Nikki Moodley, Operations Improvement Executive: Astral Foods

Celebrating the journey to operational excellence

Nikki Moodley opened the conference and facilitated the programme, drawing together the key messages and takeaways from each session.

What stood out throughout the day was that excellence is not a once-off project. It is built through strong foundations, disciplined execution, capable people, collaboration and a relentless focus on improvement.

From leadership and business transformation to technology, operational mastery and value-chain integration, the presentations demonstrated how 20 Keys has become part of the way Astral works.

ODI was delighted to be part of a day that celebrated not only achievements, but the people, ideas and commitment behind Astral’s continuous improvement journey.

Gary Arnold, Chief Executive Officer: Astral Foods

Excellence as part of the way we do business

Gary Arnold opened the speaker programme by reflecting on the role of operational excellence in Astral’s business.

A particularly strong message was the responsibility to remain mindful of cost while fulfilling Astral’s purpose of producing affordable protein.

Gary reflected on how excellence has been part of Astral’s DNA for many years, with 20 Keys providing a structured approach that helps maintain focus and discipline.

He also emphasised collaboration, teamwork and communication across the organisation, and the responsibility of today’s leaders to eventually hand the business over to the next generation in good condition.

A fitting start to a day focused on sustainable excellence.

Keynote Speaker, Gavin Sharples

Own it. Learn. Grow. Keep moving forward.

Keynote speaker Gavin Sharples brought a very different energy to the conference, challenging delegates to consider the role each individual plays in creating change.

His message centred strongly on personal accountability: lift your head, take ownership and do not allow yesterday’s experiences or limiting beliefs to determine what happens next.

He encouraged people to remain curious, keep learning and continue trying, because innovation and improvement depend on people who are prepared to think differently and act.

A lively reminder that operational excellence ultimately begins with individuals who are prepared to take responsibility for making things better.

Johan Benadie, Director of ODI

The Architecture of Excellence: Driving Improvement by Perfecting Basics

Johan Benadie focused on the foundations that make continuous improvement sustainable.

At the heart of the presentation was Kaizen – continuous improvement through small, ongoing changes – supported by disciplined processes, strong fundamentals and, in Johan’s words, “absolute respect for people, machines and products.”

He also emphasised the importance of visibility on the floor. Leaders need to be present where the work happens, understand what people experience every day and ensure that their leadership style is appropriate to the situation and the people they are working with.

“When you walk through the workplace, what message do your movements leave behind?”

The challenge is not simply to manage activity, but to energise the workplace – creating an environment where people, machines and products are treated with respect and where improvement becomes part of everyday work.

A strong reminder that operational excellence starts with the basics, but is sustained through Kaizen, respect, visibility and leadership behaviour.

To read more about Johan Benadie’s ‘The Architecture of Excellence: Driving Improvement by Perfecting Basics‘, please click here.

Hannes Uys, Chief Operating Officer of National Chicks

The Journey to Excellence

Hannes Uys shared National Chicks’ journey towards operational excellence.

He described excellence not as a destination, but as a dynamic journey built through small, deliberate steps.

The journey has moved through building strong foundations and alignment, developing innovation and capability, embedding excellence into everyday habits, and working towards optimisation and sustainability.

Across every stage, one element remains central: people.

Having the right people, creating a sense of belonging, encouraging involvement and building a common language of excellence are essential if improvement is to become part of everyday work rather than another initiative.

His presentation showed how people, purpose and performance ultimately come together.

James Berry, Chief Operating Officer of County Fair

Business Transformation in a Fast-Paced Environment

James Berry shared insights from the County Fair turnaround and the realities of transformation in a complex, fast-paced production environment.

As volumes and complexity increase, operations need the right systems, technology and capability to consistently deliver the right product, to the right customer, at the right time.

James highlighted the role of lead and lag measures, structured daily management, dashboards and disciplined execution in keeping improvement focused.

One particularly useful principle was the need to identify where change can make the greatest impact – and then maintain the focus required to execute it.

Transformation requires investment and technology, but equally important is the discipline to turn plans into results.

Minziwe Majula, Financial Manager for Tiger Animal Feeds and Tiger Chicks

Unlocking the Engines of Excellence – Improvement Projects

Given Minziwe Majula demonstrated how continuous improvement can extend well beyond the factory floor.

Her presentation considered waste in the office and how finance and administrative environments can actively participate in the 20 Keys journey through a focus on people, process and digital innovation.

Regular 20 Keys discussions, cross-functional involvement, visibility and accountability help teams identify opportunities for improvement and act on them.

The message was an important one: operational excellence is not confined to production. Every process offers opportunities to reduce waste, work smarter and improve the value delivered by the organisation.

Neil Dominy, Chief Operating Officer of Central Analytical Laboratories | Gerhard Pretorius, Nutrition Executive at Astral Foods’ Centre of Excellence | Dr. Frankus Visagie, Group Veterinary Manager

Applied Research Supported by Modern Laboratory Techniques

Neil Dominy, Gerhard Pretorius, and Frankus Visagie demonstrated the role that applied research and modern laboratory technology play in continuous improvement.

Their presentation highlighted the importance of depth of knowledge, speed and accuracy of processing, reliable results and continual technological development.

The laboratory environment provides information on which important operational decisions depend, making quality and reliability fundamental.

Their session also reinforced an important improvement principle: technology provides powerful capability, but it is the combination of knowledge, systems and continuous improvement that ultimately creates value.

Panel Discussion

Excellence through Operational Mastery

Mark Surendorff, Chief Operating Officer: Meadow Feeds – Central Region, led a practical panel discussion with Marno Lacock, Ravi Naidoo and Storm Sanderson on Meadow Feeds’ experience of operational excellence.

The discussion showed how 20 Keys help to build ownership and involvement, encouraging people to identify problems, suggest solutions and understand how their work affects the bigger picture.

The panel spoke about making ideas visible, recognising success and moving from reactive practices towards greater ownership – including operators taking a more proactive role in equipment care.

Another strong message was around measurement: do not simply chase the number; understand what drives it.

Keep the principles simple, involve people early and make improvement part of the way the operation works.

Isabella Moremi, Hatchery Operations Manager at Festive, and Madelein Truter, Broiler Operations Manager at County Fair

Excellence through Integration of the Internal Value Chain

Isabella Moremi and Madelein Truter shared their improvement journey around PEF and hatchability.

Their experience demonstrated the importance of identifying challenges early, measuring performance and working collaboratively across the internal value chain.

Over several years, stronger partnerships, disciplined execution and continuous improvement have contributed to sustained results, with structured improvement cycles supporting the process.

Better visibility enables faster responses – but the real strength comes from people in different parts of the value chain working together towards the same result.

It was an excellent practical example of 20 Keys extending beyond individual departments to improve the performance of the whole system.

Astral Awards Ceremony

Recognising Achievement Across Astral

The Astral 20 Keys Conference concluded with an awards ceremony, recognising teams and operations across the Group for their achievements and progress on their continuous improvement journey.

The awards were presented by senior Astral leaders:

• Robert Roszkopf – Managing Director: Agriculture, Astral Foods
• Kenny Schoeman – Poultry Processing Director, Astral Foods
• Herman Nienaber – Chief Operating Officer, Meadow Feeds – Zambia
• Hugo Buys – Acting Managing Director, Meadow Feeds

This fitting recognition highlights the commitment, teamwork, and continuous improvement demonstrated across Astral’s operations.

Pictured below is Gary Arnold with the winners in various categories:

Nontobeko Radebe: Ross, Mount West

.

Francois Pretorius: County Fair Agri

.

Vernon Webb, County Fair, Hocroft

.

James Berry, County Fair Commercial

.

Tiaan Auret: Meadow Feeds, Paarl

.

Dennis Mbambo: Agric Division, National Chicks

.

Chama Kalamba: Zambia, Tiger Chicks

.

