Your Guide to Smarter Manufacturing with Machine Learning

By Soham NandiReviewed by Bethan DaviesApr 16 2025

Manufacturing is an energy-hungry industry. Whether it’s heating, cooling, or machining, these processes are some of the biggest energy consumers out there. And with that energy use comes a hefty carbon footprint. For manufacturers trying to balance performance with sustainability, that’s a big challenge.

Image Credit: Gorodenkoff/Shutterstock.com

But there’s good news: real-time machine data is making it easier to track what’s actually going on inside manufacturing equipment. That data opens the door to smarter decisions, predictive maintenance (less downtime, fewer surprise breakdowns), and better yields. Even something as simple as monitoring whether machines are running, idle, or off can highlight wasted energy and non-productive time. Pair that with sensor data, and suddenly you’ve got insights to improve efficiency and quality across the board.^1,2

This article explores how manufacturers are tapping into machine data and AI-powered tools to reduce waste, improve uptime, and make smarter decisions in real time. From predictive maintenance and energy forecasting to anomaly detection and intelligent scheduling, we’ll look at how ML is being applied—and what kind of results it’s delivering.

Download your PDF copy now!

Why Machine Learning?

Energy challenges in manufacturing aren’t simple. Between unpredictable external factors like weather, shifting production schedules, and the constant balancing act of HVAC systems, things get complicated fast. And traditional controls—think basic on/off switches—just don’t cut it anymore.

That’s where machine learning (ML) shines. It handles messy, nonlinear data really well, surfacing patterns that conventional methods usually miss. What's more, ML adapts in real time. So instead of relying on static models, you get systems that learn and adjust as they go—optimizing energy use without compromising on production quality.

In fact, studies show ML can predict energy demand and fine-tune HVAC performance better than simulations alone. Some implementations have cut energy consumption by up to 50 %.^1,2 That’s not just a win for the environment, it’s a serious cost-saver, too. 

Predictive Maintenance: From Rulebooks to Real-Time Smarts

Predictive maintenance (PdM) has become a game-changer for manufacturers looking to reduce unplanned downtime and extend the life of their equipment. But this isn’t a new idea. In fact, the first wave of PdM tools goes all the way back to the 1980s.

Back then, systems relied heavily on rules and human expertise—think vibration thresholds, scheduled inspections, and logic-based diagnostic tools. They worked, but only up to a point. As equipment and processes became more complex, those static systems started to fall short.

ML has filled in the gaps, offering a more flexible and data-driven approach. Instead of relying on fixed rules, ML algorithms analyze streams of real-time data to catch issues before they cause failure—and in many cases, before humans would notice them at all.⁴

Here’s how predictive maintenance has evolved over the decades:

1. The Rule-Based Era (1980s–2000s)

Early systems were built around expert-defined rules. A great example is Ford’s TIES framework, which followed a logical decision tree to diagnose faults. By the mid-90s, hybrid models combining neural networks and fuzzy logic began to emerge. These were used in systems like induction motors to detect problems such as bearing wear or insulation breakdowns—and they could explain their reasoning in human-readable ways.⁴

2. Smarter Detection with Advanced Techniques (2000s–2017)

As data collection improved, so did the methods for making sense of it. Tools like principal component analysis (PCA) helped simplify high-dimensional data from complex systems—like PVC pipe manufacturing—making it easier to spot anomalies. Other approaches, like support vector data description (SVDD) or Mahalanobis-distance-based models, worked well when fault data was scarce. One creative solution that came about during this period was the "neo-fuzzy neuron" model that learned quickly from refrigeration data to minimize defects in copper plants.⁴

3. Deep Learning and Real-Time Insight (2017–Present)

Now, we’re seeing powerful models like long short-term memory (LSTM) networks being used to recognize patterns over time, especially in noisy equipment log data. Random forests can now predict failures up to a week in advance based on subtle shifts in vibration. And bidirectional LSTM models are improving energy efficiency in systems like ethylene production by learning from both past and future behavior. Even in cases where data is limited, traditional support vector machines (SVMs) still play a role, particularly in diagnosing imbalances in rotating equipment.⁴Even for major players in the field, like Siemens, introducing ML into predicative maintenance is not just about enhancing technology; it's also about driving tangible benefits for manufacturers:

By harnessing the power of machine learning, generative AI, and human insights, we’re taking Senseye Predictive Maintenance to the next level. The new functionality makes predictive maintenance more conversational and intuitive – helping our customers to streamline maintenance processes, enhance productivity and optimize resources. This marks an important milestone in countering skill shortage and supporting our customer’s digital transformation.

