By Eben & Kristi van Tonder, 19 November 2025

A Revolution in Stock Control, Yield Accuracy and Quality Management

GENAU is built on one principle. Data follows a structure in the factory. Every stock item must have a logical integration point, which is the element that connects the item to all its relevant information. Whether it is meat, an ingredient such as salt or sugar, or even a cleaning agent, the item must be linked to a single data core in which the essential details are recorded. These details include price, weight, batch number or traceability number, production date and all other data required for control and traceability.

In the GENAU system, this integration point is a unique number. Every item that enters the factory receives this number. Every finished product that is made receives it. Every production batch that is started receives it. These unique numbers act as a collective identifier, if one wants to use the word, that connects all of these elements. After that, one only needs to use the number. The number unlocks access to all the values associated with it and makes it possible to manage the item and, at any given moment, retrieve the necessary data about the item through the use of Artificial Intelligence with a small amount of programming.

It is a simple system that does not fail. It makes every pallet, crate and box visible in all stock locations: freezer rooms, chill rooms, deboning halls and processing areas. The system begins with simple registers in the factory that are kept by hand in a register book or in Excel. Every stock item receives a unique number and every storage area has its own register. The numbers are applied with stickers on, for example, bags of salt or other ingredients, or on crates through “loop tags” or accompanying HACCP marking systems. The registers in each stock location record every number that is booked in or booked out. Nothing more than just the number needs to be written down. It is quick and simple.

The registers for IN and OUT are sent to the system every afternoon via a WhatsApp message or by email. A page with a heading and a list of numbers. The system reads what was received and updates the status of the stock location. One of the core characteristics of AI and a well-designed system is that it integrates these independent registers into a holistic whole. The processes eliminate errors through built-in checks that are continuously applied. Similar registers are used in deboning and production.

Special registers are designed for places where something happens to the stock, such as deboning, production and the spice room. In deboning, the booking-in of meat from a cold room is followed by a block test module. In production, a “batch companion” is created for every production batch. The batch companion includes every production step, with every weight before and after it has been processed through any machine, or before and after heat treatment and smoking, or before and after cooling or packaging. Every batch number is documented against every recipe. Prices are recorded with detailed costing.

The registers, block tests and batch companions are scanned at the end of the shift or photographed, and this is sent to the system. It can also be Excel files that are sent by email. The system integrates all of this data and compiles reports. Stock levels and stages of production become visible daily for every area in the factory and for every stock location. The entire factory is visible on a management dashboard. Reports are produced in Excel.

Orders that are received and orders that are delivered are integrated. The system performs the production planning. Full quality control is applied at every stage of production.

The fact that the number of every crate or box that moves out of a stock location is recorded on an Excel sheet or a hand register sounds slow, but it is faster and more robust than barcode scanning because it does not rely on electronic stability, and because a barcode never guarantees that the item will always be visible or easy to scan.

GENAU is not merely a traceability system. It is a management system that begins on the factory floor and flows without friction into data. Data becomes manageable only when people, space, equipment and information move within predictable patterns. Stock, yield, quality controls and usable data require this predictability. Without structure, numbers drift. Without data keys, data loses meaning. It anchors all activity in stable routines, controlled movement and clear number logic.

We captured the principles that Deming taught us in a single word that is easy to remember, and it forms the foundation of the GENAU system. The word is OSASS and it stands for Order, Sanitation, Segregation, Standards and Self-discipline. These five principles determine how people, equipment, crates, workflows and data move across the floor and how every action is embedded in a fixed pattern. OSASS forms the physical environment in which GENAU operates and makes it possible for data to become meaningful. Without OSASS, a factory has movement without context: crates moving around, numbers appearing with no reference point and processes dependent on subjective interpretation. When OSASS is applied, every area gains a purpose, every route a logic and every action a fixed place in the greater system. It stabilises behaviour, removes arbitrariness and creates the basis on which GENAU can measure, analyse and improve. We will later look in more detail at these five principles and how they are applied in practice.

