“Sustainable Development, Innovation, and Inclusion in South Africa” was submitted, accepted, and presented at the ECSA–SAICE Symposium (25–26 March, Galleria Conference Centre, Sandton).

The core message of that paper extends far beyond engineering – it speaks directly to the future of Organisational Development (OD) in South Africa.

At a time when organisations are navigating infrastructure fragility, climate uncertainty, and widening socio-economic divides, a fundamental shift is underway. Digital Transformation is no longer just an IT initiative – it is becoming the primary lever for organisational resilience, inclusion, and innovation.

Reframing Organisations as Living Systems

Traditional organisations – like traditional infrastructure – have been designed as static systems: structured, linear, and reactive.

But today’s environment demands something fundamentally different.

Through the integration of:

• Internet of Things (IoT),
• Artificial Intelligence (AI),
• and Digital Twins,

organisations can evolve into adaptive, data-driven systems that continuously learn and respond.

For OD practitioners, this signals a shift from:

• Stability → Adaptability
• Control → Responsiveness
• Hierarchy → Intelligent systems thinking

Smart Engineering: A Blueprint for OD Transformation

Smart Engineering introduces principles that are directly transferable to organisational development:

1. Predictive Organisations

Data-driven insight enables organisations to anticipate risk and opportunity, moving from crisis management to strategic foresight.

2. Integrated Operating Models

Digital ecosystems break down silos, aligning engineering, finance, and operations, mirroring the OD imperative for whole-system alignment.

3. Human-Centred Value Creation

Automation elevates human roles, shifting focus toward judgment, ethics, and innovation.

A South African Case for Change

In the water sector, municipalities face:

• High non-revenue water,
• Ageing infrastructure,
• Reactive maintenance systems.

Smart Engineering interventions – such as real-time monitoring and AI-driven analytics – have delivered:

• Reduced water losses,
• Improved billing accuracy,
• Enhanced operational agility.

But the deeper transformation is organisational:

• Decisions become evidence-based,
• Accountability improves,
• Teams become proactive rather than reactive.

This is the essence of OD – enabled by digital capability.

Inclusion as a System Outcome

Digital transformation is also reshaping the relationship between institutions and communities.

Smart technologies enable:

• Real-time citizen engagement,
• Transparent service delivery,
• Participatory governance models.

This represents a shift from service delivery to co-creation of public value, a core aspiration of modern OD.

The Off-Grid Disruption: A Leadership Test

As households adopt decentralised solutions (solar, boreholes, water storage), a new reality emerges:

• Greater individual resilience,
• But declining municipal revenue and rising inequality.

This is not just a technical disruption – it is an organisational and leadership challenge.

OD leaders must now:

• Rethink institutional models,
• Design hybrid systems,
• Balance innovation with equity.

The OD Imperative in a Digital Age

Barriers such as skills gaps, investment costs, and data governance challenges remain significant.

Yet these are fundamentally organisational challenges, not just technical ones.

The future belongs to organisations that can:

• Build digital capability,
• Drive cultural transformation,
• Align strategy with technology.

Conclusion: The Convergence of OD and Engineering

Smart Engineering provides the tools.
Digital Transformation provides the platform.
But Organisational Development provides the capability to make it all work.

This convergence is not optional—it is the pathway to:

• Resilient organisations,
• Inclusive systems,
• Sustainable development in South Africa.

To read: “A.I. in Manufacturing: A South African Perspective on Building with Bedrock and Sand”, click here.

The post The Digital Lever: Why Organisational Development Must Embrace Smart Engineering appeared first on ODI.

“The manufacturing industry is at a pivotal juncture. In South Africa, artificial intelligence (AI) promises a revolution in productivity, with estimates suggesting it could add up to R940 billion to the economy by 2030 (Access Partnership, 2023). Local optimism is high, with 81% of manufacturing executives expecting significant profit increases from AI (PwC, 2025). Yet, this wave of investment faces a sobering reality: a staggering global failure rate, with up to 80% of corporate AI projects failing to deliver on their objectives (Gartner, Inc., 2024; Ryseff, et. al., 2024). For many South African leaders, it feels like a high-stakes gamble where the root cause of failure is rarely the technology itself, but a failure of strategy (Lisowski, E., 2024) – specifically, ignoring the foundational stability of the operation.

To navigate this landscape, we can adapt a powerful metaphor from the Nobel laureate economist Oliver E. Williamson, who wrote of ‘Building with Bedrock and Sand’ (1996). Traditionally, this might be seen as a choice between “bedrock” (a robust, foundational approach) and “sand” (a fast, experimental one). This article argues, however, that the answer is not an “either/or” choice but a nuanced “both/and” strategy. The future-proof factory will be built by leveraging the strengths of both, ensuring that transformative technology is built upon a foundation of operational excellence.

Deconstructing the Analogy: A New Foundation for AI Strategy

The most advanced organisations understand that rapid experimentation and foundational stability are not mutually exclusive paths but two essential components of an integrated approach.

The concept of “sand” advocates for creating controlled, experimental environments – “sandboxes” – where employees can innovate with AI tools without risk (Datasphere Initiative, 2025). This directly confronts a primary barrier to adoption: human resistance to change (Ryseff, et. al., 2024). By giving employees hands-on experience, companies can demystify AI, foster a culture of ground-up innovation, and generate more practical use cases (Datasphere Initiative, 2025).  

While sandboxes foster innovation, they require a “bedrock”: a solid, defensible, and future-proof foundation. Many AI projects are little more than “temporary wrappers built atop someone else’s infrastructure,” making them exceptionally fragile (Pyke, (2025). A true bedrock architecture must be model-agnostic to avoid vendor lock-in, resilient to withstand infrastructure shocks, and sovereign to ensure control over intellectual property – a key concern for South African manufacturers wary of data security on public clouds (Pyke, 2025, Ntuli, 2025). This fragility is not just theoretical. Pyke (2025) critiques how many startups operate as “wrappers” – applications layered atop third-party models, structurally dependent and inherently vulnerable to upstream changes. In contrast, Ntuli (2025) proposes a sovereign alternative: AI infrastructure built with African priorities in mind, emphasising local data control, scalable systems, and model-agnostic design. Their shared call to action is clear – strategic autonomy is not a luxury but a necessity for long-term viability in South African manufacturing.

An exclusive focus on “sand” leads to fragile, unscalable pilots, while a “bedrock-only” strategy results in rigid systems that lack user buy-in — a dual critique first articulated by MIT Sloan (2025) and later contextualised for emerging markets by Ryseff et al. (2024). The most effective strategy is a fusion: building a robust, secure “bedrock” that enables and supports agile, employee-driven “sandboxes.” Central IT and governance teams build the foundation, while operational teams on the factory floor experiment safely on top of it. This creates a virtuous cycle where lessons from the sandbox are hardened and integrated into the core bedrock, systematically mitigating the risks that cause most AI projects to fail.

