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MES Notes – Why Excel Became the Default MES (and Why It Must Change)
Most Common MES in the World
The most widely used MES globally is still Excel
Not SAP
Not Industry 4.0 platforms
Not AI systems
Excel became the unexpected default tool on production floors
Why Excel Became the Default MES
Excel was not designed as an MES, but it filled operational gaps.
Reasons Excel won:
Universal access across organizations
Requires little or no training
Highly flexible and customizable
Easy to adapt to any workflow
Helped teams work around:
Slow MES rollouts
Expensive enterprise systems
Complex implementations
Summary: Excel solved many past operational tracking problems.
Why Excel Is No Longer Enough
Factories and shop floors have evolved, but Excel has not.
Modern production environments generate real-time execution events, such as:
Batch processing events
Operator actions
Machine stoppages
Quality alerts and flags
Material movement tracking
Limitation:
Excel records rows of data
It captures what happened
It does not reliably capture why it happened
Execution Entropy (Spreadsheet Risk)
When execution is managed through spreadsheets, data reliability declines.
Typical consequences:
25–40% data loss per shift
Missing timestamps
No enforced workflows
Increased rework
Slower decision making
Compliance challenges
Loss of operational insights
Concept: Spreadsheet-driven execution leads to “execution entropy”.
What Excel Cannot Do for Execution
Excel is not built for real-time manufacturing execution.
Excel cannot reliably provide:
Live event capture
Strong audit trails
Structured shift handover workflows
Safe multi-user transactions
AI-ready structured execution data
Key point:
Excel is good for analysis, but risky for execution control.
Forces Driving the Move Away from Excel MES
1. Modular MES Platforms
Start small and scale gradually
SaaS-based deployment
Lower upfront cost
Faster implementation
2. Global Competitive Pressure
Digitized factories are advancing faster
Especially in highly automated regions
Spreadsheet-driven plants fall behind
3. AI and Data Quality Reality
AI systems require clean structured data
Poor execution data causes AI failures
High-quality execution data enables AI success
Final Decision Framework
Use Excel for:
Analysis
Reporting
Data crunching
Modeling
Do NOT use Excel for:
Production execution control
Real-time shop floor tracking
Compliance-grade records
Workflow enforcement
Core Takeaway
Excel is powerful for analysis
Excel is unsafe for production execution
Modern factories require:
Real-time event capture
Structured execution data
Workflow discipline
MES platforms that reduce execution entropy