Brutus Thangoane (Meadow Feeds, Delmas), Ravi Naidoo (Meadow Feeds, Standerton), Pieter Smith (Festive Agric)

INTERNATIONAL 20 KEYS EXCELLENCE AWARD – NATIONAL CHICKS

A special highlight of the Astral 20 Keys Conference was the presentation of the International Excellence Award to National Chicks’ Ashburton and Cornfields farms.

The award recognises sustained operational excellence rather than a once-off achievement. It reflects strong systems, capable people, effective processes and consistent performance over time.

Achieving this level requires a rigorous review process and evidence that the required standards have been reached and sustained. The award therefore represents an important achievement by the teams at both farms and their ongoing commitment to continuous improvement.

Esbe du Toit from ODI presented the award on behalf of ODI and PDI Japan

Hannes Uys, Chief Operating Officer of National Chicks

Duncan Watson, Systems Manager, National Chicks

To read more about Astral’s 2022 Conference: “20 Keys helps to mitigate business risk, supports efficiency improvements & business strategy”, click here.

The post The 2026 Astral Foods’ 20 Keys Conference appeared first on ODI.

By Johan Benadie, Director, Organisation Development International

Recently shared at the Astral Foods 20 Keys Conference : ODI was delighted to be part of a day that celebrated not only achievements, but the people, ideas and commitment behind Astral’s continuous improvement journey.

Operational excellence is often associated with tools, techniques, systems and improvement projects. These are all important, but sustainable excellence goes much deeper. It is created when our processes, standards, structures, capabilities, and behaviours work together, supported by leadership that understands why improvement matters in the first place.

At its heart, operational excellence is about getting the fundamentals right – and then continuously making them better.

Start with a compelling “why”

People need to understand why improvement matters.

If the purpose is not clear, continuous improvement can very easily become another programme, another checklist, or something we do because management expects it.

A compelling “why” creates psychological commitment. It helps people connect their daily work to something bigger and gives meaning to the effort required to change.

Strong leadership starts here. When people understand the purpose, improvement becomes something they participate in rather than something that is done to them.

Kaizen – better every day

Kaizen is not complicated.

It is the discipline of continually looking at what we do and asking how we can make it better.

The improvements do not always need to be big. In fact, operational excellence is more often created through the accumulation of many small improvements carried out consistently over time.

• A small reduction in waste.
• A slightly faster changeover.
• A more reliable machine.
• A better way of sharing information.
• An improvement in hatchability, feed conversion or product recovery.

Individually, these may appear small. Collectively, they can fundamentally change the performance of a business.

What can we make a little better today?

Kaizen is therefore not an event. It is a way of thinking and working. It sits within a people-centric approach where people’s well-being, needs and growth are at the core of strategy and culture.

Absolute respect for people, machines and products

One of the principles I believe in strongly is absolute respect for people, machines, and products.

Walk into any workplace and you can very quickly see what an organisation truly values.

• How are people spoken to and treated?
• What condition is the equipment in?
• How is the product handled?
• Are abnormalities ignored or addressed?

Operational excellence should be visible in the everyday condition of the workplace – not only in presentations, reports and performance measures.

Respect is demonstrated through behaviour.

Leadership must be respectful, visible and energising

Leaders cannot understand a workplace only from reports and dashboards.

We need to be visible where the work happens.

That means going to the floor, observing, asking questions, listening and understanding what people experience every day.

But visibility by itself is not enough.

Our leadership style must be appropriate to the people and the situation. How we enter a workplace, how we react to a problem, and how we engage with people all send a message.

When you walk through the workplace, what message do your movements leave behind?

Do people feel more confident after you have been there? Have you helped them think? Have you encouraged ownership? Have you left the workplace energised?

Or have people simply learned to keep their heads down until management leaves?

Respectful, visible and energising leadership builds capability. It creates an environment in which people want to participate in improvement rather than merely comply with instructions.

Improve the system, not only the number

Measures and targets matter, but we must be careful not to spend all our energy chasing the result.

The result is an output of a system.

Performance comes from the interaction between people, processes, systems, and technology.

If we want better outputs, we need to understand and improve the inputs that produce them.

This is why standards are so important.

Good standards provide the stable baseline from which we can identify abnormalities, solve problems, and improve. Without standards, we cannot easily distinguish between normal variation and a genuine problem.

But standardisation should never mean that we stop questioning.

The standard represents the best-known way today. Kaizen asks us to find a better way tomorrow.

The challenge is to maintain the balance between discipline and improvement – following the standards that protect quality, safety, and consistency while continuously challenging ourselves to improve them.

Deep implementation matters

One of the greatest risks in continuous improvement is surface-level implementation.

We can introduce boards, checklists, schedules, 5S activities, meetings, and improvement projects and still fail to address the real constraint.

This is why I place so much emphasis on understanding the principle behind the tool.

Principles → Understanding → Application → Execution

Without understanding, organisations can become very busy doing improvement activities without necessarily improving the system.

A good example is the story I used in my presentation of Bradley and “The Fixers”.

A maintenance team can introduce preventive-maintenance schedules, gather more data, develop critical-spares lists and apply 5S to machines, workshops and engineering stores. These are all worthwhile activities.

But if the underlying constraint is not addressed, the impact will remain limited and may not be sustainable.

Modern maintenance has already moved well beyond a purely reactive, historical break-fix model. The opportunity now lies in cost-effective, data-driven, predictive, and prescriptive approaches that improve long-term asset reliability and extend equipment life.

What might once have been a strength can become the constraint.

Experience is valuable. Established systems are valuable. But when yesterday’s successful way of working prevents us from adapting to today’s environment, it can begin to hold the organisation back.

We need thinking people

Operational excellence should not create organisations full of people waiting to be told what to do.

We need thinking people.

• People who can recognise waste.
• People who understand their equipment.
• People who notice abnormalities.
• People who ask why.
• People who solve problems and improve the standards of their own work.

That requires continuous learning and knowledge development.

It also means creating organisations in which information flows effectively – vertically through the organisation and horizontally across functions.

Too much organisational waste is created because information remains trapped in departments, spreadsheets or disconnected systems. The same information gets requested repeatedly, captured several times or recreated because people cannot easily access what already exists.

That is not simply an information problem. It is operational waste.

Technology must enable Kaizen, not digitise waste

Technology and AI create enormous opportunities for continuous improvement.

They allow us to process information faster, identify patterns, generate ideas, and respond more quickly than ever before.

But technology does not remove the need for good operational principles.

If we automate a poor process, we simply create a faster poor process.

Before digitising something, we should therefore ask whether the process itself makes sense.

Technology should help us remove waste, improve visibility, strengthen decision-making and enable people to improve more quickly.

This is where I believe Kaizen becomes even more powerful in the digital age. If technology allows us to implement and learn from small improvements more rapidly, the cumulative effect can be enormous.

It still comes back to people

After all the discussion about systems, processes, equipment, information, technology, and AI, operational excellence still comes back to people.

• People need skills and opportunities to learn.
• They need to be safe and respected.
• They need to understand what good performance looks like.
• They need the authority and confidence to improve their own work.
• They need to understand the customer and the value they create.
• And they need leaders who build capability rather than dependency.

The architecture can ultimately be expressed quite simply:

Great people → Great processes → Great results

That does not mean the journey is easy.

Excellence requires discipline. It requires leadership. It requires standards. And it requires the willingness to continue improving even when things are already going well.

Strategy can be written on paper.

Operational excellence is demonstrated in the daily discipline of doing the right things right – even when nobody is watching.

There is seldom one spectacular action that creates excellence.

More often, it is the accumulation of many small things, consistently done to a very high standard.

That is Kaizen.

And that is how we build the architecture of excellence.

To read more about The 2026 Astral Foods’ 20 Keys Conference, click here.

The post The Architecture of Excellence: Driving Improvement by Perfecting the Basics appeared first on ODI.

• quality,

• cost,

• productivity,

• delivery,

• service,

• safety,

• motivation, and morale,

by applying the CAPDo cycle. When the CAPDo cycle is kept rotating, there will definitely be improvement!

The CAPDo Cycle is a four-step process for continuously evaluating and improving key operations performance indicators, and the underlying people, process, and technology practices.