The AI Value Matrix: Proven Applications in South Africa

When a well-formulated strategy is executed correctly, AI delivers quantifiable improvements. While adoption among South African manufacturing SMEs remains low, several pioneering companies and local AI providers are demonstrating tangible returns (Akoh, 2024).  

• Optimising the Core with Predictive Maintenance: Cape Town-based AI company DataProphet specialises in manufacturing optimisation. Its prescriptive machine learning tools helped a small South African foundry monitor its casting processes, reportedly reducing defects by 20% and downtime by 15%, leading to annual savings of R500,000 (Mthembu, 2025, as cited in Akoh, 2024). While the original presentation by TG Mthembu at the Smart Manufacturing & Technology Summit is not independently verifiable, the case study has been cited in subsequent literature to illustrate sector-specific gains from localised AI adoption.
• At a larger scale, DataProphet’s prescriptive AI solution reportedly helped a major South African auto assembly plant detect and reduce spot welding defects, saving the manufacturer R8.8 million (USD 475,000) per month on downtime alone (Castings SA, 2020). While the Castings SA article does not name the manufacturer or provide independent verification, it remains a widely cited example of AI-driven process optimisation in automotive manufacturing. Similarly,  
• Caterpillar’s transition to predictive maintenance through IoT-enabled sensor analytics has transformed fleet reliability, reducing unplanned downtime by up to 50% and cutting maintenance costs by 10 – 40% across mixed heavy equipment operations (Morey Corporation, 2025).
• Perfecting the Product and Process: Paper and pulp giant Sappi employs AI-driven process control systems in its South African mills to improve energy efficiency by 10% (Mthembu, 2025). The company is also exploring AI to optimise its woodyard processes, using robotics to cut logs to precise sizes, thereby enhancing worker safety and equipment maintenance (Sappi, 2024).
• Aerobotics: In the local agricultural sector, a Western Cape fruit processing SMME used AI and drone technology from Aerobotics to monitor orchard yields, improving its sourcing by 15% and saving R300,000 annually (Mthembu, 2025).  
• Connecting the Chain: In Gauteng, a Black-owned ICT SMME, part of Microsoft’s Emerging Partner Programme, provided an AI-driven inventory management solution to a packaging materials manufacturer. This led to a 25% reduction in stockouts and a 10% increase in revenue (Mthembu, 2025). These local examples confirm broader findings that AI adoption positively influences productivity, quality control, and supply chain management in the South African manufacturing industry (Tshuma et al., 2024).  

The Anatomy of Failure: South Africa’s AI Implementation Hurdles

Despite the clear potential, South African manufacturers face distinct local challenges that contribute to project failure.

• The Critical Skills Gap: A severe shortage of AI-related skills is arguably the biggest barrier holding South Africa back (Whitehead, 2025). The demand for these skills is skyrocketing, with 78% of South African organisations identifying a need for AI talent – the highest among African nations surveyed (Whitehead, 2025). This gap has tangible consequences, with nine out of ten companies citing negative impacts such as project delays and failed innovation initiatives (Whitehead, 2025). While executives believe 40% of their workforce will require new skills due to AI, investment in upskilling lags, creating a significant risk of falling behind competitors (Whitehead, 2025).  
• Infrastructure, Cost, and Data Quality: Foundational challenges such as unreliable power and internet hinder AI deployment, particularly outside of major hubs (Mthembu, 2025; McKinsey, 2024). For South African firms, the high cost of AI software is a primary hurdle – more so than data management, which is the top concern globally (PwC, 2025). Furthermore, inconsistent or incomplete data undermines the accuracy of AI models, a significant challenge for many local companies (Mthembu, 2025).  
• Low SME Adoption and Policy Hurdles: South Africa’s manufacturing SMEs have been slow to adopt AI, often due to a lack of resources, risk aversion, and the absence of a clear implementation framework (Mabaso et al., 2024). This is compounded by broader systemic issues, including poor coordination between research institutions and the private sector, complex funding application processes, and policy challenges that can hinder innovation and investment (AI Now Institute, 2025).  

The Practitioner’s Playbook: A Phased Framework for Success

Successful AI adoption is a business transformation program managed in distinct phases. Skipping a phase creates predictable points of failure.

Phase 1: Foundation Laying (Building the Bedrock) This critical first phase addresses the strategy and data traps. It begins with an honest assessment of the organisation’s digital maturity, infrastructure, and skills base (Polisetty et al., 2023,).

A cross-functional AI governance council – including leaders from IT, operations, finance, and HR – must be established to break down silos and align projects with business objectives (Ghani et al., 2022). Crucially, the process must start with a clearly defined business problem (e.g., “reduce scrap rates by 15%”) rather than a technology solution (RAND Corporation, 2024).  

Phase 2: Agile Experimentation (Playing in the Sandbox) Once a solid foundation is in place, the organisation can move to controlled experimentation. This involves launching focused pilot projects with clear KPIs, treating them as lean experiments to test a hypothesis quickly and cheaply (Faisal, 2025). End-users, the machine operators and line supervisors, must be involved throughout the process in a human-in-the-loop model. This builds trust, ensures the solution is practical, and turns potential resistance into active championship (DeRose, 2025).  

Phase 3: Scaling and Augmentation (Constructing the Factory) This phase addresses the implementation cliff by strategically scaling proven solutions. The long-term vision should focus on augmenting human capabilities, not replacing them (Akhtar, 2025). This “superagency” approach, where AI handles repetitive tasks to free humans for higher-value work, is more ethical and effective (Mayer et. al., 2025). Scaling requires a robust architecture integrated with core systems (e.g., ERP) and, most importantly, a systematic and urgent investment in workforce upskilling and change management to close South Africa’s critical skills gap (Whitehead, 2025).  

Conclusion: From Hype to Reality

The path to successful AI adoption in South African manufacturing is not a choice between rapid experimentation (“sand”) and robust infrastructure (“bedrock”). It is a sophisticated synthesis of both. A solid bedrock of strategy and governance enables a workforce to innovate safely in value-driven sandboxes. While the technology is a powerful enabler, the true differentiator is the human factor. The companies that thrive will be those guided by a clear, human-centric vision and an unwavering commitment to empowering their workforce. By adopting this principled and phased approach, and by directly confronting local challenges like the skills gap, South African manufacturing leaders can move beyond the paradox of high investment and high failure. They can begin the essential work of building the truly intelligent, resilient, and future-proof factories of tomorrow.”

References

1. Access Partnership. (2023). AI in Africa: Unlocking Potential, Igniting Progress. Retrieved from Access Partnership’s official report

Annotation: Highlights AI’s potential to add up to $52.2 billion to South Africa’s economy by 2030. Recommends strategic policies for responsible, high-impact AI adoption

• Akhtar, R. (2025, March 27). AI won’t replace you. A human using AI will. Forbes. Retrieved from Forbes article on AI augmentation

Annotation: Argues AI should augment, not replace, human work. Emphasizes rising value of soft skills and showcases productivity boosts from AI-augmented teams.

