By Eben van Tonder
5 August 2025
Introduction
Unskilled workers in African food and meat processing environments often behave in ways that appear irrational or careless to industrial managers. The behaviours include magical thinking in problem-solving, ignoring warning signs, treating broken tools as usable, disregarding hygiene or process flow, resisting structure, and having no sense of urgency or conceptual thinking. To the trained mind, it is chaos. But to the untrained worker, it is life as they know it.
These are not isolated incidents. They are rooted in deep anthropological and psychological structures: fatalism, high power distance, oral learning traditions, rote education, communal identity, and survival-based logic. The behaviours persist regardless of training, SOPs, or coaching.
We must now say clearly: Training will never solve this.
There are only two working solutions:
- Robust equipment designed for hard misuse, environmental abuse, and conceptual error
- A small, highly capable group of managers, supported by AI, camera systems, and real-time dashboards who oversee everything with clinical authority
Idealistic approaches will fail. This article analyses the behaviours in detail, identifies their anthropological and psychological roots, and then, without illusion, provides practical design and management strategies based on what works, not what should.
Magical Thinking in Problem Solving
Workers often respond to problems with superstition: “It will come right.” When a machine makes a strange noise, no one switches it off. A technician will rather hit it in the “right spot” than investigate.
This behaviour reflects fatalism (Mbiti, 1990; Maranz, 2001) and an external locus of control (Rotter, 1966). It also reveals a severe lack of systematic, causal reasoning, rooted in educational systems that emphasised memorisation over analysis (Akyeampong et al., 2012).
Forget training. Design machines that react to problems on their own.
• Equip machines with non-negotiable automatic shutdowns
• Use simple lights, icons, and buzzers instead of error codes
• Assume early-stage problem detection by operators is unreliable, because it is
No Causal Reasoning, No Conceptual Understanding
A smoker not producing smoke? The operator blames the heating elements above, not the smoke generator below.
Workers do not isolate variables or follow logical sequences. Piaget (1952) called this “pre-operational thinking.” They use associative or magical logic, not systems thinking.
Design to eliminate ambiguity
• One function per control
• No optional settings
• No conceptual instructions — everything must be either physical, visual, or forced
Maintenance Neglect and Anti-Perfectionism
Screws come loose. Doors stay open. Water is sprayed into electrics. Tools lie broken. Stock rusts. It does not matter to them.
This is not laziness; it is normalised environmental degradation learned at home and in public institutions (Chisholm, 2004; Van Niekerk, 2017). There is no concept of “perfection” — only of “does it still work?”
Response: Equipment must survive abandonment
• Sealed electronics (IP65+)
• Self-lubricating parts
• Clearly visible wear indicators
• All connectors fool-proof and water-tight
Manual Work = Low Status. Hierarchy = Worship
Unskilled African workers associate manual work with shame, and title with unquestionable authority — even if the “expert” is wrong.
In high power distance cultures (Hofstede, 1984), status is ascribed, not earned. A junior staff member might make a radical decision without consulting anyone — and feel justified.
We cannot train this out. We must design around it
• Never rely on juniors for decisions. Lock critical decisions behind escalation protocols
• Use interlocks, permissions, and physical constraints
• Make expert logic built into the system, not into a person
Urgency, Time, and the Meaning of “Soon”
Time does not matter. “Soon” could mean now or in three hours. There is no concept of “time lost = money lost.” Tasks begin when workers feel ready. There is no punishment — and no reward for speed.
This comes from a polychronic time culture (Trompenaars and Hampden-Turner, 1998; Barber, 2013). Relationships trump schedules. Daily survival trumps minute-level urgency.
Systems must work even when workers are late
• Include buffer zones
• Automate as much timing logic as possible
• Let machines act when humans delay
Communication: The Art of Vagueness
“Where is the foreman?” — “He is around.”
“When will it be fixed?” — “Very soon.”
This is not deception. It is face-saving, indirect communication, rooted in oral tradition and avoidance of confrontation (Maranz, 2001). But it destroys planning and accountability.
Use technology to track and time-stamp everything
• Assign responsibility zones. One man = one answer
• Build mobile reporting systems. WhatsApp timestamps > verbal promises
• Reward clarity, not charm
Process Flow? Recipe? What Are Those?
