Artificial intelligence is becoming an essential component of today's manufacturing. It can be used for predictive maintenance, quality control, production monitoring, inventory management, and operational analytics.
But there is one important thing behind all these applications:
AI requires good data to deliver good insights.
In manufacturing companies, information can come from various sources, including industrial sensors, machines, RFID, production software, maintenance, and quality systems. Collecting this information in a convenient format can be one of the main obstacles when developing industrial AI solutions.
Manufacturing Data Challenges
Typically, a manufacturing facility can have a variety of systems running simultaneously:
- Industrial sensors
- Production equipment
- MES/ERP platforms
- RFID readers/tags
- Quality systems
- Maintenance databases
- Operator reports
- Legacy systems
Information stored in each of these systems can be stored in a different way. Names of equipment can differ in different databases; timestamps may not coincide; some data can even be collected manually.
These issues can become critical for AI-based applications.
The problem here is not collecting data.
It is making sure that collected data can be reliably interpreted.
The Importance of Data Quality
AI applications use historical and current data to discover patterns and draw conclusions.
If the source data is missing, has duplicates, false information, or different labeling – then generated analysis won't be as valuable as expected.
For instance, let's imagine that we have an AI solution monitoring production equipment. In case if maintenance data will not be correctly linked to the machine, it may become complicated to figure out whether the changed behavior of the equipment is caused by maintenance actions or not.
Good data connections bring additional context.
Context Enhances Practicality of Industrial AI
Manufacturing data is rarely isolated from other factors.
Reading of a sensor can depend on:
- Machine adjustment
- Product changeover
- Maintenance actions
- Environmental factors
- Changes in production parameters
- Variability of operation
An AI-based solution analyzing only one stream of data may consider an ordinary event as an anomaly.
Combining several sources of data brings a broader view.
This is how Industrial IoT, RFID, BLE, edge computing, and AI-based analytics can work in concert to create operational transparency. PowderForge AI is a solution focused on connecting workforce, tooling, inventory, production flow, and traceability data within powder metallurgy.
From Raw Data to Actionable Insights
An effective industrial AI architecture could be likened to a pipeline:
Machines & Sensors
↓
Data Collection
↓
Data Integration
↓
Data Processing
↓
AI / ML Analytics
↓
Operational Insights
↓
Human Decision
All the stages are equally important.
Incomplete collection means there will be less data for AI to process. Poor integration may mean that some important information gets lost. Unclear presentation of the final insights makes it hard for workers to use them.
Therefore, AI success is not just about the model itself but the whole pipeline.
Not Only Technical Infrastructure Matters
But technical infrastructure is not the only thing to consider.
Manufacturers should develop processes of data management.
Questions manufacturers need to ask themselves could include:
- What is the source of truth?
- Who is responsible for data management?
- How do you deal with mistakes?
- How do you identify equipment and materials?
- How do you preserve the historical data?
- Who can have access to sensitive operational information?
Developing data governance processes can simplify AI implementations.
Begin With an Operational Problem
It is not necessary to change all the data-related processes before applying industrial AI.
It would be much more convenient to focus on some well-defined operational problems at first.
For instance:
Problem: Downtime on critical equipment.
Data: Machine sensor readings, history of maintenance, operating conditions.
Application of AI: Pattern recognition associated with abnormal equipment behavior.
Result: Earlier warning to the maintenance team for further investigation.
This approach will make evaluation of the effect produced by the AI much simpler.
Human Expertise is still Important
Neither excellent data nor sophisticated AI models can remove the need for professional manufacturing people.
Engineers, operators, maintenance teams, and specialists in quality control know many things about the manufacturing environment which are not included in datasets.
AI can detect some patterns.
People can judge whether this pattern makes sense for the specific manufacturing environment.
Combination of human expertise and machine intelligence is the key to practical industrial AI.
Preparing for AI Implementations in Manufacturing
Manufacturers aiming at industrial AI should take several aspects into account.
Good quality data, appropriate infrastructure, processes, and human involvement are all needed for successful AI adoption.
Industrial AI is all about converting operational information into actionable intelligence.
The goal is not collecting the most data possible.
The idea isn’t to have the most data possible collected.
The idea is to have the right data collected, connected well, and used for making the best decisions possible.
For developers and engineers that are working on the Industry 4.0 framework, data quality isn’t something that’s just an issue at the backend of development. It is one of the cornerstones of industrial AI.
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