Recently, ODI’s Esbe du Toit, received the following feedback from Cameron van Kooten, Broiler Farm Manager from County Fair CT; a leading supplier of top-quality fresh chicken in the Western Cape, and a nationwide supplier of frozen and value-added chicken products. County Fair is a prominent chicken and poultry brand that operates as a wholly owned division and major subsidiary of Astral Foods.

“Standard work gives you the best known way of doing a job today, while the CAPDO cycle ensures that the ‘best way’ keeps getting better tomorrow. Standardise what works, measure the results, improve it, and repeat. By involving the team, recognising successes, and celebrating improvements, people stay motivated, take ownership, and create a culture of continuous improvement. Motivation and inclusion are key.”

Don’t miss our upcoming conference: ‘Cultivating a Culture of Operations Excellence’ on the 22nd of October 2026. At R1,190 per person (excluding VAT), the conference will be held at the Premier Hotel, O.R. Tambo, Johannesburg, South Africa, from 08:30 to 16:30. The keynote speaker is Hermann Rolfes, Managing Director of Wispeco (Pty) Ltd, a 100% subsidiary of Remgro Ltd.

Hannes Uys, Chief Operating Officer of National Chicks, a division of Astral Foods Ltd, will also join us as a speaker.

To register, please click here.

The post The CAPDO cycle ensures that the ‘best way’ keeps getting better appeared first on ODI.

By George Meiring  |  Productivity Consulting  |  Gqeberha, South Africa

The Situation

My home in Gqeberha has two 150-litre Kwikhot solar/electric geysers on the roof. Both solar collectors sit adjacent to each other on the Eastern slope, so they receive identical sunlight. Each geyser has a small 12V PV-powered circulation pump. The geysers are roughly 15 meters apart:

Geyser No. 1 is on the North-West side of the roof;

Geyser No. 2 is on the South-East side, directly below its solar panel with a short vertical pipe connection.

Every morning without fail, Geyser No. 1 is 10°C hotter than No. 2, despite both being fed by identical, adjacent solar panels. Something was systematically draining the heat from Geyser No. 2 overnight.

Figure 1 — Roof layout: geyser positions, pipe geometry, root cause and proposed fix

Lesson 1: Don’t Jump to Solutions

My first instinct? Geyser No. 2 was mounted on the outside wall, exposed to the cold overnight air. Obvious cause. Obvious fix. I even ran it past Grok AI, which agreed: if external cold exposure was the root cause, moving the geyser inside was a no-brainer. R5 000 and a plumber later, the geyser was inside the roof cavity.

The CAPDo Cycle: Key 3: Small Group Activities

In the 20 Keys to Workplace Improvement framework, Key 3 (Small Group Activities) uses the CAPDo cycle as the backbone of structured problem solving. It looks deceptively simple. The discipline is in following the steps in sequence, especially not skipping to Plan and Do before you have truly Checked and Analysed.

Figure 2 — The CAPDo Cycle: Check → Analyse → Plan → Do → Celebrate Success

Lesson 2: Use AI Wisely; It’s a Diagnostic Partner, Not a Decision-Maker

Months after the failed relocation, I put the problem to Google AI Mode in a structured conversation, progressively sharing the layout details. Watch how the diagnosis only became possible once I provided the critical geometry:

— George provides the missing geometry —

What AI Can and Can’t Do

AI is a powerful diagnostic partner, but it is not a replacement for physical verification. Before spending a cent on any solution, the human still has to go and check:

Applying CAPDo to This Case, All 8 Steps

Here is how the full CAPDo cycle maps to this geyser problem, including the cost justification that made Step 5 a clear, confident decision:

Key Takeaways

Have you ever jumped to a solution that didn’t work?

Share your story in the comments, or tag someone who needs to read this. The CAPDo cycle is not just for engineers; it works wherever problems need solving. If you’d like to know more about the 20 Keys to Workplace Improvement framework and how it applies in your workplace, reach out directly.

Click here to read more about applying the CAPDo cycle for operations improvement.

The post Stop Jumping to Solutions: The avoidable case of the faulty geyser, AI, and the CAPDo cycle appeared first on ODI.

In today’s hyper-competitive and rapidly evolving industrial landscape, manufacturers face relentless pressure to increase operational efficiency, eliminate waste, and drive continuous innovation. Lean manufacturing principles remain the cornerstone of operational excellence, but with the advent of Industry 4.0, these principles are undergoing a transformative evolution.

The ODI (SA) 20 Keys system, rooted in Iwao Kobayashi’s influential Japanese workplace improvement framework, offers a structured pathway to lean excellence through 20 interconnected focus areas. These range from foundational practices, such as workplace organisation and quality assurance, to advanced domains, including production scheduling and technology adoption. Traditionally, the 20 Keys system aligns closely with lean principles, emphasising waste elimination, employee empowerment, and process optimisation.

However, the rise of Artificial Intelligence (AI) technologies, encompassing machine learning, computer vision, reinforcement learning, and predictive analytics, now offers unprecedented capabilities to enhance each key. AI can enable

• smarter decision-making,

• anticipate disruptions,

• optimise resource allocation, and

• automate routine monitoring,

thereby supercharging lean transformations.

This blog explores how AI technologies integrate with and elevate the ODI (SA) 20Keys framework, with a particular focus on practical applications within South African industry contexts. Drawing on extensive analyses and real-world case studies, we highlight AI’s best fit within specific keys, the underlying algorithms powering these innovations, and measurable business benefits realised.

Overview of the ODI (SA) Japanese 20Keys System

The 20Keys system is a comprehensive lean management model emphasising continuous improvement through systematic workplace and process enhancements. Its keys are:

Key 1: Cleaning and Organising to make work easy

Key 2: Goal alignment

Key 3: Small group activities

Key 4: Reducing Work-in-Process

Key 5: Quick Changeover Technology

Key 6: Kaizen of Operations

Key 7: Zero Monitor Manufacturing/Production

Key 8: Coupled Manufacturing/Production

Key 9: Maintaining Machines and Equipment

Key 10: Workplace Discipline

Key 11: Quality Assurance

Key 12: Developing Your Suppliers

Key 13: Eliminating Wasteful Activities

Key 14: Empowering Employees to Make Improvements

Key 15: Skill Versatility and Cross-Training

Key 16: Production Scheduling

Key 17: Efficiency Control

Key 18: Using Information Systems

Key 19: Conserving Energy and Materials

Key 20: Leading Technology and Site Technology

(Source: Iwao Kobayashi, 1995; ODI (SA) Website)

AI-Driven Enhancements Across Key 20 Keys Focus Areas

This section details select keys with strong AI alignment and transformative potential, explaining objectives, AI-enabled innovations, core algorithms, and illustrative case studies. Priority is given to examples from South African industry, complemented by global benchmarks.

Key 1: Cleaning and Organising to make work easy

Objective: Achieve and sustain an orderly, efficient, and safe workplace through the 5S methodology: Sort, Set in Order, Shine, Standardise, Sustain.

AI Enhancements:

• Computer Vision: AI-powered cameras continuously scan work areas to detect clutter, misplaced tools, or safety hazards, alerting supervisors in real time.
• Layout Optimisation: Machine learning analyses worker movement patterns to recommend optimal workspace layouts that minimise wasted motion and enhance ergonomics.

Algorithms:

• Convolutional Neural Networks (CNNs): CNNs excel at image recognition, identifying visual anomalies like disorganised tools or debris (Goodfellow et al., 2016).
• Reinforcement Learning (RL): RL simulates various layout configurations, learning which arrangements maximise efficiency by trial-and-error (Sutton & Barto, 2018).

Case Studies:

• South Africa: A leading automotive parts manufacturer implemented CNN-driven vision AI systems that reduced tool search times by 15%, increasing worker productivity (Silcock, 2023).
• Global: Toyota employs RL to optimise 5S workplace layouts, cutting non-value-adding activities by 10% (Forbes, 2023).

Key 2: Goal Alignment

Objective: Ensure organisational activities align with strategic goals through transparent objective management and KPI tracking.

