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Machine Learning in Engineering: From Data to Decision

Updated: Apr 30

Decision making

In this blog, we delve into how the strategic application of machine learning can support decision-making in engineering, leading to enhanced efficiency and effectiveness.


Decision-making for most of us, is a part of our everyday. It is the process of choosing between alternatives to achieve a desired outcome. For a company to thrive, it needs to have people making good decisions, efficiently.

How often do we pause to consider not only how good the decisions we make are, but how efficiently we are making them?

While it is often straightforward to observe the results of the actions we take, quantifying the time and effort invested in making those decisions is far more complex. This requires a deep dive into the processes we follow and the systems and technologies we use. Here are three key reasons why decision efficiency matters.

1. Time is Limited

Many professionals face the daily challenge of managing their time. Efficient decision-making processes are vital to ensure that the limited hours in a day are used most effectively, allowing for more strategic thinking and minimising time spent on routine, low-complexity tasks.

2. Resources are Limited

Decision-making requires the conscious allocation of time and effort from personnel. This comes at a cost. We need to be able to be able to make decisions efficiently to minimise the cost to make the decisions we do.

3. Systems are Dynamic

Inefficient decision-making can result in missed opportunities. When decision-making processes cannot keep pace with dynamic system changes, potential gains can slip through our fingers, and risks may not be mitigated in time. This can result in production or performance losses as well as excessive costs and emissions.

The Opportunity

Machine learning (ML) is transforming the landscape of industrial operations by directly addressing these challenges.

ML is a subset of artificial intelligence where computer systems are trained to make predictions or estimations, based on data, bypassing the need for explicit human-coded instructions. This ability for algorithms to directly learn from data makes ML exceptionally suited for dynamic environments where manual data analysis and decision-making are too slow or impractical.

In technical applications, ML has the potential to profoundly transform decision-making processes, significantly enhancing how engineers analyse, optimise, and predict asset performance.

Here’s how machine learning is making a significant impact:

1. Time Savings

ML helps automate low to moderate complexity processes within daily tasks. This automation frees up time for engineers, allowing them to focus on higher complexity and value-added activities. It reduces the resource time typically consumed by manual processes, addressing the challenge of inefficient use of time and effort.

For more on how ML enhances an engineer’s everyday, check out our recent post here.

2. Decision Cost Savings

In operations where both technical and economic objectives need to be balanced—such as maximising production while minimising costs and emissions—ML provides a flexible framework to model multiple objectives simultaneously. By leveraging historical actions and responses, ML allows for a more dynamic and efficient decision-making process, making better use of the limited time available each day for decision-making.

3. Operating Cost Savings

ML models, such as neural networks, excel in modeling complex functions that traditional analytical solutions cannot. For instance, in predictive maintenance, an ML model might learn to predict equipment failure based on a range of sensor inputs that do not have a straightforward relationship to failure modes. This predictive capability allows for decisions about maintenance actions to be taken proactively, minimising downtime and costs.

In summary, machine learning can not only streamline workflows by automating routine tasks but also enhances the adaptability and efficiency of decision-making processes. By equipping engineers with tools that can predict outcomes and optimise operations, ML enables industries to navigate complex challenges more effectively and with greater agility.

Solution - Apply Machine Learning in Engineering

While the adoption of data-driven modeling presents a significant opportunity, it also introduces new complexities that can detract from core business objectives.

At SIG ML, we recognise these challenges and specialise in embedding machine learning models directly into engineering tools, enhancing their functionality and decision-making capabilities.

We combine machine learning with physics-based models and engineering principles to provide data-driven models based in the realities of physical systems. This integration not only enhances the predictive power of machine learning but also ensures our models are both robust and highly relevant to industrial applications. Our hybrid modeling approach is designed to deliver precision, efficiency, and reliability, transforming how industries operate and make decisions.

Integrating ML into the tools engineers use not only enables hands-on application of machine learning but also empowers their decisions, making engineering workflows more efficient and effective.

Learn More

Explore how SIG ML is revolutionising the field of machine learning in engineering by visiting our blog. You’ll find a wealth of resources and insights into how our cutting-edge solutions can be applied to your specific needs. Subscribe to stay updated with the latest posts and breakthroughs.


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