Deming is the inspiration behind GENAU because his work builds stability in physical processes and establishes the foundation upon which a factory can operate consistently. Before we can reach data, order must first exist: in the freezer rooms, in the chill rooms, on the deboning floor and in production. Deming’s focus on variation control, standard work, predictable flow and systems that determine outcomes forms the basis of GENAU. Shannon’s work, on the other hand, is the inspiration for how GENAU structures information. He showed that data is only useful when it moves through fixed channels, with a clear signal and minimal noise. These two approaches explain why a system that integrates OSASS, numbering, registers and real-time measurement produces a factory that behaves predictably, functions stably and improves day by day. They provided the elements that enabled us to build the system, and today we can reinforce it with tools that did not exist before, namely Artificial Intelligence. On the one hand, the fundamental requirements for good factory management remain exactly the same. The flow of people and products, order in storage areas, consistent processes and stable working methods do not change. On the other hand, everything has changed because data is now accessible at a level that has never been possible. Calculations that once required advanced knowledge of probability theory and statistics can now be performed automatically. This makes complex analyses available to managers and operators that previously were within reach only for academics and specialist data scientists.

The implications are enormous!

1. Unique number as the interface between data and the factory floor

Deming emphasised that data must be linked to a stable reference point. GENAU links every dataset to one unique number. This reference is assigned to every stock item in its smallest unit: every crate of meat, every box of finished product, every bag of ingredients and every bundle of packaging. The number carries production day, weight, item number, species, cut, supplier, process history and cost. AI retrieves and combines this data instantly. Operators work with one number instead of scattered details. This stabilises movement, yield, shrinkage control and traceability.

2. Structure in every department

Consistent outputs depend on consistent systems. When equipment positioning, staffing or movement changes, measurements become unreliable. GENAU defines fixed zones, flows, routes and stable positions for stock. Structure removes randomness and gives meaning to the data that follows.

3. Reporting that makes analysis possible

GENAU reports in Excel because data must remain active. Excel allows trends, comparisons, graphs and verification. Static dashboards and PDFs freeze information. Excel supports real analysis of yields, movement, stock ageing and capacity behaviour over weeks, months or years. A single datapoint is meaningless without context.

4. Deming’s core principles and the foundation of GENAU

Let us look more closely at what Deming actually taught us. It is important to understand that he was one of the great thinkers of the twentieth century and played a key role in rebuilding the Japanese industrial system after the Second World War. He is a man one should listen to carefully because his work was not built on theories, but on the behaviour of factories, people and processes in reality.

W Edwards Deming was an American statistician, born in 1900 in Sioux City, Iowa. His field was industrial statistics, with a particular focus on quality, process stability and the effects of variation on outputs. He began his career at the United States Department of Agriculture and later worked in statistical research for the U.S. Census Bureau. Already in the 1930s, he was influenced by Walter Shewhart, the father of industrial quality control and the man who developed the control chart. Deming took Shewhart’s principles and turned them into a practical philosophy for the management of factories.

After the Second World War, Deming was invited to Japan by the Japanese Union of Scientists and Engineers (JUSE). His first formal training of Japanese engineers and managers took place in 1950. He did not give one week or one single visit. He returned regularly over many years and established a sustained training programme that laid the foundation for the transformation of the Japanese economy. He did not give the Japanese new machines, but a new way of thinking: management through systems, not through blame, improvement through measurement, not through randomness, and responsibility for the structure, not the individual.

His influence was so great that Japan established the Deming Prize in 1951, the oldest and still one of the most respected industrial awards for outstanding application of quality principles. It is not an award for research, but for factories and organisations that actually put his principles into practice. Major companies such as Toyota, Nippon Steel and Sony used this framework to build systems that could produce predictable quality from ordinary materials and ordinary people.

Where the West in the 1950s and 1960s still believed that quality came from inspection, Deming taught Japan that quality comes from stable processes. Variation is the enemy of quality — fluctuation, unpredictability and improvised work lead to defects, waste, losses and low morale. He taught managers to design and control the system rather than punish workers for results caused by the system. This is the core of what GENAU requires in a meat factory: the behaviour of the floor must be predictable before the data can be meaningful.