AI Enhancements:

• Real-Time AI Dashboards: Integrate diverse data streams to provide live KPI monitoring and alert management to deviations.
• Predictive Analytics: Forecast potential impacts of decisions on performance metrics, enabling proactive resource reallocation.

Algorithms:

• Time-Series Forecasting (ARIMA, LSTM): Predict KPI trajectories, with LSTM networks adept at modeling complex temporal patterns (Brownlee, 2020).
• Decision Trees: Analyse production data to prioritise corrective actions based on likely outcomes (Quinlan, 1986).

Case Studies:

• South Africa: A steel manufacturer used LSTM models to forecast production KPIs, reducing delays due to misaligned objectives by 20% (SAISI Newsletter, 2023).
• Global: General Electric’s AI dashboards leverage decision trees, improving decision efficiency by 25% (GE Reports, 2021).

Key 4: Reduce Work-In-Process (WIP)

Objective: Optimise inventory levels to minimise waste and shorten lead times, supporting just-in-time delivery.

AI Enhancements:

• Demand Forecasting: Machine learning integrates historical and external data (market trends, seasonality) to forecast demand more accurately.
• Supply Chain Optimisation: Algorithms fine-tune replenishment schedules and logistics to reduce storage costs.

Algorithms:

• Random Forests: Ensemble methods analyse multiple variables affecting demand, enhancing prediction accuracy (Breiman, 2001).
• Genetic Algorithms: These simulate evolutionary processes to optimise replenishment timing, balancing stock availability and cost (Holland, 1992).

Case Studies:

• South Africa: Collaboration between a food processing firm and the University of Pretoria employed random forest models, improving demand forecast accuracy and aligning supply with demand (UP Industrial Engineering, 2023).
• Global: Unilever’s AI-driven supply chain optimisation reduced inventory costs by 15% and lead times by 10% (DigitalDefynd, 2025).

Key 5: Quick Changeover Technology (SMED)

Objective: Minimise setup and changeover times to increase manufacturing flexibility.

AI Enhancements:

• Predictive Sequencing: AI analyses historical setups to recommend optimal changeover sequences.
• Digital Twins: Simulate and test changeover processes virtually to identify efficiency gains.

Algorithms:

• Reinforcement Learning: Learns and improves setup sequences dynamically, minimizing downtime (Sutton & Barto, 2018).
• Monte Carlo Simulations: Probabilistic modeling to optimise changeover schedules accounting for uncertainties (Metropolis & Ulam, 1949).

Case Studies:

• South Africa: Wits University applied deep RL to supply chain inventory management, demonstrating improved demand fulfillment compared to classical forecasting (SAJIE, 2025).
• Global: Siemens’ MindSphere platform uses Monte Carlo methods, reducing downtime by 30% (Siemens, 2022).

Key 6: Kaizen of Operations

Objective: Continually refine manufacturing methods to boost efficiency and cut costs.

AI Enhancements:

• Process Mining: AI algorithms analyse production logs to identify bottlenecks and redundant steps.
• Data-Driven Recommendations: Machine learning suggests improvements based on historic process outcomes.

Algorithms:

• Alpha Miner Process Mining: Extracts workflow models from event logs to highlight inefficiencies (van der Aalst, 2011).
• Gradient Boosting (XGBoost): Predicts effects of method changes on output and quality (Chen & Guestrin, 2016).

Case Studies:

• South Africa: The growing process analytics market in textiles and manufacturing leverages AI for rapid method improvement (6Wresearch, 2023).
• Global: Bosch achieved a 12% cost reduction using XGBoost for manufacturing method optimisation (McKinsey, 2021).

Key 7: Zero Monitor Manufacturing/Production

Objective: Automate equipment and process monitoring to minimise human oversight and reduce downtime.

AI Enhancements:

• Real-Time Anomaly Detection: AI continuously analyses IoT sensor data to detect faults instantly.
• Predictive Alerts: Early warnings enable preventive interventions before failures occur.

Algorithms:

• Autoencoders: Neural networks that model normal operational patterns and flag deviations (Hinton & Salakhutdinov, 2006).
• Recurrent Neural Networks (RNNs): Monitor time-series sensor data for trends or anomalies (Graves, 2013).

Case Studies:

• South Africa: Mining firms Autolectron and RAMJACK deploy AI-based real-time monitoring reducing equipment downtime (Mining Weekly, 2014).
• Global: GE’s use of RNNs for process monitoring increased operational efficiency by 20% (GE Reports, 2021).

Key 9: Maintaining Machines & Equipment

Objective: Maximise uptime through predictive and preventive maintenance.

AI Enhancements:

• Failure Prediction: AI models forecast equipment failures using historical sensor data.
• Optimised Scheduling: Machine learning plans maintenance windows to reduce operational disruption.

Algorithms:

• Long Short-Term Memory (LSTM): Excels at predicting failures from time-series data (Hochreiter & Schmidhuber, 1997).
• Survival Analysis: Statistically models equipment lifespan to optimise maintenance timing (Klein & Moeschberger, 2003).

Case Studies:

• South Africa: Bell Equipment and Sappi adopt AI-driven predictive maintenance, improving operational reliability (ODI SA, 2024).
• Global: GE’s predictive maintenance system reduced maintenance costs by 10-15% (GE Reports, 2021).

Key 11: Quality Assurance

Objective: Maintain consistent product quality through automated inspection and process control.

AI Enhancements:

• Visual Inspection: AI systems detect defects with high precision.
• Quality Prediction: Historical data informs early warnings of quality deviations.

Algorithms:

• Convolutional Neural Networks (CNNs): Identify surface defects and assembly errors (Goodfellow et al., 2016).
• Support Vector Machines (SVMs): Classify quality issues from production parameters (Cortes & Vapnik, 1995).

Case Studies:

• South Africa: KwaZulu-Natal electronics manufacturer reduced PCB defects by 17% using CNN-based inspection (SAJEMS, 2024).
• Global: BMW’s AI systems achieve 99% defect detection accuracy using SVM (BMW Group, 2023).

Key 12: Developing Your Suppliers

Objective: Strengthen supplier performance and reliability.

AI Enhancements:

• Supplier Segmentation: Clustering identifies high- and low-performing suppliers.
• Delivery Forecasting: Predictive models anticipate supply chain disruptions.

Algorithms:

• K-Means Clustering: Groups suppliers for targeted development (MacQueen, 1967).
• Prophet Time-Series Forecasting: Predicts delivery reliability (Taylor & Letham, 2018).

Case Studies:

• South Africa: A beverage manufacturer improved supplier delivery times by 13–16% using clustering (DALRRD, 2023).
• Global: Unilever reduced supply disruptions by 20% through AI forecasting (DigitalDefynd, 2025).

Key 13: Eliminating Waste

Objective: Minimise all forms of waste—material, time, energy.

AI Enhancements:

• Waste Source Identification: Decision trees analyse production data for waste hotspots.
• Optimisation: Linear programming minimises resource use.

Algorithms:

• Decision Trees: Target key waste drivers (Quinlan, 1986).
• Linear Programming: Optimises resource allocation (Dantzig, 1963).

Case Studies:

• South Africa: Paper manufacturer cut material waste by 18% via AI analysis (ODI SA, 2024).
• Global: Toyota reduced material usage by 15% using optimisation algorithms (Forbes, 2023).

Key 16: Production Scheduling

Objective: Optimise scheduling to maximise throughput and minimise bottlenecks.

AI Enhancements:

• Dynamic Scheduling: AI predicts and adjusts schedules based on real-time data.
• Simulation: Digital twins test scheduling scenarios before implementation.

Algorithms:

• Genetic Algorithms: Solve complex scheduling balancing multiple constraints (Holland, 1992).
• Reinforcement Learning: Adapts schedules dynamically (Sutton & Barto, 2018).

Case Studies:

• South Africa: University of Johannesburg demonstrated genetic algorithms improving batch chemical plant scheduling (SACAIR, 2023).
• Global: Siemens reduced downtime by 30% using RL-based scheduling (Siemens, 2022).

