistical relationships between the components of the system or process.
Process models allow for integrating real-time data and use this data to run simulations to improve decision-making by mirroring the behaviour and performance of their physical counterparts. This way, a process model provides an experimentation ground where experiments can be simulated, allowing for to analysis of the outcome without having to intervene in the real world.
Applied to the situation described above, the process model allows for hypothetically mixing inputs with specific characteristics and simulating the resulting end product with the output characteristics being the combination of the respective input characteristics, according to the process specifications.
How the Solution Works: From Input Data to Optimised Production
The solution consists of two interconnected components: the Process Model and the Optimisation Engine.
Step 1: Data-driven Process Modelling
A process model is built using historical data from past production runs. By analyzing past input-output combinations, the model uncovers and quantifies relationships between specific input characteristics and the resulting output quality.
- Historical Data Analysis: Historical production data, including details about input characteristics and corresponding output quality metrics, form the basis of the model.
- Predictive Capability: With these quantified relationships, the model can accurately predict output characteristics based on new or hypothetical input combinations. This predictive capability allows companies to assess outcomes quickly and confidently.
Step 2: Simulation & Optimisation Engine
Once the process model is established, the optimisation engine leverages its predictive capability to virtually test many different input scenarios.
- Virtual Experimentation: The engine simulates combinations of inputs and predicts resulting outputs, allowing companies to explore different scenarios efficiently.
- Optimisation & Recommendations: Based on specific customer requirements and operational constraints, the optimisation engine identifies optimal input combinations. The optimisation objectives can vary: minimising costs, maximising quality, improving inventory management, or a combination of several strategic goals.
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Additionally, the solution can be further integrated with real-time operational data (such as inventory levels, pending customer orders, and material availability). This real-time integration enables ongoing optimisation, keeping production aligned with customer demand and available resources.
The Result: Improved Efficiency, Reduced Risk, and Consistent Quality
By implementing a process modelling and simulation framework, companies unlock significant benefits, directly addressing the initial challenge:
- Consistent Output Quality: Companies can maintain stable and predictable product quality despite input variability, reliably meeting customer specifications.
- Reduced Production Costs: Virtual experimentation significantly reduces the need for costly physical trials, saving both time and money.
- Enhanced Decision-Making: Decisions are based on data-driven insights rather than gut feelings or manual estimation, significantly improving reliability and reducing human error.
- Strategic Optimisation: Companies can optimise production strategies according to their business objectives—whether prioritising cost-efficiency, quality, energy usage, or inventory turnover.
Ultimately, the solution enables companies to remain competitive, delivering consistently high-quality products while efficiently managing variability in inputs.
