Industry
Solutions for discrete manufacturing
For production environments in which rejects, downtimes and process deviations need to be understood more quickly in the context of the line, station and product.
Contact usWhere data-based analysis helps
Typical symptoms in discrete manufacturing
In discrete production environments, isolated key figures are often not enough. Typical symptoms are high levels of rejects without a clear cause, unstable cycle times, recurring stops or quality problems that cannot be clearly assigned to a station.
Solutions that not only visualize production data, but also put it into a technical context for root cause analysis and prioritized measures, make sense.
Consider line, process and part together
Analysis context
Between the first symptom and the actual measure is the question of how production events, process parameters and quality data can be evaluated together.
Analysis context
Evaluate production problems in the context of line, process and part
Meaningful evaluations are created where production events, process parameters and quality data can be viewed in a common technical context.
- Line and station-related view of throughput and quality
- Product and part reference for root cause analysis and traceability
- Basis for prioritized measures instead of isolated individual key figures
Focus of the analysis
These issues often take center stage in discrete manufacturing environments.
Line performance
Throughput & cycle
Detect deviations in cycle time, waiting times and station cycles.
Quality context
IO / NIO / Rework
Link quality events with process data and product reference.
Product reference
Part and batch history
Consider each produced part in the context of station, process and limit values.
Root cause analysis
Error, process, progression
Evaluate recurring patterns not in isolation, but in the production process.
Typical path from conspicuousness to action
The solution is usually not used as an isolated dashboard, but as an analysis environment that enables teams to take robust measures step by step.
01Scope
Narrow down lines, products and questions
At the beginning, it is clarified which lines, stations and quality or availability issues are to be considered.
02Data basis
Capture production data in a technical context
Control, sensor and product data are linked in such a way that they can be assigned to lines, processes, products and shifts.
03Analysis
Analyze and prioritize deviations
Teams use the evaluations to narrow down bottlenecks, quality losses or recurring error patterns.
04Expansion
Supplementing measures and connection modules
Depending on requirements, traceability, process analysis, data stories or AI modules are added on the same basis.
Basis for reliable production analyses
Typically required data
Existing control and process data is usually sufficient for production-related root cause analysis if it is combined correctly.
Relevant data types
These types of data are particularly important in discrete production environments in order to evaluate deviations in a traceable manner.
Status and event data
Statuses such as processing, automatic operation, stops or station events form the basis for availability and sequence analyses.
Process parameters
Force, displacement, temperature, torque, tightness or other quality-relevant values make process behavior technically evaluable.
Product and identification data
Product IDs, batches, workpiece carriers or variants allow the linking of process history and quality per part.
Processing and test results
IO, NOK and rework information link quality events with the underlying production context.
Function modules for different tasks
Matching modules on the same database
Depending on the line structure and data situation, different modules can be combined to specifically cover process, quality and product issues.
Modules for analysis and traceability
These functional areas can be combined within the same platform.
Production analysis
For output, cycle times, quality curves and the structured evaluation of line and station data.
Process analysis
If real process paths, variants and loops are to be traceable across several stations.
Product tracking
For parts histories, traceability and access to product-related process and quality data.
Alarm analysis
For the structured evaluation of warnings, error codes and fault patterns during operation.
Demo
Experience Bytefabrik live
Arrange a demo to get to know the platform, analysis and AI functions based on your questions.