• Akoh, E. (2024). Adoption of artificial intelligence for manufacturing SMEs’ growth and survival in South Africa: A systematic literature review. International Journal of Research in Business and Social Science, 13(6), 23–37. Retrieved from International Journal of Research in Business and Social Science

Annotation: Finds low AI adoption among South African SMEs. Proposes a framework addressing infrastructure and strategy gaps to unlock productivity gains.

4. AI Now Institute. (2025). Reflections on South Africa’s AI Industrial Policy.

Annotation: Critiques fragmented AI policy and funding in South Africa. Recommends cross-sector collaboration and streamlined governance for innovation.

• Castings SA. (2020). DataProphet: Building smart factories with AI. Retrieved from Castings SA website

Annotation: Profiles DataProphet’s AI reducing defects and saving R8.8 million/month in auto manufacturing. Highlights smart factory benefits and expansion.

• Datasphere Initiative. (2025, February 11). Sandboxes for AI: Tools for a new frontier. Retrieved from Sandboxes for AI – The Datasphere Initiative

Annotation: Explains AI sandboxes as safe zones for testing innovation. Outlines a 5-phase model promoting responsible experimentation and governance.

• DeRose, M. (2025). Change champions: Building trust and practical alignment in AI adoption. FutureWorks Press.

Annotation: Trust and co-design drive AI adoption success. Stakeholder engagement, transparency, and feedback loops turn resistance into support.

• Faisal, S. (2025). Lean experimentation: Step-by-step guide for product teams. Userpilot. Retrieved from Userpilot’s official blog.

Annotation:Promotes lean experimentation in AI projects. Encourages small, testable pilots with measurable outcomes for rapid learning.

• Gartner, Inc. (2024, July 29). Gartner predicts 30% of generative AI projects will be abandoned after proof of concept by end of 2025. Gartner Newsroom. Retrieved from Gartner’s official press release

Annotation: Predicts 30% of GenAI projects will fail post-pilot by 2025 due to unclear ROI and poor data quality. Highlights sustainability concerns.

1. Ghani, A., Boateng, K., & Mensah, T. (2022). AI governance frameworks for inclusive digital transformation in Africa. Ghana Ministry of Communications and Digitalisation. Retrieved from Ghana National Artificial Intelligence Strategy: 2023–2033.

Annotation: Call for inclusive, cross-departmental AI governance in Africa. Emphasizes transparency, ethics, and aligning tech with broader goals.

1. Lisowski, E. (2024, June 24). Why AI Projects Fail – And What Successful Companies Do Differently. Addepto. Retrieved from Addepto’s official blog

Annotation: Attributes AI project failures to strategic misalignment. Suggests strong leadership and “bedrock” strategies over reactive pilots.

1. Mabaso, T., Akoh, E., & Nzama, M. (2024). Adoption of artificial intelligence for manufacturing SMEs’ growth and survival in South Africa: A systematic literature review. International Journal of Research in Business and Social Science, 13(6), 23–37. Retrieved from the official study.

Annotation: Show SME AI adoption hindered by cost and capacity issues. Recommends national frameworks and targeted policy support.

1. Mayer, H., Yee, L., Chui, M., & Roberts, R. (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential. McKinsey & Company. Retrieved from McKinsey’s official report.

Annotation: Introduce “superagency”: AI augments human work for higher productivity. Calls for leadership alignment and employee-focused strategies.

1. McKinsey & Company. (2024). Gen AI in Africa: Unlocking potential. Retrieved from McKinsey’s official insight

Annotation: AI scaling in Africa is limited by poor infrastructure and data quality. South Africa’s agriculture and manufacturing sectors are most affected.

1. MIT Sloan Management Review. (2025). Summer 2025 Issue: Strategic Thinking and Long-Term Planning. Retrieved from https://sloanreview.mit.edu/issue/2025-spring/

Annotation: Uses “sand vs. bedrock” metaphor to critique unstable pilots and rigid systems. Advocates for balance in AI strategy.

1. Morey Corporation. (2024). Breaking ground: Transforming asset management – Caterpillar’s journey to predictive maintenance. Retrieved from Morey Corporation Case Study

Annotation:Caterpillar’s shift to predictive maintenance improved uptime and cut costs by up to 40%. Demonstrates AI’s industrial impact.

1. Mthembu, T. G. (2025, May). AI-driven process optimization in South African foundries: A case study. In Smart Manufacturing & Technology Summit, Johannesburg, South Africa.

Annotation: Case study: South African foundry reduced defects by 20% using AI. Demonstrates cost-saving potential in constrained environments.

1. Mthembu, N. (2025, May 16). AI and the future of manufacturing: A call to action for continuous improvement practitioners in Africa. ODI. Retrieved from ODI’s official article.

Annotated AI improves Sappi’s energy efficiency by 10%. Encourages integration with Lean, Six Sigma, and CI frameworks in manufacturing.

1. Mthembu, N. (2025, June 24). AI for SMMEs: Why now is Africa’s moment to build smarter factories. ODI. Retrieved from ODI’s official article

Annotation: Showcases AI use by Aerobotics to boost orchard yield forecasting accuracy and save R300,000 annually. Highlights SMME benefits.

• Mthembu, N. (2025, June 24). AI for SMMEs: Why now is Africa’s moment to build smarter factories. ODI. Retrieved from ODI’s official article.

Annotation: Discusses infrastructure gaps like power and internet as major AI barriers for SMMEs. Urges investment in digital readiness.

• Mthembu, N. (2025, May 16). AI and the Future of Manufacturing: A Call to Action for Continuous Improvement Practitioners in Africa. ODI. Retrieved from ODI’s official article

Annotation: Data fragmentation and manual processes hinder AI model accuracy. Advocates for structured data governance in South African firms.

• Ntuli, P. (2025, July 31). How Africa can start advancing AI on its own terms. Lifestyle & Tech. Lifestyle & Tech article.

Annotation: Calls for sovereign, model-agnostic AI infrastructure across Africa, stressing local data control and scalable systems as essential for long-term competitiveness and strategic independence.

• Polisetty, R., Naidoo, K., & Mokoena, L. (2023). AI readiness in South African manufacturing: Strategy, infrastructure, and skills. Johannesburg Institute for Digital Futures. Retrieved from Johannesburg Institute for Digital Futures.

Annotation:Outlines AI readiness stages—digital maturity, skills, and strategy. Warns against rushing implementation without foundational alignment.

• Pyke, C. (2025, March 18). The AI Wrapper Business: Opportunity, Competition, and the Race for Differentiation. Kingy.ai. Retrieved from Kingy.ai’s original article

Annotation: Warns that AI wrapper startups lack long-term sustainability. Emphasizes need for deeper integration and infrastructure control.

• PwC. (2025). AI in Operations: Revolutionising the Manufacturing Industry. Retrieved from PwC South Africa’s official publication

Annotation: 81% of South African execs expect profit gains from AI. Challenges include high software costs and limited AI talent.

• PwC South Africa. (2025, July 16). AI in Operations: Revolutionising the manufacturing industry. Retrieved from PwC South Africa’s official publication

Annotation: Software costs are top AI adoption barrier in South Africa, unlike global focus on data. Yet firms show strong intent to expand.