Margherita Adragna, CEO, Customer Services for Digital Industries, Siemens AG 

Load Forecasting: Powering Smarter Decisions

Alongside maintenance, energy forecasting is another high-value use case for ML in manufacturing, especially as facilities work to integrate renewable power sources and manage fluctuating demand.

One standout case comes from the Technical University of Darmstadt, where researchers built an ML model to predict a factory’s energy usage in 15-minute intervals. The goal was to give manufacturers the ability to proactively manage energy loads, optimize equipment schedules, and cut unnecessary costs.⁵

Their system pulled from more than 1500 potential inputs, including machine operation data, HVAC activity, and local weather conditions. After testing multiple algorithms, gradient boosting regression trees (GBRT) hit the sweet spot, delivering both accuracy and speed.⁵

This kind of short-term forecasting isn’t just about shaving a few dollars off the electric bill. It plays a key role in grid stability, especially as more factories transition to solar, wind, and other renewable sources. Knowing your load ahead of time means you can shift production or schedule heavy energy tasks when it makes the most sense—financially and environmentally.

Catching Energy Anomalies Before They Cost You

Even with solid forecasting and maintenance plans in place, unexpected energy spikes can still slip through the cracks. These anomalies aren’t just expensive—they’re often signals of deeper inefficiencies or equipment issues. The challenge is that they don’t always follow predictable patterns, and many facilities lack labeled data to train traditional models on what counts as "normal" vs. "abnormal."

That’s where unsupervised ML comes into play. Instead of needing pre-labeled fault data, these models learn baseline behavior directly from time-series data and flag anything that strays too far from that baseline.⁶

A standout approach here is the LSTM autoencoder (LSTM-AE). It works by compressing and reconstructing energy usage data. If the difference between the predicted and actual values exceeds a certain threshold, the system flags it as a potential anomaly. And since the model learns from your facility's own data, it’s tailored to your specific operational context.⁶

In a case study from a German metal processing plant, the LSTM-AE model identified:⁶

• 133 facility-level anomalies, many triggered during high solar power generation
• 353 equipment-level anomalies, including transitions between machine modes

One of the most powerful aspects was the model's ability to detect contextual anomalies—energy patterns that seem fine in general but are unusual for a specific moment. For example, it caught the spike that occurred when a laser-punching machine switched from setup to full production mode. A traditional system likely would’ve missed it.

Anomaly detection like this gives teams the opportunity to respond faster, fine-tune operations, and avoid energy waste before it snowballs into a bigger problem.

Real-Time Optimization That Adapts On the Fly

Predictive insights and anomaly alerts are powerful—but what if your system could go a step further and actually optimize itself in real time?

That’s the promise of reinforcement learning (RL), a branch of AI that trains systems to make decisions through trial and error. It’s especially useful in dynamic environments, like a manufacturing floor, where conditions are always shifting and the right move depends on the situation.^7,8

In one study focused on industrial glass tempering, researchers built a deep reinforcement learning (DRL) system to optimize two energy-critical variables: conveyor belt speed and furnace temperature across nine heating zones.^7,8 The model was trained using a random forest predictor built on ANSYS simulation data. Of the RL techniques tested, proximal policy optimization (PPO) delivered the best results, cutting energy consumption by 18 % while still maintaining glass quality within tight ±5°C tolerances.⁷

And it didn’t need human intervention. The system learned from a simulated environment and continuously improved on its own.

Meanwhile, another team tackled a more complex scheduling problem—how to allocate jobs with different energy demands across multiple machines. Their solution was a hybrid RL and genetic algorithm system, known as RLGA.⁸

This model blended:

• A Q-learning layer that adjusted algorithm parameters in real time
• An NSGA-II algorithm to explore optimal job-machine pairings
• A k-means clustering tool to sort similar scheduling problems for quicker optimization

By accounting for things like machine-specific energy profiles, setup energy penalties, and timing constraints, RLGA helped reduce idle machine time and improve energy efficiency, which is especially useful for factories rolling out new, greener equipment alongside legacy machines.

In both cases, real-time learning systems didn’t just optimize a single process. They learned how to improve over time, adapting to changes and reducing waste with every cycle.

Smarter Quality Control Without the Energy Drain

Energy efficiency doesn’t stop at big systems like furnaces or HVAC, it also extends all the way to the inspection table. And that’s an area where traditional methods have often struggled.

Manual visual inspection systems are not only time-consuming, but they also tend to rely on dedicated lighting, handling equipment, and trained personnel, all of which adds up in terms of energy use and labor costs.

