Machine Learning in Manufacturing
Turn the production data you already have into predictions about quality, maintenance and demand, built into the systems you already run, not another dashboard to check.
Machine learning in manufacturing turns the production data you already generate (sensor readings, inspection photos, MES records, order history) into predictions your team can act on for quality control, predictive maintenance, demand forecasting, and process optimization, before a problem reaches the shipping dock, the maintenance log, or the sales forecast.
We build machine learning models around the data your plant already produces and integrate them into your existing ERP, MES or CAD systems. When off-the-shelf software covers most of your process, we tell you to buy it. When it does not, that is where a custom model earns its place.
23 Deloitte Fast 50 Central Europe 2023
Deloitte Fast 50
Forbes Technology Council Official Member
Forbes
1000 Europe’s Fastest Growing Companies
2023 & 2024
Financial Times
Polish Company International Champion 2020
PwC
Master of Innovative Transformation 2021
MIT Sloan Review
Where is machine learning already paying for itself on your floor, and where are you still guessing?
Does your defect rate depend on which inspector is on shift?
Manual visual inspection is consistent for exactly as long as the inspector’s attention holds.
Do you find out a machine is about to fail after it already has?
Reactive maintenance means the failure, the downtime and the rush order all happen at once.
Is your demand forecast still a spreadsheet, last quarter’s numbers and a gut feeling?
Statistical and ML models built on your own sales and production history do this with less guesswork.
Did an earlier AI or ML pilot stall before it reached the shop floor?
It’s common. A Parsec Corporation survey of manufacturers found 72% have adopted AI in some form, but only 10% have scaled it past a pilot. [Source: Parsec survey, via PR Newswire, 2026]
Signature differentiator statement
Machine learning is not a system that sits next to your production line, but a set of predictions built from the data your production line already makes, delivered inside the tools your team already opens.
We don’t sell a standalone ML platform. We build a model, train it on your sensor data, your inspection images or your order history, and wire it into your ERP, MES, or quality system, so the prediction shows up where your team already works instead of in one more login they have to remember.
The real technical variable: what kind of data you already have
What decides what machine learning can do for you? Not your industry. Your data.
A precast concrete plant, a furniture manufacturer and a steel fabricator ask for different things, but the technology that answers them sorts into the same three buckets, based on what data already exists in a usable form:
| You already have… | Machine learning can do… | Starting point |
|---|---|---|
| Sensor or time-series data from equipment (vibration, temperature, current draw) | Predictive maintenance: flag equipment likely to fail before it does | Model training on historical sensor + failure data |
| Images or video from the line (cameras, existing inspection stations) | Quality control and defect detection: catch defects a tired inspector on the last hour of a shift would miss | Computer vision model trained on labelled examples of good and defective parts |
| Transactional or ERP data (order history, production schedules, inventory) | Demand forecasting and production planning: plan raw material needs and schedules against a statistical model instead of a gut estimate | Forecasting model trained on historical order and production data |
If none of these exist yet in a usable form, the first project is building the data pipeline, not training a model. Many manufacturing companies struggle with gathering, structuring, and governing production data before any model work can start. We say that upfront rather than after the invoice.
Four ways manufacturers put machine learning to work
Quality control and defect detection
Computer vision models trained on images from your existing cameras or inspection stations learn what a good part looks like, then flag the ones that aren’t, including microscopic defects a human eye is prone to miss on repeat inspection.
Business impact: fewer defective parts reach the next process step or the customer, less material wasted on parts that fail downstream, and inspection stops depending on which inspector is on shift.
→ Deeper mechanics: Computer Vision for Manufacturing, AI Defect Detection Solutions
Predictive maintenance
Models trained on historical and real-time sensor data (vibration, temperature, current draw, run hours) learn the pattern that precedes a failure and flag it early enough to schedule a repair instead of reacting to a breakdown.
Business impact: maintenance gets scheduled around production, not the other way around, and fewer failures turn into emergency downtime.