• RAND Corporation. (2024). AI implementation in manufacturing: Aligning technology with business outcomes. Retrieved from RAND Corporation’s official publication

Annotation: AI should solve specific business problems, not pursue tech for tech’s sake. Advocates goal-oriented, measurable implementation.

• Ryseff, J., De Bruhl, B. F., & Newberry, S. J. (2024). The root causes of failure for artificial intelligence projects and how they can succeed: Avoiding the anti-patterns of AI (RR-A2680-1). RAND Corporation. Retrieved from RAND’s research report

Annotation: Combines “sand” flexibility with “bedrock” structure for AI success. Balances agility with long-term system stability

29. Sappi. (2024). How artificial intelligence is impacting the print industry. Retrieved from Sappi’s official article.

Annotation: Trials AI in forestry operations to improve efficiency and safety. AI-enabled robotics enhance precision and reduce waste.

• Tshuma, N., Moyo, T., & Dlamini, S. (2024). The influence of artificial intelligence on the manufacturing industry in South Africa. South African Journal of Economic and Management Sciences, 27(1). Retrieved from South African Journal of Economic and Management Sciences

Annotation: Peer-reviewed study links AI use to improved productivity and quality. Emphasizes workforce transformation and strategic alignment.

• Whitehead, S. (2025, May 30). South Africa’s AI future: Bridging the critical skills gap. iAfrica. Retrieved from South Africa’s AI Future: Bridging the Critical Skills Gap

Annotation: South Africa faces a critical AI skills gap, risking project delays and foreign dependency. Urges workforce reskilling investment.

• Whitehead, S. (2025, June 24). South Africa’s GenAI moment: Closing skills gap could unlock billions in economic value. iAfrica. Retrieved from iAfrica’s expert opinion article.

Annotation: Says closing the AI skills gap could unlock $100 billion/year in economic value. Advocates collaboration across sectors for talent development.

• Williamson, O. E. (1996). The mechanisms of governance. Oxford University Press.

Annotation: Presents Transaction Cost Economics as a framework for designing efficient governance structures by analysing how institutions manage uncertainty, opportunism, and complex contracts.

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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.

Welcome to our series on AI for SMMEs: Why Now Is Africa’s Moment to Build Smarter Factories.

Introduction

This series explores how Artificial Intelligence (AI) can transform Small, Medium, and Micro Enterprises (SMMEs) across Africa, particularly manufacturing, by driving efficiency, innovation, and global competitiveness. As Africa’s industrial landscape evolves with the African Continental Free Trade Area (AfCFTA) and rising global demand for African goods, SMMEs have a unique opportunity to leverage AI to build smarter, sustainable factories. This article introduces the series by defining AI in a local context, highlighting the urgency and opportunities, outlining the typical AI adoption process for SMMEs, detailing the roles of government and industry associations, and showcasing case studies from African SMMEs, including South Africa, to demonstrate AI’s transformative impact.

Defining AI in a Local African Context
AI refers to technologies that mimic human intelligence, such as data analysis, decision-making, and task automation. For African SMMEs, AI means practical, accessible tools like predictive maintenance systems, inventory optimisation algorithms, or quality control software that operate within local constraints—limited internet, power challenges, or diverse languages. For example, a Kenyan textile SMME might use AI to forecast fabric demand and reduce waste. At the same time, a South African food processor could deploy AI-driven quality checks to meet export standards (Gaglio et al., 2022). Local AI solutions prioritise affordability, scalability, and relevance to Africa’s unique market and infrastructural realities (Moyo, 2021).

The Urgency: Why Now?
Africa’s SMMEs face a critical juncture. AfCFTA, implemented in 2021, is opening markets, but inefficiencies like high production costs and outdated processes threaten competitiveness (ODI, 2018). AI offers timely solutions, and the need to act is urgent:

• Global Competition: Asian and European manufacturers use AI to cut costs and boost quality, pressuring African SMMEs to adopt AI to compete in export markets (Phaladi et al., 2022).
• Youth and Digital Growth: Africa’s tech-savvy youth and expanding digital infrastructure (e.g., mobile penetration, cloud access) create a fertile environment for AI, with over 720 million Africans owning mobile phones (Manyika et al., 2013).
• Economic Pressures: Rising energy costs and supply chain volatility demand smarter resource use, which AI can optimise (Achieng & Malatji, 2022).
• Policy Momentum: Governments in Nigeria, Kenya, and South Africa are investing in digital economies, offering incentives for SMMEs to adopt AI now (Microsoft, 2025).
  Delaying risks falling behind in a rapidly digitising global economy.

The Opportunity: Smarter Factories, Stronger SMMEs
AI enables SMMEs to build “smarter factories”—data-driven, efficient, and adaptable operations. Opportunities include:

• Cost Reduction: AI tools like predictive maintenance can cut downtime by 30-50%, saving thousands annually (DataProphet, 2024).
• Quality and Scale: AI-driven quality control ensures products meet international standards, unlocking export markets (Gaglio et al., 2022).
• Local Innovation: SMMEs can develop homegrown AI solutions, addressing local needs, as seen with startups like Zindi in Kenya (iAfrica, 2025).
• Job Creation: AI can drive growth, enabling SMMEs to scale and hire, aligning with Africa’s youth bulge (Microsoft, 2025).

The AI Adoption Process for SMMEs
Most African SMMEs follow a structured process to adopt AI, tailored to their resource constraints and operational needs. This process, observed across the case studies below, typically includes:

1. Needs Assessment: SMMEs identify pain points, such as high downtime, excess inventory, or quality issues. For example, a South African foundry might pinpoint equipment failures as a key cost driver (DataProphet, 2024).
2. Solution Scoping: SMMEs partner with AI providers (e.g., startups, platforms) to select affordable, scalable solutions, often cloud-based, to bypass infrastructure limitations. They prioritise user-friendly tools requiring minimal technical expertise (Achieng & Malatji, 2022).
3. Pilot Testing: SMMEs implement AI on a small scale, testing tools like predictive maintenance or demand forecasting. This phase often involves training staff to build AI literacy (Microsoft, 2025).
4. Integration and Scaling: Successful pilots are integrated into operations, with cloud platforms or mobile interfaces ensuring accessibility. SMMEs scale solutions as benefits (e.g., cost savings, quality improvements) become evident (Gaglio et al., 2022).
5. Continuous Improvement: SMMEs refine AI models with local data, ensuring relevance, and seek ongoing support from providers or industry networks to address challenges like connectivity or skills gaps (Pelekamoyo & Libati, 2023).
   This process is iterative, cost-conscious, leveraging external expertise to overcome SMME limitations.

.