The idea that a task follows a sequence or that a recipe must be followed exactly is completely foreign. It is not rebellion — it is simply not how the mind has been trained.
Continuous improvement? That is a managerial fantasy.
Machines must enforce flow
• Each action should physically block the next until complete
• Recipes should be digitally locked into systems — no manual override by line workers
• If a machine or process expects “discipline,” it will fail
The Home Environment is the Root
These patterns are not workplace problems — they are home-based mental models. If someone has never seen a clean storeroom, a precise job, or systematic hygiene at home, they will never spontaneously do it at work.
You cannot “correct” this with a poster, a safety slogan, or a weekend training session.
AI as the Only Scalable Solution to Chaos
No amount of training will fix this. Not today. Not ever. Workers who have not internalised discipline, order, and causality by adulthood cannot be “retrained” into these habits at scale. The same applies to incentive schemes: workers may accept bonuses, but it will not permanently alter deep-rooted behaviour.
The belief that training or incentives alone can overcome generational behaviour gaps is a fallacy.
If someone does not see a problem in a loose screw, an open panel, a door left ajar, or an idle machine, no amount of theory will teach them the urgency of precision.
AI is the only scalable tool capable of imposing continuous accountability across time, space, and departments.
AI Must Monitor Everything — Every Day, Every Shift
• Maintenance schedules must be tracked digitally. AI flags overdue work and escalating risks
• Cleaning procedures must be logged with time, duration, and verification by camera or phone
• Worker attendance must be scanned in and out. AI tracks how long they work, where they move, how often they leave the line
• Bathroom and break time must be accounted for. AI calculates average breaks and identifies fake downtime
• Hand-washing and knife-changing procedures must be observed and verified — either via direct camera analytics or mobile audits
• Every machine must become an IoT device. AI must monitor RPMs, temperatures, usage, errors, and cleaning cycles
• Doors left open must trigger alerts — and the system should log who left it open and when
• Every storeroom and workshop must be inspected daily — either by AI camera systems or a person recording every square metre via phone upload. Faults generate work tasks with deadlines
• Setup changes (e.g. blade changes, calibration) must be guided and confirmed by AI or sensor-based protocols
• Every manager must receive a full AI-generated report every morning showing:
- Staff presence and location data
- Maintenance task compliance
- Cleaning completion
- Workflow deviations
- Non-compliance flags
AI is not a replacement for management — it is a multiplier of competent leadership.
In this system, even the most chaotic plant can become world-class — not because of the workforce, but because the oversight is relentless, data-driven, and uncheatable.
Final Word
This is not about condescension or superiority. It is about facing reality soberly. Africa’s work culture is shaped by centuries of communalism, oral tradition, colonial damage, and economic constraint. We do not fix that with hope.
We fix it with:
• Design
• Leadership
• Data
• Clarity
• Accountability
Do not plan for the world you want. Build for the one you are in.
References (Harvard Style)
Akyeampong, K., Lussier, K., Pryor, J. and Westbrook, J. (2012). Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal of Educational Development, 33(3), pp.272–282.
Barber, M. (2013). The Good News from Africa: How to Improve Education Systems. Center for Global Development.
Chisholm, L. (2004). The Quality of Primary Education in South Africa. South African Journal of Education, 24(4), pp.256–264.
Gyekye, K. (1996). African Cultural Values: An Introduction. Accra: Sankofa Publishing.
Hofstede, G. (1984). Culture’s Consequences: International Differences in Work-Related Values. 2nd ed. Beverly Hills: Sage.
Maranz, D. (2001). African Friends and Money Matters: Observations from Africa. Dallas: SIL International.
Mbiti, J.S. (1990). African Religions and Philosophy. 2nd ed. London: Heinemann.
Piaget, J. (1952). The Origins of Intelligence in Children. New York: International Universities Press.
Rotter, J. (1966). Generalised expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), pp.1–28.
Trompenaars, F. and Hampden-Turner, C. (1998). Riding the Waves of Culture: Understanding Diversity in Global Business. New York: McGraw-Hill.
Van Niekerk, L. (2017). Building Maintenance Cultures in African Factories. Journal of Industrial Management, 42(1), pp.13–22.