BI Analysis and Diagnosis
1. From reporting to industrial analysis
Prodia does not stop at displaying charts.
It organizes manufacturing analytics around operational questions such as output, quality, takt, fault, SPC, and OEE, then continues into diagnostic reasoning and investigation guidance.
2. Typical analytical domains
| Domain | Typical focus |
|---|---|
| Output | line output, process throughput, shift comparison, trend |
| Quality | defect distribution, bad rate, FPY, quality loss |
| Takt and balance | bottleneck process, station fluctuation, line balance |
| Fault and downtime | downtime duration, Pareto, MTBF, MTTR |
| OEE | availability, performance, quality decomposition |
| Process parameters | SPC, parameter drift, abnormal window, relationship analysis |
3. Analytical depth
Prodia commonly moves across four layers:
| Layer | Core question | Typical methods |
|---|---|---|
| Descriptive | What happened? | query, aggregation, ranking, distribution |
| Diagnostic | Why did it happen? | comparison, correlation, Pareto, root-cause clues |
| Predictive | What is likely to happen? | risk signals, deterioration trend, threshold judgment |
| Prescriptive | What should be done? | next checks, operational suggestion, optimization path |
4. Typical output structure
When Prodia performs BI analysis and diagnosis, it usually returns a combination of:
- key metrics
- comparison results
- anomaly concentration points
- likely factor ranking
- recommended next checks
This helps users move directly from seeing a number to deciding what to investigate next.
5. Why this differs from general BI
General BI tools often require users to:
- know where the data lives
- know which chart to build
- know how to interpret the result
- know how to continue into diagnosis
Prodia compresses this path by making analytics queryable through industrial language and by keeping diagnosis linked to the same result context.
6. Best-fit problems
This capability is strongest when teams need to answer questions such as:
- which object changed the most
- where the main loss comes from
- which abnormal factor deserves first attention
- how one indicator differs across line, process, station, shift, or period