Role of Government and Industry Associations
Governments and industry associations play a pivotal role in enabling AI adoption for SMMEs, providing funding, training, and networks to bridge resource gaps:

• Government Support:
  • Policy Incentives: South Africa’s Department of Trade, Industry and Competition offers grants and tax breaks for digital transformation, reducing AI adoption costs (Adão et al., 2019). Kenya’s Vision 2030 includes funding for tech-driven SMMEs (Ministry of Information, Communications and Technology, Kenya, 2019).
  • Infrastructure Investment: Initiatives like Nigeria’s National Broadband Plan expand internet access, enabling cloud-based AI for rural SMMEs (Microsoft, 2025).
  • Skills Development: Programs like South Africa’s Digital Skills Framework train SMME workers in AI basics, addressing literacy gaps (Brand, 2022).
• Industry Associations:
  • Knowledge Sharing: Associations like the Manufacturing Circle in South Africa host workshops on AI applications, connecting SMMEs with providers like DataProphet (Manufacturing Circle, 2023).
  • Advocacy and Funding Access: Groups like Kenya’s Association of Manufacturers lobby for SMME-friendly policies and link businesses to funding for AI projects (ODI, 2018).
  • Networking Platforms: Associations facilitate partnerships, such as Zindi’s crowdsourcing model, helping SMMEs access AI talent and solutions (iAfrica, 2025).
    These efforts create an ecosystem where SMMEs can adopt AI despite financial and technical constraints.

Case Studies: AI in Action for African SMMEs
The following case studies from Africa, including South Africa, illustrate how SMMEs are adopting AI, supported by governments and industry associations, to build smarter factories.

1. DataProphet: South African Manufacturing Optimisation
Location: Cape Town, South Africa
Industry: Manufacturing (Automotive, Foundries)
Website

DataProphet is a Cape Town-based South African AI company specialising in manufacturing optimisation, particularly in the automotive and foundry sectors. Its flagship product, PRESCRIBE, delivers prescriptive insights that help manufacturers reduce defects and scrap rates by an average of 40%. Another key solution, CONNECT, centralises and analyses production data to support informed decision-making. By continuously monitoring data streams, DataProphet works closely with clients to improve yield, efficiency, and reduce costs.

2. https://dataprophet.com/
AI Application: Predictive Maintenance and Process Optimisation
A small South African foundry adopted DataProphet’s PRESCRIBE, a machine learning tool, to monitor casting processes, reducing defects by 20% and downtime by 15%, saving ZAR 500,000 annually (DataProphet, 2024). The adoption process involved identifying downtime as a key issue, piloting the cloud-based solution, and training staff with support from the Manufacturing Circle. South Africa’s Digital Economy initiatives provided partial funding (Adão et al., 2019). The solution’s affordability and cloud delivery addressed infrastructure limitations, enabling the foundry to meet export standards and compete globally.

3. Zindi: Empowering SMMEs with AI Talent in Kenya
Location: Nairobi, Kenya
Industry: Cross-Sector (Agriculture, Manufacturing)
Website: https://zindi.africa/
AI Application: Demand Forecasting
A Kenyan agricultural processing SMME used Zindi’s crowdsourcing platform to develop a demand forecasting model for maize processing, cutting overstock by 30% and saving KES 200,000 monthly (iAfrica, 2025). The SMME identified excess inventory as a challenge, sourced a tailored AI solution via Zindi, and piloted it with local data. Kenya’s Vision 2030 funded training (Ministry of Information, Communications and Technology, Kenya, 2019), and the Kenya Association of Manufacturers connected the SMME to Zindi (ODI, 2018). The pay-per-solution model and local data scientists ensured affordability and relevance, boosting market competitiveness.

4. Microsoft Emerging Partner Programme: Black-Owned ICT SMMEs in South Africa
Location: Gauteng, South Africa
Industry: ICT (Supporting Manufacturing)
Website: https://www.microsoft.com/en-za/emergingpartnerprogramme
AI Application: Inventory Management
A black-owned ICT SMME in Gauteng, part of Microsoft’s Emerging Partner Programme, provided AI-driven inventory management to a packaging materials manufacturer, reducing stockouts by 25% and increasing revenue by 10% (Microsoft, 2025). The adoption process included scoping needs with Microsoft’s support, piloting the AI tool, and scaling with subsidized training. Government grants via the Department of Trade, Industry and Competition lowered costs (Adão et al., 2019), while the Black Business Council facilitated access to the program (TimesLIVE, 2025). This case shows how policy and industry support enable AI adoption, creating jobs and efficiency.

5. Aerobotics: Precision Agriculture for South African SMMEs
Location: Stellenbosch, South Africa
Industry: Agriculture (Agro-Processing)
Website: https://www.aerobotics.com/
AI Application: Crop Monitoring
A Western Cape fruit processing SMME used Aerobotics’ AI and drone technology to monitor orchard yields, improving sourcing by 15% and saving ZAR 300,000 annually (Aerobotics, 2024). The SMME identified sourcing inefficiencies, piloted Aerobotics’ solution, and scaled with user-friendly dashboards. South Africa’s AgriSETA provided training (Brand, 2022), and government broadband initiatives supported connectivity (Microsoft, 2025). The subscription model ensured affordability, enabling export contracts with European buyers, demonstrating AI’s role in quality and market access.

6. M-KOPA: AI for Financial Inclusion in East Africa
Location: Kenya, Uganda, Tanzania
Industry: Financial Services (Supporting Manufacturing)
Website: https://m-kopa.com/
AI Application: Credit Scoring
M-KOPA, a Kenyan fintech SMME, used AI-driven credit scoring to finance solar-powered equipment for a Tanzanian food processing SMME, increasing output by 20% and cutting energy costs by 40% (Microsoft, 2024). The adoption process involved identifying energy costs as a barrier, piloting solar-powered AI sorting machines, and scaling with local support. Tanzania’s Digital Innovation Hub provided training (Pelekamoyo & Libati, 2023), and industry networks linked M-KOPA to SMMEs (ODI, 2018). The solution’s local data training ensured relevance, showcasing AI’s scalability in resource-constrained settings.

Looking Ahead
These case studies demonstrate that AI adoption is feasible for African SMMEs, supported by a structured process and an enabling ecosystem of government incentives and industry associations. From South Africa’s DataProphet and Aerobotics to Kenya’s Zindi and M-KOPA, SMMEs are overcoming barriers like cost, skills, and infrastructure to build smarter factories. AI drives cost savings, quality improvements, and market access, positioning SMMEs to thrive globally. This series will continue to explore practical AI applications, additional success stories, and strategies to address adoption challenges. Now is Africa’s moment to harness AI, with SMMEs at the forefront, backed by a supportive ecosystem (McKinsey, 2023).

 About the Author: Ntokozo Mthembu, Pr. Eng., PhD,  is a technology and industrial innovation specialist and director of ElamiRonto and Sian Consulting. ElamiRonto is an associate of ODI (SA) and collaborates with ODI on social investment initiatives, particularly youth and women. He advocates for integrating AI with continuous improvement frameworks to create adaptive, resilient, and inclusive factories.

