Make knowledge accessible. Understand processes better.
Reliable information and quick orientation matter on the shop floor. AI can help unlock operational knowledge and support more focused investigation of unusual patterns in process data.
Where do your teams lose time between a fault report, the search for information and finding a solution?
USE CASE EXAMPLES
How AI could help in everyday work.
Illustrative scenarios. Requirements and value are assessed for your specific process.
01
KNOWLEDGE ASSISTANCE
Support for troubleshooting
THE STARTING POINT
A machine stops. The answer may be buried in manuals, old service tickets or the experience of individual colleagues.
A POSSIBLE APPROACH
AI-assisted search makes approved instructions and previous fault reports easier to access. Answers point to their sources and help the team identify possible next checks.
WHAT YOU NEED
Up-to-date machine documentation, approved fault and maintenance reports, clear equipment references and defined access rights.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
RAG assistant · Semantic search
What does the AI actually do?
AI interprets the incident description, retrieves similarly worded past cases and summarises relevant guidance with sources.
How the solution works
01
Set context
Machine, error code and operating state constrain retrieval.
02
Retrieve knowledge
A search index matches keywords and meaning; an LLM drafts a suggested check from the returned passages.
03
Keep evidence visible
Sources, revisions and open questions accompany the response.
What value can it deliver?
Less searching and easier access to recorded experience can shorten expert investigation.
A concrete example
An informal description links to a relevant manual passage and a reviewed historical case.
Technology, setup & operation+
A pretrained LLM and maintained index support a pilot. Evaluate real incident questions, correct asset selection and faithfulness to the source.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
One equipment family, a defined set of documents and typical troubleshooting questions from daily operations.
HOW TO ASSESS VALUE
Compare search effort and time to an initial technical assessment. Evaluate the relevance and usefulness of results with the maintenance team.
Qualified personnel approve checks and interventions in line with applicable operating and safety procedures.
02
DATA ANALYSIS
Identify unusual process patterns
THE STARTING POINT
Deviations can develop across several process parameters. Individual thresholds and separate reports often reveal only part of the picture.
A POSSIBLE APPROACH
Statistical methods and machine learning can flag unusual patterns. Process owners review these signals in the context of material, batch, equipment and operating conditions.
WHAT YOU NEED
Time-aligned process and condition data, operational events and sufficiently representative data for typical operating conditions.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Anomaly detection · Unsupervised machine learning
What does the AI actually do?
The model flags unusual combinations of process values even when individual readings remain within limits.
How the solution works
01
Separate operating states
Synchronise time series and distinguish products, recipes and machine states.
02
Fit a reference model
A method such as Isolation Forest scores unusual feature combinations relative to suitable reference data.
03
Review signals
An anomaly score prioritises time windows, shown with their parameters and recorded events.
What value can it deliver?
Earlier investigation signals and less manual scanning. False alarms and useful warning time determine practical value.
A concrete example
Temperature and pressure each remain acceptable, but their joint pattern differs from the usual behaviour for the recipe.
Technology, setup & operation+
No LLM is required. Representative reference data, expert feedback and monitoring of operating changes are essential. An anomaly score is not a defect probability.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Investigate a recurring process issue on one machine and initially evaluate the signals in observation mode.
HOW TO ASSESS VALUE
Assess the relevance of signals, false alarm rates and additional insights compared with existing analysis.
A statistical anomaly is a hypothesis for investigation. Any causal relationship requires technical validation.
03
DOCUMENT ASSISTANCE
Structure shift handovers
THE STARTING POINT
Information about shifts, faults and actions is spread across free-text notes, emails and different repositories.
A POSSIBLE APPROACH
AI can structure existing entries, organise them by equipment and topic, and prepare a draft shift handover.
WHAT YOU NEED
Accessible shift logs, consistent equipment names and an agreed structure for reports and actions.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Generative AI · Structured summarisation
What does the AI actually do?
AI extracts events, actions, owners and outstanding items from free-form shift notes.
How the solution works
01
Link entries
Preserve and validate time, asset and shift references.
02
Populate fields
An LLM maps statements to a fixed handover schema with links to the original entries.
03
Prepare handover
Collect unresolved actions for shift approval.
What value can it deliver?
Less writing and compilation effort; more complete handovers can reduce follow-up questions.
A concrete example
“Pump noisy again, maintenance informed” becomes an observation and an open action, without implying completion.
Technology, setup & operation+
A pretrained LLM can support the initial pilot. Assess omissions, incorrect assignments and unsupported additions.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Trial an agreed handover format on one line with a small team.
HOW TO ASSESS VALUE
Evaluate handover completeness, preparation time and follow-up questions using actual shift handovers.
The responsible shift reviews and confirms the summary. Unresolved items remain visible.
MY CONTRIBUTION
Work out together what is worth pursuing.
I help you turn an interesting use case into a clear, assessable business task.
Structure needs and objectives with business functions and IT
Clarify value, requirements and open questions
Prepare a basis for decisions and define a possible pilot scope
Support alignment with suitable implementation partners
FREQUENTLY ASKED QUESTIONS
Before taking the first step.
Is a language model always the right choice? +
No. Language models can support knowledge search and text-based tasks. Statistical models or other machine learning methods are often more suitable for process patterns and anomaly detection. The task determines the method.
Where is a sensible starting point? +
Start with a clearly defined problem and a team able to assess the value in day-to-day work. Existing data, current ways of working and a measurable objective provide the foundation.
FROM A USE CASE TO THE NEXT STEP
Which idea would you like to make concrete?
Together, we clarify the starting point, possible use cases and expected value, creating a sound basis for your next decisions.