Here are a few of the key principles he developed.

a. Variation is the enemy of quality

Deming taught that uncontrolled variation is the primary cause of defects, waste, delays and poor performance. In a meat factory, this appears as fluctuating yields, inconsistent trimming, unpredictable shrinkage, unstable weight declarations and stock drifting through the plant without a stable pattern. GENAU addresses this by stabilising space, numbering, flow and data capture so variation is reduced at its source. It targets the environment in which data collection takes place as much as the method of data collection.

On the Batch Companion side, which is the system used to manage QC, especially in processing and on the deboning modules where targeted block tests are applied, variables are controlled through tight processes, predefined SOPs and a real-time monitoring system that measures variation precisely and predicts outcomes. The strong QC component makes this one of the most capable and reliable systems in existence.

b. Systems must reduce variation to become stable

Deming argued that the system, not the worker, produces most outcomes. A factory only becomes stable when processes are fixed, flows are known, tools have defined positions, and each step has a consistent method. GENAU follows this directly: zones, crate logic, routes, registers and batch numbering systems remove randomness so the factory behaves the same way every day. As in the previous point, the application of the principle begins by addressing the environment and the processes that maintain order. Every day there is a meeting with every department, where the questions are asked: What are we doing better today than yesterday, and where have we advanced the system? We view every aspect of life in the factory as serving the processes. Because we work with people, we also follow the wisdom of Solomon that the wise make knowledge acceptable. We therefore design human-centred systems in consultation with management to hardwire outcomes.

c. Measurement must be continuous

Deming emphasised that understanding comes from studying results over time, not from occasional inspection. GENAU therefore measures movement, weights, yields and batch behaviour continuously. This allows the system to detect where shrinkage enters, where delays occur, where yield is gained and where the process is drifting. Continuous data is the basis for daily improvement. It reports in spreadsheets, in Excel. The absence of a dashboard is deliberate. Presenting results in spreadsheets gives the user full control over the data, allows direct interrogation of figures and trends, and supports management workbooks that track results over time.

d. Operators must understand the impact of their own work on the flow

Deming taught that people perform best when they understand the system they work in. GENAU includes clear SOPs, coaching and explanation so operators know why they do each action, how it affects yield and stock, and how their decisions influence downstream departments. When understanding increases, variation drops. It is a key feature that we explain to everybody how the entire system works, so that they can understand themselves in relation to the whole and the key part they play in achieving the shared objectives

e. Management must design processes that make correct work the default

Deming insisted that the system must support correct behaviour automatically. GENAU applies this by designing the environment so that the right action is the easiest: defined crate logic, numbering systems, movement paths, fixed capture points and stable workstations. When the system is well-designed, quality becomes a natural outcome rather than an effort. The entire batch companion and deboning model is based on this.

Taken together, these five principles form the intellectual foundation for GENAU. OSASS is the practical method through which these principles are expressed in daily work. It is the backbone of GENAU. Deming describes how a factory must think. OSASS describes how a factory must behave. OSASS is the physical environment in which GENAU operates and is an integral part of the system. The second part of the system is the world of advanced analysis, supported by human judgment and intuitive input, where AI processes data at a level of speed and structure that no manual method can match. Where OSASS is the backbone of GENAU, this analytical layer is its muscles and its central nervous system, including its brain.

5. OSASS: Deming’s principles in factory practice

Kristi and I studied Deming’s work for months, and while we were developing the GENAU system, we realised that his approach could not simply be an addition, but had to become the underlying principle of the entire framework. We could not allow his theory to remain on paper; it had to be applied on the floor. The answer was a five-part operational method that lives practically in the factory, which expresses in action what Deming described in theory. This method is OSASS.

a. Order

Predictable factory behaviour requires predictable placement of people, equipment and material. Order stabilises the system. It sounds simple when one says it, but sorting out only the freezer rooms and chill rooms completely can take months. Absolute control and order in every storage location must be the standard. No box, no crate and no item may be out of place.

b. Sanitation

Clean environments protect workflow. Disorder forces operators to change routes, makes processes unpredictable and introduces variation. Meat processing cannot function without cleaning. It remains striking how many people do not understand this principle. Kristi introduced me to the old Austrian and German practices where, in the late 1800s and the early 1900s, the factory was placed on the same level as a church. The equipment was not seen as ordinary objects but as tools that serve to feed the community, and order and cleanliness were part of the dedication to God. When one realises that every machine and every process is an answer to our simple prayer, “give us this day our daily bread,” a different attitude towards the workplace emerges. Cleaning does not become a task, but an expression of pride and responsibility for what we do and for the people we serve.