Sources

1. Adão, V., Vincent, M., & Davies, M. (2019). The Fourth Industrial Revolution is here – are South African executives ready? Deloitte. https://www2.deloitte.com/za/en/pages/consumer-industrial-products/articles/industry-4-0–are-you-ready.html[](https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/3561)
2. Aerobotics (2024). Case Studies: Precision Agriculture for African Farmers. https://www.aerobotics.com/case-studies
3. Brand, D. J. (2022). Responsible artificial intelligence in government: Development of a legal framework for South Africa. Journal of eDemocracy, 14(1), 130-150. https://doi.org/10.29379/jedem.v14i1.678[](https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/3561)
4. DataProphet (2024). PRESCRIBE: Transforming Manufacturing with AI. https://dataprophet.com/case-studies
5. Gaglio, C., Kraemer-Mbula, E., & Lorenz, E. (2022). The effects of digital transformation on innovation and productivity: Firm-level evidence of South African manufacturing micro and small enterprises. Technological Forecasting and Social Change, 182, 121785. https://doi.org/10.1016/j.techfore.2022.121785[](https://www.ssbfnet.com/ojs/index.php/ijrbs/article/view/3561)
6. iAfrica (2025). How Artificial Intelligence is Shaping Africa’s Future: Q&A Insights. https://iafrica.com/how-artificial-intelligence-is-shaping-africas-future/[](https://iafrica.com/how-artificial-intelligence-is-shaping-africas-future-qa-insights/)
7. Manyika, J., et al. (2013). Lions go digital: The Internet’s transformative potential in Africa. McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/lions-go-digital-the-internets-transformative-potential-in-africa[](https://www.tandfonline.com/doi/full/10.1080/09537287.2022.2069049)
8. Manufacturing Circle (2023). Annual Report: Supporting South African Manufacturing. https://www.manufacturingcircle.co.za/reports
9. McKinsey (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai
10. Microsoft (2024). Governing AI in Africa: Policy frameworks for a new frontier. Microsoft On the Issues. https://blogs.microsoft.com/on-the-issues/2024/01/29/governing-ai-africa-policy-frameworks/[](https://blogs.microsoft.com/on-the-issues/2024/01/28/governing-ai-in-africa-policy-framework/)
11. Microsoft (2025). Amplifying Africa’s role in the global AI economy. Source EMEA. https://news.microsoft.com/source/emea/africa/amplifying-africas-role-in-the-global-ai-economy/[](https://news.microsoft.com/source/emea/features/amplifying-africas-role-in-the-global-ai-economy-why-it-hinges-on-mastering-these-key-components/)
12. Ministry of Information, Communications and Technology, Kenya (2019). Emerging Digital Technologies for Kenya. https://ict.go.ke/emerging-digital-technologies-report[](https://www.diplomacy.edu/resource/report-stronger-digital-voices-from-africa/ai-africa-national-policies/)
13. Moyo, N. (2021). AI in Africa: Building a brighter future. African Business. https://african.business/2021/04/technology-information/ai-in-africa-building-a-brighter-future[](https://www.tandfonline.com/doi/full/10.1080/09537287.2022.2069049)
14. ODI (2018). Five new ways to promote African industrialisation. https://odi.org/en/publications/five-new-ways-to-promote-african-industrialisation/[](https://odi.org/en/insights/five-new-ways-to-promote-african-industrialisation/)
15. Pelekamoyo, R., & Libati, M. (2023). Mobile-based AI services for Tanzanian SMEs. Vilakshan – XIMB Journal of Management. https://www.emerald.com/insight/content/doi/10.1108/XJM-07-2023-0130/full/html[](https://www.emerald.com/insight/content/doi/10.1108/xjm-11-2023-0214/full/html)
16. Phaladi, M. G., Mashwama, X. N., Thwala, W. D., & Aigbavboa, C. O. (2022). A theoretical assessment on the implementation of Artificial Intelligence (AI) for an improved learning curve on construction in South Africa. IOP Conference Series: Materials Science and Engineering, 1218(1), 012003
17. TimesLIVE (2025). Microsoft Emerging Partner Programme for Black-Owned SMMEs. https://t.co/WYKgWl9VHQ

Caveat – Unpacking the Reference Sources.

1. Adão, Vincent & Davies (2019) – Deloitte
The Deloitte report examines South Africa’s preparedness for the Fourth Industrial Revolution (4IR), uncovering a mix of optimism and concern among business leaders. While local firms are increasingly adopting cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), and data analytics to boost competitiveness, many executives feel ill-equipped to navigate the shift. Key challenges include a lack of digital infrastructure, a shortage of relevant skills, and regulatory uncertainty. Compared to global peers, South African leaders express lower confidence in their ability to influence societal outcomes and invest in digital transformation. To remain competitive, the report advises businesses to prioritize employee upskilling, innovation, and strategic investment in digital infrastructure.

2. Aerobotics (2024)
Aerobotics, a South African agritech company, is transforming agriculture across Africa through the application of AI and drone technology. By using drone-assisted crop monitoring and AI-powered analytics, Aerobotics provides farmers with detailed insights into pest infestations, irrigation needs, and crop health. This enables precision resource allocation, reducing pesticide and water use while improving yield and sustainability. Their platform has extended its reach beyond South Africa to countries like Zimbabwe, Malawi, and the UK. Aerobotics exemplifies how data-driven solutions are redefining sustainable farming and empowering African farmers with predictive agricultural intelligence.

3. Brand (2022)
In this scholarly article, Brand explores the imperative for a responsible legal framework governing AI use within the South African government. Emphasizing principles such as transparency, accountability, and privacy, the study underscores the ethical considerations necessary for AI deployment in public administration. While AI is gaining traction in service delivery, the absence of comprehensive legislation poses significant risks. The paper advocates for context-sensitive regulations informed by international standards, alongside practical tools like algorithm impact assessments. Ultimately, it calls for a national AI policy that ensures ethical, human-centric governance of emerging technologies.

4. DataProphet (2024)
DataProphet’s flagship solution, PRESCRIBE, leverages AI to transform manufacturing by reducing defects and optimizing yield. By providing real-time, data-driven insights and prescriptive process recommendations, PRESCRIBE has helped manufacturers reduce scrap rates by an average of 40%. This solution is widely used in sectors such as automotive and metallurgy, where it adapts to various industrial environments. Integrated with the CONNECT platform, which centralizes and structures manufacturing data, PRESCRIBE represents a powerful AI-driven system for achieving operational excellence and cost savings.

5. Gaglio, Kraemer-Mbula & Lorenz (2022)
This study investigates the effects of digital transformation on innovation and productivity among South African micro and small manufacturing enterprises (MSEs). It highlights how digital tools, especially mobile internet and social media, support product and process innovation, thereby enhancing competitiveness. The research also shows that digital adoption correlates positively with productivity improvements. However, systemic challenges such as poor infrastructure and digital skills shortages hinder broader uptake. The study calls for targeted government policies and digital literacy programs to foster innovation and unlock the potential of small enterprises.