→ Deeper mechanics: IoT in Manufacturing
Demand forecasting and production planning
Statistical and machine learning models trained on your order history, seasonality, and production data forecast demand and feed it directly into production schedules and raw material planning. This extends naturally into inventory management and supply chain operations: knowing what’s coming lets you hold less safety stock without running out.
Business impact: production schedules and purchasing decisions are based on a model of your own data, not a spreadsheet extrapolation.
Process and root-cause optimization
Machine learning models analyze production data across your MES and IoT sensors to find ideal operating parameters and the hidden variables behind recurring delays, quality drift, or energy consumption spikes, the kind of pattern that’s invisible in a single shift’s data but obvious across months of it.
Business impact: faster answers to “why did this batch run long” or “why did energy use spike on Tuesdays,” instead of a root-cause investigation that takes longer than the problem it’s chasing.
What changes for your team
Consistent quality, regardless of who’s on shift
Inspection criteria live in the model, not in one inspector’s judgment that varies by fatigue and time of day.
Maintenance planned around your schedule, not around failures
Repairs get booked in before a breakdown forces the issue.
Production and purchasing plans based on data, not a gut feeling
Forecasts come from a model trained on your own history, which also improves inventory planning.
Faster root-cause answers
Patterns that take a human weeks to spot across scattered data get surfaced in the tool your team already uses.
No new system to manage
The model’s output lands inside your ERP, MES or quality system. Nobody on the floor needs a new login.
Where we’ve built this
Ranked below the mechanism and benefit sections per our internal template rule: useful for buyer self-identification, not a technical claim.
- Precast concrete and structural fabrication
- Sheet metal fabrication and metal parts manufacturing
- Steel and welding
- Furniture manufacturing
- Industrial equipment and plant maintenance
What this looks like in practice
ML-Powered Eye-Tracking Solution For Real-World Customer Insights
A market research company approached us with the need to develop a gaze estimation system capable of accurately tracking user attention while they browse content on their smartphones in natural environments. They needed a team of experts that could build something entirely from scratch.
The project involved pioneering work in gaze estimation without relying on specialised hardware, an area that is highly innovative and uncharted. What further added to the project’s complexity was a need for precision in various lighting conditions and across different smartphone models.
As we had to overcome a barrier of limited data being available at the start of the project, we began by collecting training and test data through crowd-sourcing platforms and a dedicated web app. This data fueled the creation of an AI-powered computer vision processing pipeline, which accurately detects where the user’s gaze stops on the screen.
The project could not have been completed without applying ML algorithms with our ML experts using deep learning elements at every stage of image processing. And, we are continuously gathering and processing more training data to improve the algorithm’s accuracy even further.
AI And ML Sensor For Detecting OA Disease In Dogs
A startup founded by veterinary experts sought to create machine learning algorithms for a high-tech sensor that could detect dog Osteoarthritis (OA) once placed into a collar.
Our experts were tasked with building both software and hardware aspects of the solution. After cleaning the provided data and fixing pre-existing issues with incorrect data annotation, our experts employed deep learning models and a deep convolutional neural network to recognise patterns in the collected raw data.
Putting ML and AI at core of the project, they aimed to introduce significant improvements to the work and results done by the client’s previous partners. We were able to sort through all the data, extract deep features, and implement AI solutions and deep neural networks to distinguish between dogs with OA symptoms.
Consequently, our team successfully proposed a deep neural network machine learning algorithm to increase the accuracy of classifying OA severity and the diagnostic accuracy of the device and AI models.
What the data says
Fact 1:
The AI in manufacturing market is projected to reach $128.81 billion by 2034, up from $9.85 billion in 2026, a compound annual growth rate of 37.9%.
[Fortune Business Insights, 2026]
Fact 2:
Predictive maintenance typically reduces machine downtime by 30 to 50 percent and extends machine life by 20 to 40 percent.
[McKinsey & Company, “Manufacturing: Analytics unleashes productivity and profitability”]
Fact 3:
72% of manufacturers have adopted AI in some form, but only 10% have scaled it past a pilot.
