By Eben & Kristi van Tonder
16 November 2025

Introduction

In 1948, Claude E Shannon published A Mathematical Theory of Communication, a work that defined how information moves, how noise distorts it and how structure restores clarity. Although developed for telecommunication networks, Shannon’s principles apply to any environment where information must pass through imperfect channels. Meat factories are such environments, where entries, weights, QC steps and stock movements must be captured reliably under pressure.

GENAU, the factory-management method developed by Kristi and Eben van Tonder, is based on manual registers, AI-supported extraction, standardised numbering logic and disciplined human review. When viewed through Shannon’s lens, GENAU emerges not simply as an operational system but as a complete communication architecture that handles noise with mathematical discipline.

Understanding this link strengthens the system, clarifies its advantages over conventional factory platforms and provides direction for further improvements.

Claude Shannon and the birth of information clarity

Shannon’s insight was that information becomes measurable when symbols and their structure are precisely defined. Once an alphabet is fixed, uncertainty can be quantified, and communication can be analysed systematically. His model, although conceived for telephone circuits, remains the blueprint for all modern information transfer.

To set the stage for GENAU, it is useful to recall Shannon’s universal communication model, which applies to everything from radio signals to factory paperwork:

• A source that originates the message
• A transmitter that formats it
• A channel that carries it
• Noise that distorts it
• A receiver that reconstructs the message
• A destination that uses the information

Shannon showed that all channels have limits, noise is unavoidable, and reliability depends on intentional coding, redundancy and error correction.

GENAU as a Shannon-style communication system

GENAU follows a simple but disciplined structure: operators write in standardised registers, pages are photographed, AI extracts the data, validations highlight anomalies, and human reviewers resolve uncertainty daily. This practical workflow maps directly onto Shannon’s architecture.

To illustrate this, each part of GENAU fits naturally into Shannon’s model:

• The source is the operator recording crates, batches, QC notes and movements.
• The transmitter is the layout of the register, which defines the allowed symbols.
• The channel is the scanned or photographed page.
• Noise arises through handwriting, smudging, blur, missing entries or mistakes.
• The receiver is the AI that decodes the images into structured rows.
• The destination is the Master Workbook and the decision-makers who use it.

Recognising this alignment makes it possible to evaluate GENAU using Shannon’s scientific principles rather than convenience or preference.

Evaluating GENAU through Shannon’s principles

Shannon’s framework offers a clean, objective way to test the strength of GENAU. Each principle reveals one layer of reliability. GENAU already satisfies several of them and can be refined further.

The first principle is the clarity of the alphabet and the code. Shannon argued that high reliability begins with a clearly defined symbol set. GENAU meets this condition through its numbering logic and register structure. It achieves this through:

• Unique RM, BCN and FG serial sequences
• Standardised fields such as species, cut, weight and date
• QC documentation integrated into the same books as stock movements

The second principle is the handling of noise through redundancy. Shannon showed that noise cannot be removed, but its effects can be limited through structured duplication. GENAU implements this by keeping each piece of information in multiple coordinated forms. The redundancy includes:

• The hardcover register
• The photographed page
• The AI extraction result
• Validation rules
• Daily human verification
• Weekly stock takes

The third principle is error detection and correction. Shannon insisted that errors must be exposed early and corrected systematically. GENAU already performs this through AI validation and human review, and the system can be strengthened further. Effective measures include:

• Introducing check digits on crate and batch codes
• Flagging impossible or missing values automatically
• Ensuring that every flagged line is resolved by the reviewer

The fourth principle is channel capacity. Shannon proved that every system has a limit, and reliability declines when this limit is exceeded. In GENAU, capacity can be monitored through simple daily metrics. This includes:

• The number of pages photographed
• The number of lines extracted
• The number of lines flagged
• The time reviewers spend per correction

The fifth principle is the reduction of uncertainty. Shannon defined information as a decrease in uncertainty. GENAU can adopt this directly by giving each record a certainty status. Tracking these statuses gives a clear picture of data quality. This is done by identifying:

• Confirmed entries
• Suspect entries
• Estimated entries
• Missing entries

Key improvements implemented today

We strengthened GENAU in three core areas directly aligned with Shannon’s information principles. All upgrades are now active in the system.

1. Check digits added to all RM, BCN and FG serial numbers

GENAU now uses check digits across all raw material, batch companion and finished goods serials.
This upgrade reinforces the coding layer and prevents incorrect serials from entering the system unnoticed.

It delivers:

• Immediate detection of miswrites and misreads
• Prevention of serial errors moving between departments
• Faster and more accurate human verification

2. Daily measurement of channel capacity

A new daily capacity log has been introduced to protect system integrity as data volumes increase.
GENAU now records:

• Number of pages entering the system
• Extraction volume
• Errors flagged
• Minutes required to resolve each issue

This quantifies the workload and keeps the review process within sustainable limits.

3. Introduction of an uncertainty budget

Uncertainty is now measured rather than sensed.
The system tracks unresolved entries and sets target levels for daily reduction.
This provides a clear picture of the stability of each information channel.

The uncertainty budget monitors:

• Proportion of unconfirmed records
• Daily reductions in uncertainty
• Impact of stock takes
• Differences in stability between departments

Comparison with commercially available factory systems

Most commercial factory systems rely on barcode scanning, industrial terminals and ERP integration. They assume perfect scanning conditions and uninterrupted infrastructure. Shannon’s framework helps explain why these systems break under real-world conditions and why GENAU thrives.

The first major difference lies in data capture. Standard systems depend on expensive hardware and stable electricity. GENAU begins with structured manual registers supported by AI extraction. This gives GENAU meaningful advantages:

• It functions during power cuts or network failures
• It provides a physical audit trail
• It requires minimal training for operators
• It works in both advanced and limited-infrastructure plants

The second difference concerns noise. Barcode systems avoid some errors but introduce new ones when labels fall off or devices malfunction. GENAU accepts natural noise but counters it with redundancy and human review. This results in:

• Predictable, correctable forms of noise
• Fewer silent or hidden errors
• A transparent correction process

The third difference involves quality control. Commercial systems often treat QC as a separate workflow. GENAU embeds QC documentation directly into registers and batch companions. This results in:

• Unified visibility of stock, yields and QC
• Complete traceability for each batch
• A stronger position for audits, certification and export markets

The fourth difference is environmental suitability. Commercial systems work best in ideal, highly managed settings. GENAU works across the spectrum. This makes it suitable for:

• African plants with variable infrastructure
• Austrian and German plants where audit depth is essential
• Any environment needing clarity and resilience rather than fragile automation

Conclusion

Shannon’s 1948 paper offered a universal method for handling information in the presence of noise. GENAU follows the same logic by using a clearly defined alphabet, robust channel, structured redundancy and disciplined correction. With additions such as check digits, capacity metrics and an uncertainty budget, GENAU becomes a mathematically grounded communication system for real factory environments.

By linking the GENAU method to Shannon’s principles, it becomes clear why the system performs reliably where other solutions fail, why it adapts across continents and why it brings unusual strength in quality control and traceability. Shannon provided the mathematics of reliable communication. GENAU applies those principles on the factory floor.

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