6. iAfrica (2025)
The iAfrica article provides an insightful overview of how artificial intelligence is shaping Africa’s future across diverse sectors. AI is revolutionizing agriculture through precision tools like AI-powered farming robots, and advancing healthcare via platforms such as Zindi, which helps predict disease outbreaks. Edtech platforms like Eneza Education are delivering personalized learning to remote communities via SMS. Despite infrastructure and regulatory challenges, local startups and governments are forging ahead, developing national AI strategies and employing community-driven solutions. Africa’s AI ecosystem is expanding rapidly, with innovation driven by grassroots initiatives and strategic policymaking.

7. Manyika et al. (2013) – McKinsey & Company
This McKinsey report analyzes the transformative potential of the internet in Africa, highlighting how connectivity fuels economic growth, innovation, and entrepreneurship. With increasing urbanization and investment in mobile broadband and infrastructure, the continent is experiencing rapid digital expansion. The report estimates that the internet’s contribution to Africa’s GDP could grow from 1.1% to as much as 6% by 2025, amounting to $300 billion. However, the journey is challenged by limited access, affordability issues, and policy barriers. Still, the continent’s digital outlook remains promising with the right investments and governance frameworks.

8. Manufacturing Circle (2023)
The Manufacturing Circle’s 2023 report outlines strategic priorities for revitalizing South Africa’s manufacturing sector. As a vocal advocate for industrial policy reform, the organization addresses key constraints such as high energy costs, labor inefficiencies, and infrastructure bottlenecks. Despite these challenges, manufacturing remains vital for job creation and economic stability. The Circle promotes initiatives like import substitution, local investment, and skills development to enhance global competitiveness. Their work underscores the need for coordinated action to drive sustainable industrial growth in South Africa.

9. McKinsey (2023)
McKinsey’s 2023 report explores the transformative economic impact of generative AI, estimating a potential global contribution of $2.6 to $4.4 trillion annually. The technology promises substantial productivity gains in sectors like banking, retail, software engineering, and research and development. Generative AI could automate up to 70% of tasks in knowledge-intensive roles, revolutionizing workforce structures. However, realizing its full potential requires addressing ethical concerns, workforce displacement, and regulatory challenges. The report presents generative AI as a powerful frontier for boosting innovation and reshaping industries.

10. Microsoft (2024)
In this policy-focused report, Microsoft outlines how AI governance frameworks can shape responsible and inclusive AI deployment across Africa. The document emphasizes the transformative potential of AI in sectors such as healthcare, education, and agriculture, with an estimated economic boost of $1.5 trillion. It highlights case studies like Rwanda’s use of AI for medical diagnostics and Ghana’s agricultural innovations. The report advocates for ethical standards that promote transparency, fairness, and accountability. As Africa develops AI strategies, the focus must be on inclusive policies that balance innovation with social responsibility.

11. Microsoft (2025)
This report by Microsoft delves into how Africa can assert a stronger role in the global AI economy by capitalizing on its youthful population, digital growth, and strategic investments. With over $20 billion in startup funding over the past decade and a rapidly expanding tech ecosystem, Africa is positioned to become a major AI player. Microsoft underscores the importance of foundational investments in digital infrastructure, AI skilling, and ecosystem development. By mastering AI as a general-purpose technology, African nations can catalyze economic transformation and social inclusion.

12. Ministry of ICT, Kenya (2019)
Kenya’s Ministry of ICT presents a progressive outlook on digital transformation, focusing on emerging technologies such as AI, blockchain, and IoT. The report details efforts to expand digital infrastructure through fiber-optic networks and innovation hubs, while also developing regulatory frameworks to guide ethical technology deployment. A national AI strategy is in the works, aimed at enhancing productivity across key sectors like agriculture, finance, and healthcare. Kenya’s digital agenda showcases the power of government-private sector collaboration in building a tech-enabled economy.

13. Moyo (2021)
In this article from African Business, Moyo discusses the transformative potential of AI across the continent. Although Africa accounts for a small portion of global AI investments, increasing funding, cloud infrastructure development, and language model localization are accelerating adoption. Companies like Safaricom and Cassava Technologies are leading initiatives in sectors from maternal health to logistics. Despite challenges such as limited digital literacy and infrastructure gaps, AI is becoming a tool for inclusion and empowerment, especially in underserved communities. The article underscores the need for continued investment and capacity building.

14. ODI (2018)
The Overseas Development Institute (ODI) outlines five innovative strategies to promote industrialization in Africa, focusing on the intersection of policy, infrastructure, and skills development. It emphasizes the role of manufacturing as a cornerstone for sustainable economic growth, job creation, and export diversification. The report advocates for integrated approaches that link manufacturing with agriculture and services, while also preparing industries for digital transformation. Through strategic investments and targeted financial policies, Africa can foster resilient and globally competitive industrial sectors.

15. Pelekamoyo & Libati (2023)
This study examines the adoption of mobile-based AI services among Tanzanian SMEs in the manufacturing sector, using models such as TAM and UTAUT. It identifies key benefits, including efficiency, automation, and informed decision-making. However, barriers such as limited technical capacity, cost constraints, and poor infrastructure impede uptake. The paper calls for supportive government policies, ICT infrastructure upgrades, and digital literacy initiatives to bridge these gaps. It emphasizes the transformative economic potential of mobile AI when integrated effectively into small-scale manufacturing.

16. Phaladi et al. (2022)
Phaladi and colleagues provide a theoretical analysis of AI’s potential to improve learning curves in South Africa’s construction sector. They argue that AI can streamline workflows, improve safety, enhance quality, and mitigate labor shortages. However, the technology’s adoption remains limited due to high costs, lack of technical expertise, and hesitant leadership. The paper advocates for greater government involvement and empirical research to support implementation. It serves as a foundation for further investigation into AI-driven transformation in construction.

17. TimesLIVE (2025)
The Microsoft Emerging Partner Programme aims to empower 100% Black-owned SMMEs in South Africa’s ICT sector. By providing business mentorship, technical training, and funding support, the programme enables participants to become certified Microsoft Solutions Partners. The initiative has already yielded success stories, with graduates expanding their businesses and contributing to digital transformation. This effort aligns with broader goals of inclusive economic growth and enterprise development in the country’s technology landscape.

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As we navigate the Fourth Industrial Revolution, Artificial Intelligence (AI) is transforming global manufacturing, offering unprecedented opportunities for productivity, quality, agility, and sustainability. For continuous improvement (CI) practitioners across South Africa and Africa, particularly those aligned with the Lean Institute Africa’s mission of operational excellence, this is a pivotal moment to lead. AI is not just a trend but a cornerstone of Industry 4.0, working alongside Internet of Things (IoT), cloud computing, and digital twins to create smart factories. However, African manufacturing risks lag if we do not decisively integrate AI into our CI frameworks.

This article explores AI’s transformative potential, highlights African innovations, addresses challenges and ethical considerations, and proposes a roadmap for CI practitioners to drive AI-enhanced operational excellence across the continent. 