[Parsec Corporation survey, reported via PR Newswire, 2026]
Three steps to start
Step 1: Free consultation
We look at what data you already have and whether it supports a machine learning project today.
Step 2: Scoped workshop
We define the specific prediction you need (quality, maintenance, demand) and what data preparation it requires.
Step 3: Scoped pilot
A priced, time-boxed pilot on your actual data, with a clear go/no-go before anything scales.
About DAC.digital
PhD-level expertise: our team consists of PhD experts with research backgrounds in machine learning, computer vision, and natural language processing. They bring unparalleled knowledge and innovative thinking to each project, ensuring you get the most advanced solutions.
Proven track record: we have successfully delivered numerous complex ML projects across various industries.
Customised solutions: we understand that every client is unique and we take time to understand their specific requirements and challenges, developing custom machine learning solutions that align with their needs.
Holistic approach: instead of only providing quick fixes to problems, we develop comprehensive solutions that seamlessly integrate into your business operations and existing infrastructure.
Broad expertise: the knowledge and skills of our team span a variety of ML methods and our experts can develop machine learning solutions for a wide range of applications and industries
Buzz words such as Machine Learning and Artificial Intelligence have recently gained momentum in the business world. For businesses looking to deploy such emerging technologies to gain an advantage, it is imperative not to treat them as a supplement but as an integral part of the business processes. This is the same as a good doctor would suggest taking a balanced and nutritious diet instead of supplements. Our team’s main principle is to develop holistic solutions and NOT cut corners, making a vital difference for our clients to achieve their goals.
Our Experts
At DAC.digital, we are proud to have a team of PhD-level experts who bring a wealth of skills and experience to each project. Having extensively explored the topic of ML in their research papers, they can seamlessly turn your ideas into practical machine learning solutions, assisting you at each stage of the development process.
Even if we are approached with a topic that we are not familiar with, we can adapt to it quickly. Having numerous young specialists in the team is an advantage as we are not strictly focused on a specific area and we can easily adjust to new fields. At the same time, in the team, we have many experts and mentors with years of experience who provide their topical expertise whenever needed. I think I will not lie when I say that we can fulfil any project in the fields of deep learning and computer vision.
Meet some of our specialists:
Marek Tatara, PhD
Assistant Professor at Gdańsk University of Technology, AI/ML Expert at M5 Technology, Member of the Polish Society For Measurement, Automatic Control And Robotics. Works on the implementation of both EU-funded and commercial R&D projects from the field of Computer Vision, Machine Learning and Embedded Systems.
Krzysztof Wołk, PhD
NLP Scientist/Technical Project Coordinator. Natural Language Processing Expert with PhD in the field of Artificial Intelligence. Experienced in AI related project management. Constantly developing. Very interested in dialog systems, human computer interaction, multimedia and signal processing.
Stanisław Raczyński, PhD
Distinguished professional with an impressive track record of 17 years in ML/AI and audio DSP research, coupled with 23 years of engineering experience. He has actively contributed to various applied research projects, demonstrating his expertise in signal processing, natural language processing, machine learning, and robotics.
Karol Duzinkiewicz
Senior Computer Vision Researcher at DAC.digital. Seasoned engineer with many years of experience in international tech companies. Currently holds a team leader role in gaze estimation projects developed in the company.
Jacek Niklewski, PhD
Data Scientist at DAC.digital. Involved in projects related to computer vision, sports applications, medical diagnosis, and recommender systems. He graduated from the Computer Science degree in at Gdansk University of Technology. He has a MSc in Investment Management, a PhD in Finance, and a PgCert in Academic Practice in Higher Education at Coventry University.
Jan Glinko
Machine Learning Researcher at DAC.digital. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in applying synthetic datasets for learning deep neural networks and in learning algorithms to reduce the amount of data required for effective network training.
Artur Skrzynecki
Machine Learning Researcher at DAC.digital. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. His areas of interest mainly focus on computer vision tasks, including biomedical data processing and deep neural networks training and evaluation. Apart from that, he also develops towards web development topics.