Key 17: Efficiency Control

Objective: Continuously monitor and improve operational efficiency.

AI Enhancements:

• Real-Time Dashboards: Track efficiency KPIs and suggest improvements.
• Anomaly Detection: Identify sudden efficiency drops early.

Algorithms:

• XGBoost Gradient Boosting: Predicts efficiency trends (Chen & Guestrin, 2016).
• Isolation Forest: Detects anomalies in performance data (Liu et al., 2008).

Case Studies:

• South Africa: Furniture manufacturer improved efficiency through centralised data dashboards (Eybers & Mayet, 2020).
• Global: Bosch reduced costs by 12% using anomaly detection (McKinsey, 2021).

Key 18. Using Information Systems

Objective: Leverage integrated information systems for data-driven decisions and transparency.

AI Enhancements:

• Natural Language Processing (NLP): Extracts insights from unstructured data like reports.
• Predictive Analytics: Provides foresight on operational trends.

Algorithms:

• BERT (Bidirectional Encoder Representations from Transformers): Advanced NLP model for text analysis (Devlin et al., 2018).
• LSTM Networks: For real-time predictive analytics (Hochreiter & Schmidhuber, 1997).

Case Studies:

• South Africa: Johannesburg logistics firm enhanced Balanced Scorecard with BERT for superior decision-making (DUT, 2020).
• Global: GE’s AI systems improved efficiency by 20% using LSTMs (GE Reports, 2021).

Key 19. Conserving Energy and Materials

Objective: Reduce consumption of energy and raw materials to promote sustainability.

AI Enhancements:

• Energy Usage Optimization: Machine learning analyzes consumption patterns and adjusts settings.
• Material Efficiency: Predictive models recommend process adjustments.

Algorithms:

• Particle Swarm Optimization (PSO): Efficiently minimizes energy use by tuning machine parameters (Kennedy & Eberhart, 1995).
• Regression Models: Forecast material usage trends (Hastie et al., 2009).

Case Studies:

• South Africa: Cement manufacturer achieved 15% energy savings using AI-enabled energy management systems (IEEE-NCPC-SA, 2024).
• Global: Tesla reduced energy costs by 15% at its Gigafactory using regression analytics (Tesla, 2024).

Key 20. Leading Technology and Site Technology

Objective: Adopt and integrate advanced technologies to drive operational excellence.

AI Enhancements:

• Digital Twins: AI-powered virtual replicas simulate and optimise operations.
• Technology Adoption: Reinforcement learning optimises how new technologies integrate into workflows.

Algorithms:

• Deep Learning: Enables detailed simulation and analysis for digital twins (LeCun et al., 2015).
• Reinforcement Learning: Guides effective technology deployment (Sutton & Barto, 2018).

Case Studies:

• South Africa: Mining companies like Exxaro employ digital twins combined with deep learning to boost productivity by over 18% (EngineerIT, 2024).
• Global: Siemens reported 20% cost reduction through AI-led technology integration (Siemens, 2022).

Keys with Limited Direct AI Alignment

Certain keys remain more human-centric but can benefit indirectly from AI:

• Key 3: Small group activities: NLP tools like BERT help prioritise ideas but cannot replace human collaboration.
• Key 8: Coupled Manufacturing/Production: Scheduling algorithms assist but integration is complex.
• Key 10: Workplace Discipline: AI tracks metrics; culture and discipline are core.
• Key 14: Empowering employees to make improvements, Key 15: Skill Versatility and cross training: AI supports personalised training recommendations.

Practical Considerations for AI Integration

• Data Infrastructure: Robust IoT networks and cloud data storage are essential (McKinsey, 2021).
• Workforce Upskilling: Employee training to operate and trust AI tools is critical (Deloitte, 2020).
• Ethical AI Use: Compliance with South Africa’s POPIA and ethical standards ensures transparency (IEEE SA, 2023).
• Cost-Benefit Analysis: Companies must carefully assess AI implementation costs against expected efficiency gains.

Conclusion

The fusion of AI technologies with the ODI (SA) 20 Keys system heralds a new era in lean manufacturing and operational excellence. AI transforms core keys—such as Cleaning and Organising, Rationalising Systems, and Information Systems usage—by enabling real-time insights, predictive analytics, and dynamic optimisation. South African manufacturers across automotive, food processing, mining, and other sectors are already realising substantial gains in quality, throughput, and energy efficiency.

Global leaders like GE, Siemens, Toyota, and Bosch demonstrate that AI-enhanced lean systems provide not only measurable returns on investment but also essential agility and sustainability in the AI-driven Industry 4.0 landscape.

By embedding AI into the 20 Keys framework, manufacturers future-proof their operations, unlocking unprecedented levels of efficiency, responsiveness, and environmental stewardship required for competitive advantage in today’s digital age.

The 20 Keys system was developed in Japan by Iwao Kobayashi when he, after completing industrial studies at university, joined Mitsubishi Heavy Industries. Soon after joining them, he developed the first mixed-lot automated assembly line in Japan (and arguably in the world). He was also known as the expert in Japan on quick changeover technology, and shared learning experiences with other great Japanese engineers like Taiicho Ohno, and Shigeo Shingeo. Practical, in the workplace learning together with inputs from employees, the shopfloor and production technologies, led to the development of the 20 Keys. He later authorised a timeless book on productivity improvement, “20 Keys to Workplace Improvement’. To read more about the 20 Keys Operations Improvement System, please click here.

The post AI Meets Lean: Transforming the ODI (SA) 20Keys System for Industry 4.0 Excellence appeared first on ODI.

At Verigreen Pty (Ltd), a weeklong intervention combining Value Stream Mapping (VSM) with 20 Keys benchmarking proved to be a game-changer. This blog post dives into how this powerful approach, rooted in Key 4 of the 20 Keys framework, transformed Verigreen’s operations and set them on a path to achieve their ambitious production goals.

Figure 1: Key 4 of 20 Keys System for Continuous Improvement

The Power of Key 4: Reducing Work-in-Process

Value Stream Mapping, as outlined in Key 4 of the 20 Keys to Workplace Improvement, is a lean manufacturing technique designed to visualise and optimise the flow of materials and information. The “rule book” for Key 4 provides a clear roadmap:

• Rule 1: Document the current process and information flow (Current State VSM).
• Rule 2: Determine how processes are linked.
• Rule 3: Set targets for improvement.
• Rule 4: Improve coupling between processes.
• Rule 5: Allocate responsibilities.
• Rule 6: Decide on focus areas.

When Verigreen approached me to optimise their production system, I proposed a unique blend of 20 Keys benchmarking and a weeklong VSM workshop involving their senior management team. The goal? To not only identify inefficiencies, but also empower the team to design a future state that would help Verigreen scale from producing 900,000 bags per day to an ambitious 1.2 million bags per day.

Planning the Intervention

The journey began with meticulous planning. Inspired by a successful VSM session with BBF Safety in Pinetown, I proposed a structured approach for Verigreen, as illustrated below:

Figure 2: The Process flow of events for the Benchmarking and optimisation.

A Value Stream Mapping Charter was developed to define the scope and expectations. This charter, agreed upon by all participants, set the stage for a collaborative and focused intervention.

Figure 3: Example of Value Stream Mapping Charter

Day 1: Benchmarking with 20 Keys

The first day kicked off with an introduction to the 20 Keys system. I met with managers, walked the production floor, and engaged with operators to assess the current state of operations. The benchmarking results, compiled and shared on Day 4, were visualised in a radar chart, providing a clear snapshot of strengths and areas for improvement.

Figure 4: 20 Keys Benchmarking Radar Chart

The insights gained from this process were invaluable, especially when paired with the hands-on learning from the VSM workshop.

Day 2: Mapping the Current State

Day 2 was all about discovery. The team walked the production floor twice—first to identify the process blocks and then to quantify metrics for each block. We mapped three types of flow:

• Information Flow: From right to left, capturing how data moves.
• Material Flow: From left to right, tracking physical goods.
• Timeline: At the bottom, highlighting process durations.