c. Arrangement

Space is precisely delineated. Cuts, stations, flow and tools have fixed boundaries. No accidental crossing of areas or movement. Delineation removes noise from data and behaviour. In practice, however, it takes time to find the right place for everything, especially in a factory that is already chaotic. Storage rooms often need to be planned and repacked more than once, and with each iteration, the structure becomes better. The same applies to work areas such as the deboning room. Only after months of adjustments and testing does it begin to feel as if the layout is truly optimal. In one factory where we have been involved for a long time, we are now experiencing for the first time that everything is starting to fall into place.

d. Standardisation

Registers, numbering rules, SOPs and fixed procedures are established and repeated. Standards make behaviour predictable. What is striking is how effectively the system helps ordinary factory workers to follow these processes and become part of their improvement. Since we began implementing the system, we have seen that the ordinary worker takes ownership and supports the system independently. When people understand what must be done and why, they begin to protect the order. GENAU was developed for people. It is not people who were developed for the system.

e. Self-discipline

The human side. Daily compliance without exception. Without self-discipline, variation returns, even in the best systems. Discipline does not arise from someone shouting at you all day. It comes from human behaviour anchored in values. It comes from respect for the people beside you and for the work you do together. It comes from integrity that requires every person to do the right thing, even when no one is watching.

GENAU in Operation

The power of GENAU does not lie in one component but in the interaction between structure, data and human actions. When fixation points, routes and number logic are applied consistently, factory behaviour begins to form predictable patterns. Operators perform the same action in the same place in the same way. This creates a baseline that can be studied over time. By placing every movement, weight and operation on a defined path, it becomes possible to see variation at its point of origin rather than attempting to manage symptoms at the product level. This is where GENAU comes alive: not in software but in the behaviour of people in physical space.

The Japanese industrial upheaval offers a useful analogy. Toyota did not conquer the world because it had better steel or cheaper labour. They succeeded because they viewed the flow of production as a learnable system. The core of the Toyota Production System was not just standard work and Jidoka, but the idea of Just in Time production. This means that a factory does not produce because it can, but because the market demands it. Every station, every movement and every task is designed so that it aligns with real demand. When the rhythm of demand becomes the rhythm of the factory, congestion disappears, inventory is not built up “just in case,” and operators begin to work within a flow instead of against chaos. Once a factory is managed according to flow, discipline changes from an instruction into a logical consequence.

This is the same thinking GENAU applies to meat processing. A meat factory must be predictable before it can be intelligent. Only when deboning, cutting, mixing and packaging function in fixed routes and tempos can the numbers begin to show where behaviour deviates. GENAU brings the Just in Time principle back to its origin: not as a software module trying to summarise things, but as a physical pattern that emerges on the floor. When every crate, every cut and every movement follows a defined path, it becomes possible to see real capacity and real bottlenecks rather than trying to fix symptoms at product level.

GENAU’s use of AI does not serve as a replacement for human thinking but as a reinforcement of it. When crate movements, cutting volumes, batch behaviour, yields and storage shifts are consistently recorded, the data begins to speak. AI does not create order; it identifies order or disorder that already exists. It shows where shrinkage enters, which cuts correlate with lower yields, how storage patterns cause delays and where capacity disappears. It does this without the emotional filters of management, without assumptions, and without the adjustments people make when they are under pressure. It is an instrument that measures reality rather than confirming assumptions. It also does this without expensive equipment.

Where most factories work with data, they attempt to manipulate it from the top down. The ERP-first approach says: show me dashboards and graphs and then we will adjust the factory. GENAU turns this exactly around. The physical factory must be stable first and then data must describe the behaviour of the factory. Only then are predictive modelling, production scheduling and planned yields truly accurate. When a company bases its production programmes on behaviour that has already stabilised, the same rhythm emerges that made Toyota’s plants strong. Every cut, every kilogram and every movement becomes part of a flow that is not reinvented from day to day, but arises from reality. This is not research. This is operational physics.

