Insights · 10 min read
A practical look at where AI pays off in manufacturing, from predictive maintenance to visual quality inspection, what a build needs, and what it costs.
By GGP Editorial
A factory floor is one of the few places where AI has a business case you can read straight off a spreadsheet. Every hour a machine sits idle when it should not, every part that comes off the line defective, every pallet of raw material bought at the wrong time is a cost with a number attached. That is why manufacturing keeps coming up when we talk to founders and operations teams about where AI is worth the effort.
This guide covers the three uses of AI in manufacturing that actually carry their own weight, what a build needs, and what it costs. It is written for plant managers, operations directors, and the companies building software for manufacturers. Not for data scientists.
I write this from the vendor side. GlobeSoft is a software company founded in 2018 with 40-plus engineers and more than 300 delivered projects, with clients across Brazil, South Africa, Singapore and the US. We build IoT and data systems for industrial clients, and the AI that sits on top of them. What follows is what we see again and again.
Most of the AI talk in manufacturing is noise. The work that survives contact with a real plant falls into three buckets.
Predictive maintenance. Equipment fails on a schedule nobody can see. Vibration, temperature and current draw shift before a bearing seizes or a motor burns out, but a person watching a gauge catches it too late. A model trained on that sensor history flags the drift early so maintenance happens before the line stops.
Visual quality inspection. A camera does not blink and does not get tired. Surface defects, missing components, wrong labels, bad welds. Computer vision finds these faster and more consistently than a person at the end of a long shift. This one pays for itself quickly on high-volume or high-rejection lines.
Production planning and demand forecasting. Getting the schedule and the inventory roughly right saves more money than any single machine. It is also the hardest of the three to build, because it depends on clean historical data, and it is usually a second or third phase rather than a starting point.
I cover the first two in detail because those are the ones a plant should start with.
The reason to start here is the math. Unplanned downtime costs a plant a real, known amount per hour, and one avoided failure can justify the whole project. A single bearing caught a week early often saves more than the software costs.
What it needs is less glamorous. Vibration sensors on rotating equipment, temperature and current readings, and a history of past failures to learn from. The model does not have to be exotic. In practice, anomaly detection that flags "this looks different from normal" catches most failures, while a true remaining-useful-life prediction takes far more data and time. Start with anomaly detection and add the fancier version later, once you have the failure history to train it.
The failure mode to plan for is false positives. A model that cries wolf gets ignored within a month. Plan for a maintenance engineer who reviews alerts and tunes the thresholds to what the team can actually handle. A system the crew ignores is worse than no system, because it burns trust.
Manual inspection has a hard ceiling. People are consistent for about an hour, then fatigue sets in and defect rates drift. Computer vision inspection replaces the repeatable part of that job. It works well on surface defects, assembly verification, packaging and label checks, and weld or solder quality. It does not replace the judgment calls.
The build is mostly engineering rather than data science. Cameras and lighting have to be set up so the defect is actually visible, which is often the hardest part of the whole project. Inference runs on an edge device next to the line so a decision takes milliseconds, not a network round trip. A human review loop for uncertain cases keeps the system honest and keeps operators in control of their own line.
If this is new ground, our guide to computer vision development goes into the camera, lighting and model setup in more detail.
Factories usually have the data. They just do not have it in a usable shape. Machines ship with sensors and PLCs, and a SCADA or historian system stores the readings, but the formats, naming and gaps are inconsistent. A build spends a large share of its time getting that data out, cleaned and flowing into one place before any model is trained.
Treat connectivity as first-class work. Reading a PLC, pushing readings into a time-series store, and handling the gaps when a sensor drops offline are each real engineering tasks, and each one can stall a project for weeks if it is assumed to be easy. If the data layer is new, it is worth planning it deliberately. Our guide to data platform development covers the architecture, and the device side is covered in our guide to building an IoT device management platform.
A model that predicts a failure has to run where the decision is made, which is on the line. Training and retraining usually happen in the cloud, where the compute is cheap. Many plants insist the data stays on-site, so a build often splits: inference on an edge device or a local server, training in the plant or a private cloud.
The split matters for cost and for security, and it is decided early. There is no single right answer. It depends on the plant's data policy and on how fast a decision has to be. A quality camera making a go/no-go call in 50 milliseconds cannot wait on a cloud round trip. A weekly maintenance report can.
Off-the-shelf condition-monitoring and quality-inspection tools cover standard equipment well and cost less to start. You build instead when the equipment or process is unusual, when you need the model and the data under your own control, when it has to integrate deeply with an existing MES or ERP, or when you are a company selling the software itself and the product is your business.
The test is the one I use everywhere. Write down the three things your process needs that off-the-shelf tools do not handle. If that list is real and the workaround costs you hours every week, a build starts to make sense. If the list is empty, buy the tool and put the budget into the data cleanup instead.
Cost in these projects is team size multiplied by time, plus the data work nobody budgets for. The main drivers:
As a rough planning figure, a single-asset pilot with one use case usually lands in the tens of thousands of dollars. A multi-line system across predictive maintenance and visual inspection runs into six figures. Those are ranges, not quotes, and they assume a cost-efficient team. The only way to get a real number is to define the assets, the failure modes and the integrations first, then get a fixed-scope estimate. Our guide to estimating a custom software project shows how to do that without padding.
Start with one line, one asset and one failure mode. A plant that tries to predict everything at once builds a research project, not a tool. Pick the asset that causes the most unplanned downtime, define the single failure you want to catch, and set the success number before you start. Reduced downtime hours, fewer scrapped parts, whatever the plant already tracks.
Lock data access in week one. The fastest way to stall a pilot is to discover the historian vendor wants a fee for the connector, or the data sits in a proprietary format nobody can export. Get a raw data sample in hand before you commit to a scope.
If the pilot pays off, expand to the next asset and the next use case. The pattern that works is boring: prove it on one machine, then roll it out. If the pilot does not pay off, you spent a small amount to learn that the data or the use case was wrong. That is still cheaper than betting the factory.
How long does a first build take?
A single-asset predictive maintenance pilot usually lands in two to three months. A multi-line system with visual inspection and MES or ERP integration runs six to twelve months. The data cleanup and the integration work stretch the timeline more than the model itself does.
How much does it cost?
A scoped pilot is usually in the tens of thousands of dollars. A full multi-line system runs into six figures. The number depends on how many assets, how hard the data is to reach, and how deep the MES or ERP integration goes.
Do we need data scientists on staff?
For a pilot, no. A good development team brings the modeling expertise. What you do need is a maintenance or process engineer who knows the equipment and can review the alerts. That person matters more than how sophisticated the model is.
Cloud or on-site?
It depends on the plant's data policy and how fast the decisions have to be. Inference on the line is standard. Training and retraining usually sit in the cloud unless the plant requires the data to stay on-site.
Will it work with our existing MES and ERP?
It has to. A prediction that does not turn into a work order or a quality hold is just a notification. Plan the MES and ERP integration as part of the scope, not a bolt-on. If the ERP itself is aging, our piece on ERP for manufacturing covers the modernization question.
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Planning AI for a manufacturing line, or building software for manufacturers? Tell us what you make, where the downtime or scrap is, and what your systems already capture. We can help you pick the first use case, scope the data work, and put a realistic timeline and budget against it.
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