Cezary Polak
Machine Learning Researcher at DAC.digital. He graduated from the Faculty of Electronics, Telecommunications, and Informatics at the Gdansk University of Technology. He is interested in using deep learning in biomedical engineering and also in generating synthetic data as photos and texts.
Michał Ostyk
Computer Vision Engineer at DAC.digital. He has experience in computer vision in agriculture, fast food, sports analytics, and healthcare. He loves researching SOTA and converting it into an MVP in Pytorch. However, he recently delved deeper into MLops.
Michał Affek
Embedded Machine Learning Researcher at DAC.digital. He is currently enrolled in an industrial PhD programme at the Gdansk University of Technology. His main interests are remote sensing (processing done specifically on satellites), machine learning algorithms for edge devices, and parallel computing.
Machine Learning Technologies We Use
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FAQ
No. Machine learning, an application of artificial intelligence, is built to access data from and write predictions into the MES, ERP, or quality system already running your manufacturing operations. Incorporating machine learning into an existing production process means the model adds a prediction, a flagged defect, a maintenance alert, a demand forecast, to a workflow your team already uses. That’s process automation applied to one decision, not a replacement for the manufacturing execution system, ERP, or the manufacturing processes built around it.
It depends on the prediction you want. Predictive maintenance needs historical and real time data from machine sensors (vibration, temperature, run hours) alongside a record of past equipment failures. Quality control needs images or video from your existing cameras or inspection stations, including enough training data of both good and defective parts for the model to identify patterns reliably. Demand forecasting needs historical order, production, and if possible seasonality data, to predict consumer demand accurately. In every case, the model needs large volumes of your own manufacturing data to work well, and once forecasting is in place, it naturally extends into optimized inventory management too. If none of this exists yet in a usable, structured form, the first project is building that data pipeline, not training a model, and we’ll tell you that upfront.
A scoped pilot, run on your own data with a clear go/no-go point, typically comes before any full rollout. The exact timeline depends on how ready your data already is: a manufacturing facility with clean sensor logs and labelled defect images moves faster than a manufacturing plant that needs data collection built first. Across the manufacturing sector, this stepped approach is usually a faster route to real results than treating machine learning as one big digital transformation project on day one.
In most cases, yes. Predictive maintenance and quality control models are usually built around whatever sensors or cameras already exist on the line for real time monitoring, since installing new hardware first adds cost and delay. Predictive maintenance built on your existing sensors can meaningfully reduce equipment failures and maintenance costs. McKinsey research on manufacturing analytics found predictive maintenance typically cuts downtime by 30 to 50 percent, cost savings that come from your own data, not new hardware. On the quality control side, catching defects before they reach the customer lowers scrap rates and protects both product quality and customer satisfaction. We assess your existing setup during the free consultation before recommending any new equipment.
Machine learning is the underlying technology, built using approaches like supervised learning or neural networks depending on the problem. Quality control, predictive maintenance, demand forecasting, and process control, including root cause analysis for issues like quality drift or energy usage spikes and production process optimization, are the specific applications built on that technology on this page. Our Computer Vision and AI for technical drawings and CAD file analysis pages go deeper into two of those applications.
Every model is trained on your data, so the underlying work is custom. That said, our machine learning solutions are built from proven components and existing model architectures rather than researched from a blank page for every manufacturing industry client. When an off-the-shelf tool already covers most of what you need, we’ll say so instead of pitching a custom build. Most manufacturers see the strongest production efficiency gains from a custom model once the off-the-shelf option no longer fits their specific process.
For some companies, yes, especially if machine learning will become a permanent, growing part of how you run production, and if hiring specialized data scientists in-house is realistic for your team. Many manufacturers move toward this once they’re further along the path to smart factories and want that capability owned internally. If you’d rather build in-house capability with our team augmenting yours during the ramp-up, see our Team Augmentation Services.
Contact us!
Send us an email: [email protected]