Figure 5: Value stream mapping picture board

The result was a hand-drawn Current State Value Stream Map, later digitised for clarity. The flow unit was defined as a roll of 20 bags, aligning with Verigreen’s production metrics.

Figure 6: Digitised Current state map

This process was a revelation, uncovering collective knowledge and pinpointing waste in the system. For each step, we quantified value-adding time, laying the groundwork for improvement.

Day 3: Designing the Future State

On Day 3, the team shifted gears to envision a Future State Value Stream Map. The focus was on creating a pull production system to replace the existing push-based approach. A key innovation was the introduction of a “supermarket” model in the finished goods warehouse, where stock levels would trigger production at the bottleneck area, rather than relying on customer orders.

The heart of the Future state design. “The supermarket level in Finished Good Store, pull, production”.

Figure 7: Digitised Future State Value Stream Map follows the hand-drawn one.

The Future State Map highlighted several interventions, marked as Kaizen blitz stars, to reduce inventory (raw materials and work-in-process) and improve flow. A comparison matrix summarised the improvements between the current and future states.

Figure 8: Current State vs Future State summary Comparison Matrix

Day 4: Building the Transformation Plan

The workshop culminated in the development of a Value Stream Transformation Plan. This detailed roadmap assigned responsibilities, timelines, and priorities for implementing the identified improvements. The plan was designed to guide Verigreen toward its future state, with clear, actionable steps.

Figure 9: Value Stream Transformation Plan with reference to applicable Key of 20 Keys

Post-Workshop: Bringing the Plan to Life

To ensure the plan’s success, weekly follow-up meetings were scheduled every Wednesday in Verigreen’s Innovation Boardroom, with remote participation via MS Teams. These meetings tracked progress, addressed challenges, and made necessary adjustments.

Figure 10: Weekly follow-up meetings were scheduled and diligently adhered to.

The Impact of Value Stream Mapping

The Verigreen case study underscores the transformative power of Value Stream Mapping when combined with 20 Keys benchmarking. By involving senior management in walking the floor, identifying waste, and co-creating solutions, the process fostered a shared understanding and commitment to change.

As Iwao Kobayashi, the founder of 20 Keys, emphasised, reducing inventory through process and information flow mapping is a cornerstone of workplace improvement. Key 4’s focus on flow and efficiency builds on the foundational keys, delivering measurable results when executed well.

Key Takeaways

• Collaboration is Key: Engaging senior management in VSM fosters ownership and drives meaningful change.
• Visualising Waste: Mapping the current state reveals hidden inefficiencies and collective knowledge.
• Pull Over Push: Designing a pull-based system, like the supermarket model, enhances flexibility and reduces inventory.
• Continuous Improvement: Weekly follow-ups ensure the transformation plan stays on track.

References

1. Learning to See by Mike Rother and John Shook
2. 20 Keys to Workplace Improvement by Iwao Kobayashi
3. Value Stream Mapping by Karen Martin and Mike Osterling

By embracing Value Stream Mapping and the principles of Key 4, Verigreen is well on its way to achieving its production goals and setting a new standard for operational excellence. What could VSM do for your organisation?

A Success Story: Verigreen’s Ongoing Transformation

The impact of the Value Stream Mapping (VSM) intervention at Verigreen didn’t stop with the workshop. The company has continued to evolve, leveraging the insights and strategies from the VSM process to drive efficiency and innovation across its operations.

The Initial Impact

The VSM workshop laid a strong foundation for Verigreen, identifying bottlenecks, streamlining processes, and aligning teams toward common goals. By applying the 20 Keys framework, Verigreen tackled waste reduction and improved production flow, setting the stage for long-term success.

A Celebratory Update from Theo Govender

Theo Govender, Verigreen’s Operations Manager, recently shared an inspiring update on how the company has built on that foundation:

“The write-up is good; it covers all the areas that we implemented. Just to add, the following was continued to ensure we continue to be efficient and reduce all waste in all departments:

• We worked on the Skills Matrix and ensured all the members are trained to the level we needed them to be.
• Monthly 5 Whys root cause analysis is done on major breakdowns that occurred during the month.
• We have VSM every two weeks, and that allows us to fix issues that the team is facing by setting target dates.
• Our OTIF (On-Time In-Full) has increased to 99%.
• Our inventory holding has gone from 1 day to 1 week now.
• We have a planner that manages our correct model stock levels.
• We also moved the warehouse to reduce logistics and rental costs.
• Specs and Parameters docs have been introduced.”

Theo’s feedback showcases the remarkable progress Verigreen has made. From boosting workforce skills to achieving near-perfect delivery performance, the company has embraced continuous improvement wholeheartedly. The bi-weekly VSM sessions and new operational tools like Specs and Parameters docs reflect a dedication to excellence that keeps Verigreen thriving.

This ongoing transformation underscores the power of Value Stream Mapping and the 20 Keys framework. Verigreen’s success proves that any organisation can turn insights into action and achieve lasting results with the right tools and commitment.

Author: George Meiring: Facilitator and Coach

The post Transforming Production with Value Stream Mapping: A Verigreen Case Study appeared first on ODI.

In short, collaboration thrives when everyone works together toward a common goal, leading to increased efficiency.

The ODI team regularly meets for a Strategic Workshop. Our recent workshop, which took place on February 27th and 28th, 2025, was held at Kleinkaap Boutique Hotel, Centurion. Our focus was Key 2 of the 20 Keys Management System – Goal Alignment.

As ODI’s Head Office is in Pretoria, it is an excellent opportunity for colleagues across South Africa to get together and catch up on the news. To meet our team, please click here.

20 Keys is a vehicle for creating a culture of continuous improvement in an organisation, energising and motivating all employees to work towards the achievement of goals.

Key 2 of the 20 Keys Operations Improvement System focuses on having an effective organisational structure with goal alignment at all levels. Within the structure, the various work groups are organised as “mini-businesses” or MBs. Each MB is guided by Quality, Cost, Delivery, Safety, and Morale performance indicators (QCDSM measures).

Die ODI team participated in small goal alignment groups, creating much energy.

Each member had an opportunity to present their findings.

As the two-day workshop ended, everybody appreciated sharing knowledge and experience, great learning, and the importance of Key 2. 

Please click here to read more about Key 2 of the 20 Keys Management System.

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According to business management guru, Dr. Eliyahu Goldratt, there are three necessary conditions for a successful organisation:

1. Make money now as well as in the future
2. Provide a secure and satisfying environment to employees now as well as in the future
3. Provide satisfaction to the market now as well as in the future

“Thinking people” have the ability to detect problems quickly and address it before it snowballs into an issue where the manager has to get involved. Thinking people ensure that transition from the “fighting fires” – culture to the “building for tomorrow” – culture takes place. “Thinking people” creates capacity for the management team to focus on the most important things that might not seem that urgent but will have an impact and returns in future.   

Obtaining the objective of “thinking people” on all levels of the organisation requires a process of creating layers of understanding. It is achieved through

• structured, daily communication (Key 2: Mini-Business meetings) as well as

• participation in problem-solving groups (Key 3: Small Group Activities).

The image below aims to explain how the different layers of understanding are constructed in an organisation.

The foundational layer of understanding is constructed through the sharing of information. But how do we know what information to share?

Every team in the organisation represents a small part of the bigger organisation. The first line manager of every team is responsible for the achievement of goals within their specific small business – also referred to as Mini-Businesses.  For every Mini-Business there should be specific measurements for Quality, Cost, Delivery, Safety and Motivation (QCDSM). From an operations perspective, the first-line teams have a direct impact on these indicators and therefore it is of utmost importance that they are equipped with information about their team’s QCDSM performance.

Once the operational objectives or QCDSM indicators have been defined for every team. These indicators have to be monitored and discussed on a daily basis through a structured meeting which is held preferably at the start of the day/shift. Monitoring and discussing the same information in a structured way, repetitively, over a period of time, creates a rhythm of constructive communication which ultimately leads to the understanding of information.