To solve this, researchers have in fact developed a machine learning workflow that uses deep convolutional neural networks (D-CNNs) to automate visual quality checks—specifically for brake calipers.⁹

Here’s how it works:

• First D-CNN: Removes background noise from product images
• Second D-CNN: Detects surface-level defects—scratches, missing logos, paint inconsistencies

What makes this approach energy-efficient is how it was trained. Instead of collecting massive amounts of real-world data (which takes time and energy), the researchers used synthetic images generated from CAD renderings. These were layered with simulated lighting and defect types to teach the model how to spot problems in a wide range of conditions. A limited set of real images helped fine-tune accuracy.⁹

Multiple CNN architectures, like VGG-16, ResNet, and Inception, were tested to find the right balance between speed and precision. The final model delivered accurate, scalable inspections without the overhead of traditional setups.

So not only does this reduce inspection time and costs, but it also helps lower the overall energy footprint of the production line.

Wrapping It Up: Smarter Energy, Smarter Manufacturing

If there’s one theme running through all of this, it’s that data alone isn’t enough—what matters is how you use it. Machine learning is helping manufacturers do just that: turning raw machine and energy data into actionable insights, smarter operations, and meaningful cost savings.

Whether it’s predicting when a motor’s about to fail, fine-tuning HVAC systems to match demand, spotting energy spikes before they cause problems, or even optimizing job schedules on the fly, ML is proving its value across every corner of the production floor.

And as energy costs rise and sustainability targets tighten, these tools aren't just “nice to have”—they’re quickly becoming essential for staying competitive.

Of course, getting started with ML doesn’t mean overhauling your entire operation overnight. Many of the most effective applications, like anomaly detection or load forecasting, can be layered onto existing systems and scaled over time.

The bottom line is that smart energy management isn’t about cutting corners. It’s about being more intentional with the resources you already have.

Ready to Explore More?

If this has got you thinking about where machine learning could fit into your facility, here are a few next steps worth considering:

1. Audit your existing data streams—what’s being collected, and how is it being used?
2. Start small: predictive maintenance and energy forecasting are great entry points.
3. Look into ML platforms designed for industrial use—many are built to integrate with the systems you already have.
4. Explore partnerships with academic or research institutions—they’re often ahead of the curve on ML techniques tailored to manufacturing.

And if you're interested in deeper dives on any of the topics from this post—digital twins, smart scheduling, or quality control automation—why not check out some of the below articles?

• Advancing Additive Manufacturing with ML and Digital Twin
• The Role of AI in Production Planning
• Optimizing Additive Manufacturing Processes with Machine Learning
• Machine Learning and Semiconductor Manufacturing

Download your PDF copy now!

References and Further Reading

1. Mawson, V. J., & Hughes, B. R. (2020). Thermal modelling of manufacturing processes and HVAC systems. Energy, 204, 117984. DOI:10.1016/j.energy.2020.117984
2. The Art of Machine Data Collection in Manufacturing | IoT For All. (2024). IoT for All. Available at: https://www.iotforall.com/the-art-of-machine-data-collection-in-manufacturing (Accessed on 15 April 2025)
3. Mawson, V. J., & Hughes, B. R. (2020). Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector. Energy and Buildings, 217, 109966. DOI: 10.1016/j.enbuild.2020.10 9966
4. ‌Das, M. K., & Rangarajan, K. (2020). Performance Monitoring and Failure Prediction of Industrial Equipments using Artificial Intelligence and Machine Learning Methods: A Survey. DOI:10.1109/iccmc48092.2020.iccmc-0000111
5. ‌Walther, J., Spanier, D., Panten, N., & Abele, E. (2019). Very short-term load forecasting on factory level – A machine learning approach. Procedia CIRP, 80, 705–710. https://doi.org/10.1016/j.procir.2019.01.060
6. Kaymakci, C., Wenninger, S., & Sauer, A. (2021). Energy Anomaly Detection in Industrial Applications with Long Short-term Memory-based Autoencoders. Procedia CIRP, 104, 182–187. DOI:10.1016/j.procir.2021.11.031
7. Choumicha El Mazgualdi, Tawfik Masrour, Barka, N., & Ibtissam El Hassani. (2022). A Learning-Based Decision Tool towards Smart Energy Optimization in the Manufacturing Process. Systems, 10(5), 180–180.         
8. Perez Bernal, C., Salido, M. A., & March Moya, C. (2024). Optimizing energy efficiency in unrelated parallel machine scheduling problem through reinforcement learning. Information Sciences, 693, 121674. DOI:10.1016/j.ins.2024.121674
9. Casini, M., De Angelis, P., Porrati, M., Vigo, P., Fasano, M., Chiavazzo, E., & Bergamasco, L. (2024). Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control. Energy Efficiency, 17(5). DOI:10.1007/s12053-024-10228-7

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.