From Spreadsheets to AI Shopfloor Supervisors—How Manufacturing Decisions Will Actually Get Made
Manufacturing Data Reality Check: 2025
Let’s not sugarcoat it: in 2025, “Business Intelligence” in manufacturing often meant printing CSVs from SAP or 1C, gluing together downtime logs from the MES, and reformatting defect reports from Siemens Teamcenter or Oracle EBS. Nobody knew whose numbers were right. The production planner’s OEE was 79.2%, the plant manager’s was 84%, and Quality had their own truth—usually on a whiteboard.
Data sources?
- SAP for inventory, procurement, finance
- MES (Siemens, Honeywell, Rockwell, Schneider, 1C MES, Oracle MES) for production data
- OSIsoft PI for real-time sensor/line data
- SCADA for equipment status
- WMS for warehouse flows
- Siemens Teamcenter for engineering changes
- Tableau or Power BI for dashboards (at least the brave ones)
It was fun—until you had to explain last month’s yield dip to HQ.
The Five Stages of BI Maturity in Manufacturing (2035 Edition)
1. Scattered Reporting
Each department—Production, Maintenance, Quality, Supply Chain, Finance—runs its own numbers.
- Production uses MES for batch data and OEE
- Maintenance pulls unplanned downtime from SCADA or OSIsoft PI
- Quality tracks defect rates in Oracle, 1C, or even Google Sheets
- Warehouse team tracks inventory turns in WMS
- The only thing everyone agrees on: tomorrow’s daily meeting will overrun by 40 minutes
Example, 2025:
Quality says defect rate is 1.3%. MES says 1.1%. No one knows why, and nobody’s sure who to believe.
2. Centralized Reporting
Finally, someone connects SAP, MES, SCADA, and WMS to Power BI, Tableau, or Qlik.
Now everyone sees the same OEE, downtime, scrap rate, order fulfillment time, and inventory turns—at least, until a last-minute spreadsheet override.
Example, mid-market plant, $110M revenue:
Consolidating order-to-ship times (SAP), yield (MES), and unplanned maintenance (OSIsoft PI) cut weekly reporting work from two days to three hours. But every plant manager still needs a cheat-sheet for what “OEE” means here.
3. Data Storytelling Platform
Dashboards start to explain:
“Yield dropped 2.5% in Assembly Line 2, primarily due to 18 equipment failures (OSIsoft PI logs) and 6 engineering change orders (Teamcenter) in the last week. See maintenance logs and quality tickets for root causes.”
Key tools:
- Automated anomaly detection in GE Predix, OSIsoft PI
- Natural language narratives in Tableau, Power BI, or Sisense
- Integrated data from MES, SAP, WMS, Oracle, Teamcenter
Client value:
A global auto parts supplier reduced manual root-cause investigations by 70% and cut downtime costs by $520,000 per year after launching this level of BI.
4. Insight Platform
Now BI tells you what’s happening, why, and even what’s likely next.
“Scrap rate up 1.7% in Machining. Root causes: new supplier batch (SAP procurement data), increased changeover frequency (MES), operator learning curve (training logs). Predict further impact if no action taken.”
Specifics:
- Sources: SAP, MES, OSIsoft PI, Teamcenter, WMS, SCADA
- Metrics: OEE, downtime, scrap rate, yield, throughput, order fulfillment time, mean time to repair (MTTR), cost per unit
- Outputs: Automated insight tickets for maintenance, quality, and production—each with $ impact
Case:
A US electronics plant flagged a spike in unplanned downtime via real-time PI System alerts, traced to a batch of faulty servos. Fixing the vendor issue avoided $700,000 in lost output.
5. Action Platform / The Rise of the AI Decision Making Assistant
By 2035, every plant supervisor and supply chain director has their own AI Decision Making Assistant.
Instead of “reacting,” you log in and see:
- “Re-sequence Production Orders 15-22—cuts changeovers by 40%, saves $350,000/year”
- “Schedule predictive maintenance for Press #7—anomaly detected, risk of failure up 11% (OSIsoft PI data)”
- “Order from Supplier B—Supplier A’s last 4 batches had 0.7% higher defect rate (SAP, Teamcenter)”
You approve or comment, and the assistant learns your preferences, plant context, and cost trade-offs. All decisions, signals, and outcomes are auto-logged for audit and continuous improvement.
Tools and integrations:
- Decision platforms: GE Predix, custom ML solutions, DataRobot
- Data: SAP, MES, PI, Teamcenter, WMS, Oracle, 1C, SCADA
- All actions and insights tracked in a centralized decision log
How Manufacturing Runs in 2035
- Every shift manager, maintenance planner, and supply chain director starts the day in the Action Platform
- AI Decision Making Assistant surfaces issues, root causes, and actions drawn from all key systems
- Data flows in real time from SAP, MES, OSIsoft PI, SCADA, Teamcenter, WMS, and even IoT sensor nets
- Plant teams spend more time fixing and improving, less time reconciling “whose numbers are right?”
Manufacturing Examples — Tangible Value
Midwestern Automotive Supplier, $220M revenue:
AI Assistant detected a rising trend in changeover downtime on Line 3. Suggested rebalancing operator shifts and automating tool setup, saving $640,000 per year.
European Food Processor:
Integrated SCADA, WMS, and SAP data; anomaly detection caught refrigeration drift before spoilage, saving $1.1 million in wasted product.
Industrial Equipment Manufacturer:
Automated insight tickets in Power BI flagged that raw material cost per unit was 7% higher in Q2, traced to a single batch. Sourcing switch saved $450,000/year.
What To Do Today (for Plants Stuck in 2025)
Find your stage:
- Are you still reconciling SAP, MES, and WMS in Excel every Friday?
- Do you trust your OEE, downtime, and yield numbers—can you explain last week’s blip in detail?
- Can everyone see the same data, or does every department have their own “reality”?
Track decisions:
- Log every major action and why it was made.
- “Changed packaging vendor—defect rate down 0.4% (SAP, Quality logs).”
- Build an audit trail—your future audits (and board meetings) will be less terrifying.
Connect insights:
- Integrate MES, SAP, SCADA, Teamcenter, WMS.
- Ask your BI team for narrative explanations—not just numbers.
Start slow:
- Build a working dashboard for one line or plant, integrating at least MES and SAP
- Add anomaly detection for downtime and yield
- Gradually expand to connect Teamcenter, WMS, and SCADA, and automate explanations
Shortcut?
Try our free BI Maturity Audit. We’ll show you your plant’s “decision journey,” highlight bottlenecks, and help you start building toward that 2035-grade AI Decision Making Assistant—now, not later.
In manufacturing, BI is no longer just “reporting”.
It’s a living digital supervisor, wired into every machine, process, and team, surfacing real, actionable insights. By 2035, your AI Decision Making Assistant will spot trends, suggest fixes, and track value in real time—freeing your teams to actually make things better, not just make reports.
Ready to retire the spreadsheet and build a factory that thinks for itself?