Understanding of information is reinforced by applying simple techniques like involving team members in the collection, manual updating and visually tracking of information about these operational indicators. Another way of reinforcing understanding is having different members of the team facilitate the morning meeting and give feedback.

If the layer of understanding is well-constructed for the team, they will be able to start recognising patterns and understand drivers which influences the results. Brainstorming and idea generation will flow naturally with everyone on the look-out for improvement opportunities and reducing waste.

If an idea or a problem that has been identified requires some analysis before it can be implemented, the next step is to formulate a small problem-solving group, referred to as a Small Group Activity (SGA) which operates independently from the Mini-Business meeting.

The first principle to remember when commencing with a Small Group Activity is to NEVER SOLVE PROBLEMS ALONE. Apart from the obvious benefits of viewing the problem from multiple perspectives, there is a significant amount of learning and cross-skilling that takes place during problem-solving activities. Knowledge is often only transferred when we are prompted to share it. Solving problems together allows for the transfer of implicit knowledge and equips individuals to develop their intuitive responses when faced with similar problems.

Another important principle of problem-solving is following the disciplined approach outlined in the CAPDo cycle.

CHECK – The ability to “see” a problem is a victory in itself. If the environment is not safe, employees tend to hide problems and mistakes, or they simply do not realise that something is wrong. Employees have to be encouraged to see and highlight potential problems.

ANALYSE – This is the most important step in the cycle where most of the time and energy should be spent, especially when you are faced with more complex problems. The Analysis phase requires a thorough and detailed RCA (Root Cause Analysis) process. The quality and sustainability of the solution depends on the depth of the analysis.

PLAN – The possible solutions are then translated into a SMART Action plan. All actions should be documented meeting the conditions of being specific, measurable, achievable, realistic and timely. The objective of the action plan is progress and accountability.

DO – Ensure that the plan is implemented by following up on specific tasks executed by specific people within a pre-determined timeframe.

CHECK – After implementingthe plan we have to “check” whether the plan/solution had an impact on the results. If the impact can be seen, CELEBRATE SUCCESS and standardise. If not, we continue to run through the CAPDo cycle until we have achieved the desired result.  

The result of “Thinking People” on all levels of the organisation is an energised workplace where every individual understands their role and contribution towards the ultimate long-term success of the organisation. In this way, the individuals in the business can pull together to achieve the same goals. As former CEO of General Electric, Jack Welch stated:” The companies that find a way to engage every mind – harness every volt of passionate energy, bring excitement to the lives of people to come to work in search of a better way – every day – will be the companies that will win”

Author: Esbe’ du Toit: Senior Consultant

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“In today’s rapidly evolving industrial landscape, manufacturers looking to maintain their competitiveness must prioritise achieving operational excellence. Key performance indicators (KPIs) like Overall Equipment Effectiveness (OEE) are crucial in assessing and improving manufacturing efficiency. However, with the integration of artificial intelligence (AI) technologies, traditional methods of calculating OEE are evolving to provide more accurate insights and promote higher levels of optimisation. This article delves into the calculation of AI-inspired Overall Equipment Effectiveness and its significance for modern factories.

Understanding Traditional OEE Calculation

Before exploring AI-enhanced approaches, it is essential to grasp the fundamentals of traditional OEE calculation[1].

OEE is a metric that measures the effectiveness of manufacturing processes by three critical factors[2].

Availability: The percentage of time that equipment is available for production. It takes into consideration both scheduled and unforeseen downtime including maintenance, changeovers, and breakdowns.

Performance: The ratio of actual production speed to the ideal or maximum achievable speed. Performance losses may result from equipment slowdowns, minor stoppages, or suboptimal operation.

Quality: The proportion of good-quality products produced relative to the total output. Quality losses encompass defects, rework, and scrap.

The formula for calculating OEE is:

𝑂𝐸𝐸=𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦×𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒×𝑄𝑢𝑎𝑙𝑖𝑡𝑦OEE=Availability×Performance×Quality

Traditionally, this calculation provides valuable insights into overall manufacturing efficiency, helping identify areas for improvement and optimization.

Advancements with AI in OEE Calculation

The advent of AI has revolutionised how OEE is calculated and utilised within manufacturing environments. AI-driven approaches offer several enhancements over traditional methods, including:

Predictive Maintenance: AI algorithms can analyse equipment sensor data in real-time to predict potential failures before they occur[3]. By proactively addressing maintenance needs, factories can minimise downtime and maximise equipment availability[4].

Dynamic Performance Analysis: Unlike static performance assessments in traditional OEE calculations, AI enables dynamic analysis of production speeds and identifies optimal operating conditions in real-time[5]. This capability allows for immediate adjustments to maximise performance efficiency.

Quality Assurance and Defect Detection: AI-powered vision systems can inspect products with unparalleled accuracy, detecting defects and anomalies in real-time[6]. By integrating quality assessment directly into the manufacturing process, factories can reduce waste and improve overall product quality.

Data Integration and Analytics: AI facilitates the integration of diverse data sources, including equipment telemetry, production logs, and external factors like weather or market demand. Advanced analytics algorithms can then uncover hidden patterns and insights, enabling data-driven decision-making for optimizing OEE[7].

Calculating AI-Inspired OEE

To calculate AI-inspired OEE, factories integrate AI technologies into each component of the traditional OEE formula:

AI-Enabled Availability: Utilising predictive maintenance algorithms, factories can accurately predict equipment downtime and schedule maintenance activities during optimal production windows[8]. This proactive approach minimises unplanned downtime, maximising equipment availability.

Dynamic Performance Optimisation: AI algorithms continuously monitor equipment performance metrics and production conditions, dynamically adjusting operating parameters to optimise performance in real-time[9]. By leveraging machine learning techniques, factories can identify patterns and trends to enhance production efficiency continuously.

AI-powered Quality Assurance: Integrating AI-based vision systems into the production line enables real-time inspection and quality assessment of products[10]. Any deviations or flaws are found right away, enabling quick remedial action to preserve high standards and reduce quality losses. By incorporating AI advancements into each component of the OEE calculation, factories can achieve higher levels of operational excellence, driving greater efficiency, productivity, and competitiveness in today’s dynamic manufacturing landscape.

AI algorithms and techniques that are frequently applied in OEE calculations and related areas

There isn’t a single “best practice” algorithm for calculating Overall Equipment Effectiveness (OEE) using artificial intelligence (AI). Instead, the choice of algorithm depends on various factors such as

• the type of manufacturing process,

• the availability of data,

• computational resources,

• and the specific goals of the organisation.

However, I can outline some common AI algorithms and techniques that are frequently applied in OEE calculations and related areas.

Predictive Maintenance Algorithms: These algorithms utilise machine learning techniques, such as regression analysis, time series forecasting, and anomaly detection, to predict equipment failures before they occur[11]. By analysing historical data on equipment performance, maintenance logs, and sensor readings, predictive maintenance models can estimate the remaining useful life of machinery and schedule maintenance activities to prevent downtime.

Dynamic Optimization Algorithms: AI-driven optimisation algorithms, including genetic algorithms, simulated annealing, and reinforcement learning, can dynamically adjust production parameters to maximise OEE in real-time[12]. These algorithms continuously analyse production data, such as throughput rates, cycle times, and quality metrics, to identify optimal operating conditions and adapt production schedules accordingly.

Machine Learning for Quality Control: Machine learning algorithms, such as convolutional neural networks (CNNs) and support vector machines (SVMs), are widely used for automated quality inspection and defect detection in manufacturing processes[13]. By analysing images or sensor data from production lines, these algorithms can identify defects, anomalies, and quality issues in real-time, allowing for immediate corrective actions to maintain high-quality standards and minimise quality losses.

Anomaly Detection Algorithms: Anomaly detection techniques, such as Isolation Forest, One-Class SVM, and Autoencoders, can identify unusual patterns or deviations in production data that may indicate equipment malfunctions, process inefficiencies, or quality issues[14]. By flagging anomalous events in real-time, these algorithms enable proactive interventions to prevent downtime and quality defects, thereby improving OEE.

