AI becomes more popular in manufacturing.
Predictive maintenance, computer vision, demand forecasting, process optimization, digital twins, and intelligent monitoring move from experiments to real production.
Nevertheless, just adding AI to the manufacturing process does not automatically generate value.
Where does AI actually solve the manufacturing problem, and where conventional software remains more suitable?
Where AI Creates Value
- Predictive Maintenance
Traditionally maintenance is performed regularly according to the schedule.
So, a machine will be serviced once in 30 days irrespective of its state.
AI can analyze:
Data from sensors
History of equipment
Maintenance history
Operating conditions
Failure patterns
The objective is to detect some signals that can show a future failure.
Instead of:
Every 30 days → Service machine
it is possible to perform:
Gathering data
↓
Analysis of patterns
↓
Detection of abnormal behavior
↓
Forecasting of potential failure
↓
Scheduling intervention
The value is in reducing the risk of unexpected breakdowns and unnecessary service.
Nevertheless, predictive maintenance is not magic. The predictions will be useless without good sensor and historical data.
- Computer Vision for Quality Control
Quality inspection is yet another use case.
Computer vision systems can inspect products with high speed and detect defects such as:
Damage of surface
Missing parts
Improper assembly
Problems with packaging
Labeling errors
AI will be able to inspect large volumes of repetitive inspections while specialists can deal with anomalies and complicated cases.
It makes more sense to consider it as assistance rather than try to get rid of humans in quality inspection completely.
- Production and Demand Forecasting
Manufacturing enterprises make daily decisions related to inventories, production planning, purchasing, capacity.
AI can analyse historical demand and other data to find patterns that can improve forecasting.
Better forecast can help to reduce:
Excessive inventories
Stockouts
Delays in production
Underused capacity
Emergency purchases
Nevertheless, forecast will always be just a prediction.
- Manufacturing Process Optimization
A manufacturing process can have dozens or even hundreds of parameters involved.
For instance:
Temperature
Pressure
Machine speed
Material quality
Cycle time
Environmental factors
These parameters can correlate and interact in complex ways which may not be easily understandable manually.
AI can analyze large amounts of data and find relationships between process conditions and outcomes.
This will help engineers to explore new operating conditions and find possible reasons for process variations.
The key here is to have sufficient amounts of good-quality data.
- Integrating Operational Data
Manufacturing-related data is hardly ever centralized in one place.
A usual landscape might consist of:
ERP
MES
WMS
SCADA
IoT platform
Quality system
Maintenance system
The problem usually is not in additional data collection but in data analysis.
AI can become an analysis tool for all of these platforms to reveal correlations, outliers, and relationships.
This is especially important in the context of pharmaceutical manufacturing when all types of data, including operational data, equipment details, inventories, environmental conditions, and manufacturing processes have to interoperate. (https://pharmafluxai.com/)
When AI Doesn't Make Sense
The most common mistake is the assumption that every manufacturing challenge requires AI.
Not every does.
Simple rules do not require AI
For instance:
IF temperature > threshold
THEN trigger alert
It makes no sense to substitute this rule with an AI algorithm.
A deterministic rule is:
Simpler to test
Simpler to audit
Simpler to understand
Simpler to maintain
When there is a simple rule to solve a problem - just go with that rule.
Poor Data Quality Means Poor AI Quality
An AI system depends on data.
If your data is:
Incomplete
Inconsistent
Inaccurate
Poorly labeled
Hard-to-access
Then you won't make your AI model more sophisticated fix the situation.
Quite often in manufacturing AI projects, data infrastructure is more crucial than the choice of a particular model.
Do we have the necessary data to answer this question?
Don't Build AI Without a Business Problem
Even when AI system itself is technically well built, there can be zero value for business.
Think of a dashboard, which predicts dozens of metrics in production process.
If no decisions change based on these predictions, then how did the system add any value?
The right AI system has to make a connection between technology and an outcome.
Better inspection
↓
Early defects detection
↓
Reducing waste
Value should be quantifiable.
AI shouldn't take high-stake decisions by default
Manufacturing environment may require safety, quality, regulatory, and financial considerations.
Allowing unrestricted control of critical processes to the AI system is unnecessary risk.
A more secure architecture can include:
AI detects
↓
AI analyzes
↓
AI recommends
↓
Human reviews
↓
Approved action
Depending on the specific case, different level of human involvement is needed.
The crucial thing is that the system must be designed to operate with the proper level of autonomy.
Domain Knowledge is Important for Industrial AI
AI is capable to process huge amount of data.
It doesn't necessarily understand the operational context behind this data.
Engineer knows why certain sensor data is fine.
Production manager understands why certain schedule recommendation is not feasible.
Quality expert understands that there is no defect in a product despite of the fact that certain parameters seem off.
This means that good industrial AI is not usually just:
AI + data
But rather:
AI + data + domain knowledge + operational context
Start Small
There is no need for the company to build an entire AI factory.
Rather the better way is to solve a particular problem.
Find the process where:
The reliable data is available
There is a weakness in the current process
AI system can enhance the predictions or decision making
The results are measurable
Proper human supervision is possible
The company can start from one critical machine, one inspection process, or forecasting problem.
When the results bring measurable value, it can be scaled up.
The True Question Is...
"Where do we make difficult, repetitive, expensive, or time-sensitive decisions?"
*
Answer if it is possible to make the decisions better with the help of AI.
Sometimes the answer will be AI.
Sometimes it will be traditional automation.
Sometimes it will be better data infrastructure.
And sometimes the right combination of all three solutions.
AI is the Tool, but Not the Strategy
The future of manufacturing does not have to go to those companies using the most AI.
It has to go to those companies knowing when AI can be of real benefit.
AI can aid in prediction of failures, detection of defects, process optimization, and complex data analysis in manufacturing operations.
However, the successful application of AI involves availability of quality data, clear goals, solid engineering skills, domain knowledge, security, governance, and people.
The purpose is not to make manufacturing "more AI."
The purpose is to make manufacturing more predictable, efficient, informed, and resilient.
And sometimes, it is smarter not to use AI at all.
Unexpected market changes, supply disruptions, and new customers' behaviuor can make historical patterns irrelevant.
AI should assist in decision-making, but not be considered as a truth.