Here lies the point that managers underestimate. GENAU is not merely a way to count stock or track batches. It is a way to design the factory so that behaviour, cost, quality and yield are not determined by individual performance but by the structure in which individuals move. When the question is asked: “Why did we have fourteen percent less yield than last week?” GENAU does not give stories but movement, routes, timestamps and item numbers. Management can point to exactly where it happened, in which shift, at which station and which product range was involved. No debate. No blame. Only reality.

This is why GENAU is not an optional tool but a framework for factory management. When a company implements the system fully, the same thing happens that happened in Japan: the factory begins to improve itself. Employees protect flow. Team leaders reorganise workstations because the data shows where the pinch point is, and not because someone above says it must be changed. Management does not only look at margins but at patterns, tempos and stability. Profit is no longer a surprise but an expected outcome of a predictable system.

In the end, this makes GENAU the only realistic option when considering a management framework for a meat factory. ERPs, barcodes and scattered modules create data. GENAU creates factory behaviour that produces meaningful data. It creates a world in which AI is truly useful because the foundation has already been established. When a system places predictability on the floor before data, a factory gains the same advantage that Japan gained in the fifties and sixties: ordinary people become outstanding performers because the system itself does the brilliant part.

Conclusion

GENAU is not a piece of software or a digital tool. It is a factory philosophy that organises the physical reality of a meat-processing plant so that data becomes meaningful. Where Deming requires stability in processes, GENAU provides the mechanism to enforce it in practice. Where Shannon shows that signal exists only when noise is excluded, GENAU does this through fixed routes, defined spaces and consistent fixation points. Where OSASS shapes the behaviour of people, materials and equipment, AI provides the capacity to process the enormous volume of data that modern production generates.

The power of the system lies in the fact that it does not start with technology, but with order. Registers, numbers, flow and working methods come first. AI comes afterwards. When the environment is predictable, numbers become stable, patterns appear, and analysis begins to have meaning. Without order, data becomes noise: weights that cannot be compared, yields that cannot be explained and stock that cannot be traced. With order, the same data becomes an instrument for control, improvement and profit.

GENAU makes the factory measurable. It shows where shrinkage originates, where delays creep in, where yield is formed and which routes waste energy. It enables managers not to react but to design. Operators understand how their actions contribute to yield, flow and stability, and the results of their behaviour become visible every day.

The value of GENAU is simple. It changes a factory from a place where people “work” into a system that produces predictably. It makes quality not a consequence of inspection but of design. It moves management away from drama, improvisation and blame, and returns it to its rightful role: to build a structure in which ordinary people can deliver extraordinary results. When OSASS is established on the floor, when number logic anchors the factory and when AI handles the data stream, a factory does not merely operate better — it improves every day without anyone having to beg for it.

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The Complete Work on our GENAU System

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References

Deming, W. E. Out of the Crisis. MIT Press, 1986.
Deming, W. E. The New Economics for Industry, Government, Education. MIT Press, 1993.
Deming, W. E. Statistical Method from the Viewpoint of Quality Control. Dover Publications, 2000 (original lectures delivered in 1939).
JUSE (Japanese Union of Scientists and Engineers). The Improvement Journey: Early Records of Quality Control in Postwar Japan. JUSE Press, various editions.
Shewhart, W. A. Economic Control of Quality of Manufactured Product. D. Van Nostrand, 1931.
Shannon, C. E. A Mathematical Theory of Communication. Bell System Technical Journal, 27 (1948): 379–423 and 623–656.
Shannon, C. E. and Weaver, W. The Mathematical Theory of Communication. University of Illinois Press, 1949.
Ishikawa, K. Guide to Quality Control. Asian Productivity Organization, 1976.
Juran, J. M. Juran on Planning for Quality. Free Press, 1988.
Ohno, T. Toyota Production System: Beyond Large-Scale Production. Productivity Press, 1988.
Norman, D. A. The Design of Everyday Things. MIT Press, 2013 (revised edition).
Montgomery, D. C. Introduction to Statistical Quality Control. Wiley, 8th edition, 2019.
Provost, L. P. and Murray, S. K. The Health Care Data Guide: Learning from Data for Improvement. Jossey-Bass, 2011.