Time Series Analysis: Time series analysis methods, including autoregressive integrated moving average (ARIMA), exponential smoothing, and Fourier analysis, are used to model and forecast production metrics over time[15]. By analysing historical OEE data and identifying underlying trends, seasonal patterns, and cyclic variations, time series models can provide insights into long-term performance trends and inform strategic decision-making for optimising OEE.

Big Data Analytics: Big data analytics platforms, such as Apache Hadoop and Spark, enable the processing and analysis of large volumes of manufacturing data from diverse sources, including equipment sensors, production logs, and enterprise systems[16]. By leveraging distributed computing and parallel processing capabilities, these platforms can uncover hidden insights, correlations, and causal relationships in manufacturing data, thus facilitating data-driven decision-making for improving OEE.

It’s important to note that the effectiveness of AI algorithms in OEE calculations depends on the quality and availability of data, the complexity of the manufacturing process, and the specific objectives of the organisation. Therefore, organisations should carefully evaluate and customise AI algorithms based on their unique requirements and constraints to achieve optimal results in optimising Overall Equipment Effectiveness.

Conclusion

As factories embrace digital transformation and Industry 4.0 initiatives, the calculation of Overall Equipment Effectiveness is evolving with the integration of AI technologies. AI-inspired OEE offers a more nuanced and dynamic approach to assessing manufacturing efficiency, enabling proactive maintenance, dynamic performance optimiSation, and real-time quality assurance. By harnessing the power of AI, factories can unlock new levels of operational excellence, driving continuous improvement and sustainable growth in the modern industrial era.”

To read Ntokozo’s article about Measuring Machines & Equipment Effectiveness, click here.

RESOURCES:

[1]               https://www.oee.com/

[2]              https://www.oee.com/calculating-oee/

[3]           https://www.ppmashow.co.uk/press-release/ai-in-manufacturing

[4]           https://tractian.com/en

[5]           https://www.aveva.com/en/solutions/digital-transformation/artificial-intelligence/

[6]           https://neurosys.com/blog/ai-defect-detection-in-manufacturing#

[7]           https://www.linkedin.com/pulse/revolutionizing-manufacturing-unleashing-power-advanced- analytics-yevqc/

[8]           https://www.myaifrontdesk.com/blog/24-7-availability

[9]           https://www.plantengineering.com/articles/ai-driven-solutions-for-enhanced-plant-automation-productivity/

[10]          https://www.linkedin.com/pulse/industrial-automation-redefined-leveraging-ai-vision-systems/

[11]          https://www.linkedin.com/pulse/machine-learning-algorithms-predictive-maintenance-hope-edet-2tuje/

[12]          https://arxiv.org/pdf/1901.02256

[13]          https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512995/

[14]          https://medium.com/@venujkvenk/anomaly-detection-techniques

[15]          https://www.datamation.com/big-data/what-is-time-series-analysis/

[16]          https://www.intellectyx.com/apache-hadoop-big-data-analytics-for-manufacturing/

The post Maximising Efficiency: Calculating AI-Inspired Overall Equipment Effectiveness for Factories appeared first on ODI.

From an operations perspective, first-line management has the most important role in the business, as they can switch on and switch off productivity on the shop floor.  We often underestimate the impact of a strong first-line manager who can

• motivate team members,

• align team and individual goals with business objectives, and

• facilitate continuous improvement within their teams.

Generally, more than 80% of the workforce is on a first-line team level, therefore the realisation of strategic goals and objectives depends on the successful linking of individual efforts, skills, and commitment to the vision and key success factors of the organisation. Much of this responsibility rests on the shoulders of the first-line management. It is therefore critical that organisations are continuously focused on aligning and engaging first-line managers whilst evaluating and enhancing their skills and knowledge.

Recently, Wispeco Aluminium, embarked on a company-wide effort to re-align and re-energise the first-line management team. ODI’s Director: Johan Benadie, facilitated multiple sessions with first-line managers from all business divisions. These sessions commence with a simple, yet fundamental question posed to first-line managers… Why do people work?

The answer to this question may present itself in several different constructs but they all share the same golden thread… we all work to have a better life for our families. If we then continue to work for a successful organisation, we increase the probability of securing a better life for ourselves in the future. In this way, every individual should acknowledge and understand their obligation towards the future and current success of the business.

According to Dr. Eliyahu Goldratt, there are three necessary conditions for a successful organisation:

• Make money now as well as in the future.
• Provide a secure and satisfying environment to employees now as well as in the future.
• Provide satisfaction to the market now as well as in the future.

To secure the sustainable profitability of the business, the company should be committed to keeping customers happy while simultaneously driving employee engagement. Both aspects are equally important as they are mutually interdependent.  This reminds me of a quote by Zig Ziglar saying – “You don’t build a business, you build people, and then people build the business.”

From an operations point of view, customer satisfaction depends on

• the continued supply of consistent and reliable quality,

• delivered on time and at a reasonable price.

The operational teams rarely have control over pricing, but they play a significant role in increasing the margin by controlling costs and driving productivity. On the other hand, employee engagement is slightly more complex since it is influenced by multiple factors. These factors can be categorised under two overarching themes – Motivation and Safety.

For a sustainably successful business, all teams and functions should pursue better quality, lower costs, and faster delivery times. This brings us back to the critical role of the first-line management who leads their teams in this relentless pursuit. Our first-line managers should be sufficiently skilled and equipped to

• simultaneously monitor and improve Quality (Q),
• Cost Productivity (C),
• Delivery (D),
• Safety (S), and
• Motivation (M) in their teams and as we all know, this is no easy task.

So how do we ensure that our first-line management level is sufficiently skilled and equipped to realise the goals and objectives of the business?

The implementation of Mini-Business Areas, through Key 2, provides the necessary structure for first-line managers and their teams, to monitor QCDSM trends daily and follow the CAPDo cycle for the generation and implementation of improvement suggestions from team members. This quick feedback loop ensures that performance momentum is maintained, and team members are aligned at the start of the shift.

The first-line manager can use the Mini-Business meeting to encourage the early detection of problems. Early detection of problems has two distinct benefits; we can correct the problem quickly and the cost of correction is much less. In this way, we achieve a quality-focused workplace where every team member takes the responsibility of quality at the source and doing things “first time right”.

The implementation of Small Group Activities through Key 3 provides the necessary structure for first-line managers to solve problems effectively with their team members. By following the correct problem-solving methodology, the team can brainstorm ways to remove waste from the system and increase productivity or reduce costs. First-line managers must remain objective throughout the process and allow input from all team members. They also have to be well-versed in root-case analysis techniques and methods.

As previously mentioned, motivation of staff is one of the fundamental predictors of the future success of the business. It consists of many small parts that add up to the whole.

Of course, the expectation of being fairly remunerated is a non-negotiable but employee engagement is also influenced by questions like:

• Do I know what is expected of me?

• Do I have the necessary tools and skills to do what is expected of me?

• Do I have a leader that supports me?

• Do I understand the goals and objectives of the business?

• Do I understand the value that I bring to the team?

• Am I sufficiently trained for the tasks that I have to perform?

• Are there any growth opportunities?

• Are my ideas and suggestions encouraged and welcomed?

The first-line manager has a significant influence on the workplace “experience” of their team members which ultimately impacts motivation.  The first-line managers should be equipped and skilled to answer most of these questions and also facilitate participation from team members. A formal qualification creates a solid foundation and should be the starting point. ODI offers a supervisory qualification called Supervision for Energised WorkTeams (NQF level 3). The qualification together with workplace coaching, regular check-ins, and refresher sessions ensures that the momentum is maintained, gaps are identified, and the internal capability of first-line managers is continuously refined and enhanced.

If the CEO is the Head of Culture, then the first-line managers are the Hands and Feet of Culture in the workplace, and as we are frequently reminded by Peter Drucker – “Culture eats strategy for breakfast”. We may have an intention of what we would like the culture of the organisation to be, but the first-line management will be the practical illustration of what the culture of the organisation truly is.

Author: Esbe du Toit: Senior Consultant

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