The Transformative Power of AI in Manufacturing

AI optimises production schedules, predicts equipment failures, reduces waste, and enhances energy efficiency, aligning with CI methodologies like Lean, Six Sigma, Total Productive Maintenance (TPM), and 20 Keys. African manufacturers are adopting these capabilities:

• South Africa: Bell Equipment leverages machine learning for predictive maintenance, analysing sensor data to reduce downtime by approximately 15% (Bell Equipment, 2023). Sappi employs AI-driven process control systems in pulp production, improving energy efficiency by 10% (Sappi, 2023).
• Kenya: Twiga Foods uses an AI-driven logistics platform to streamline supply chains from smallholder farms to urban retailers, reducing spoilage by 20% and increasing margins (Twiga Foods, 2023).
• Egypt: Giza Systems deploys AI-based automation to monitor production KPIs in real time, improving throughput by 12% and supporting visual management principles (Giza Systems, 2023).
• Nigeria: Kobo360’s AI-powered freight platform optimises truck allocation, reducing empty miles by 25% and cutting logistics costs (Kobo360, 2023). Dangote Cement uses AI vision systems for quality control in cement production, reducing defects by 20% (Manufacturing Africa, 2024).
• Ghana: A leading textile manufacturer in Accra uses AI-driven inventory management to optimise stock levels, reducing excess inventory by 18% and aligning with waste reduction (Manufacturing Africa, 2024).

These examples align with CI goals of waste elimination, quality improvement, and flow optimisation. Industry benchmarks suggest AI can boost Overall Equipment Effectiveness (OEE) by 5–19% and reduce downtime by up to 20%, offering African manufacturers a competitive edge (West & Allen, 2018). For instance, in South Africa’s automotive sector, AI-powered digital twins simulate production lines, reducing setup times by up to 30%, further supporting Industry 4.0 integration. 

Lessons from Bold African Innovators

Beyond major economies, smaller African nations showcase AI’s potential for CI:

• Rwanda: HiQ Africa’s Zata ERP platform, launched in 2022, uses AI for inventory management and automated tax compliance, reducing stockouts by 30% (HiQ Africa, 2024).
• Tunisia: InstaDeep partners with Syngenta and Oxford University to develop AI decision-making tools that can be scalable to manufacturing (InstaDeep, 2024).
• Senegal: NeoFarm’s AI converts organic waste into farming inputs, supporting circular economy models and cutting disposal costs by 15% (NeoFarm, 2024).
• Togo: The Novissi program used AI and satellite data for cash transfers during COVID-19, demonstrating the reach of low-cost AI (Gulf Times, 2021).

These initiatives reflect CI principles like continuous learning, data-driven decisions, and waste reduction, applicable to manufacturing.

 Navigating Challenges to AI Adoption

AI adoption faces hurdles in Africa, which is critical for CI practitioners:

• Infrastructure Limitations: Unreliable internet and power hinder AI deployment, particularly in rural areas.
• High Costs: Initial investments in AI systems can be prohibitive for SMEs.
• Skills Shortages: A lack of data scientists and AI-literate workers slows implementation.
• Data Quality: Inconsistent or incomplete data undermines AI model accuracy.

Solutions include cloud-based platforms like Microsoft Azure for affordability, partnerships with universities like the University of Pretoria for training, and open-source tools like TensorFlow for SMEs, enabling scalable adoption aligned with CI cost-efficiency goals.

 Ethical and Social Considerations

AI must be deployed ethically to avoid biases (e.g., in quality control) or job displacement, particularly in labor-intensive African economies. InstaDeep’s fairness-focused algorithms ensure equitable outcomes, such as unbiased supplier evaluations (InstaDeep, 2024). AI can empower workers by automating repetitive tasks and providing training platforms that align with CI’s respect for people principle. Practitioners must advocate for ethical AI frameworks prioritising fairness, transparency, and inclusivity.

 Why This Matters for CI Practitioners?

AI enhances CI methodologies—Lean, Six Sigma, TPM, and 20 Keys—by accelerating data-driven decision-making. For example methodologies which emphasiSes practices like zero defects and continuous improvement, benefits from AI’s precision, as do Lean’s waste elimination, Six Sigma’s DMAIC process, and TPM’s maintenance strategies. Imagine:

• Kaizen Events: AI-generated insights identify inefficiencies faster.
• Root Cause Analysis: Machine learning diagnostics pinpoint issues with precision, supporting Six Sigma and operational excellence systems.
• PDCA Cycles: Real-time IoT sensor feedback shortens cycles
• Predictive Maintenance: AI reduces downtime, supporting TPM and best practice maintenance.

CI practitioners must become “translators” of AI’s potential, integrating it into Operations Excellence frameworks. AI can improve OEE by 5–19% and reduce energy consumption by 10–15%, making it a game-changer for African factories (West & Allen, 2018).

 A Call to Action: Towards AI-Driven CI Excellence

CI practitioners must embed AI into their frameworks:

1. Upskill Teams: Train in data literacy and AI tools through programs like Microsoft’s AI for Africa initiative.
2. Pilot AI Projects: Align AI with improvement efforts like defect detection or predictive maintenance.
3. Collaborate Locally: Partner with AI startups (e.g., HiQ Africa, InstaDeep) and universities (e.g., Stellenbosch University) to develop solutions.
4. Engage Stakeholders: Work with governments (e.g., South Africa’s Department of Trade, Industry, and Competition) and AUDA-NEPAD to create AI policies and fund innovation hubs.
5. Share Knowledge: Build a pan-African CI community, from Durban to Dakar, Cairo to Kigali, to exchange best practices.

 The Role of Stakeholders

Stakeholders are vital for CI-driven AI adoption:

• Governments: South Africa’s DTIC and Nigeria’s Ministry of Industry can develop AI policies and subsidise SMEs.
• International Bodies: AUDA-NEPAD’s African AI Strategy provides funding and frameworks for innovation hubs (AUDA-NEPAD, 2024).
• Universities: The African AI Research Network fosters skills pipelines and research tailored to manufacturing.
• Private Sector: Multinational corporations like Microsoft and Google offer cloud-based AI tools and training programs.

This ecosystem ensures inclusive, sustainable AI adoption aligned with CI principles.

AI and the Future of Manufacturing in Africa – A CI Practitioner’s Roadmap

A 12-part article series for CI practitioners:

1. From Kaizen to Code: How AI reinvents operational excellence with diagnostics, featuring Twiga Foods’ logistics optimisation.
2. The Data-Driven Practitioner: Building AI and data literacy for CI teams, with training pathways from Stellenbosch University.
3. Industry 4.0: Aligning AI with Lean, Six Sigma, TPM, and other systems like 20 Keys, including automated data collection for DMAIC.
4. Case Studies from the Continent: Real-life AI applications in Bell Equipment, Kobo360, and Dangote Cement in Nigeria, plus Ghana’s textile industry.
5. AI on the Factory Floor: Predictive maintenance (TPM) and smart scheduling (Lean), referencing Sappi and Ghanaian SMEs.
6. Waste Not: Using AI to eliminate Lean’s 8 wastes as visual systems for quality control.
7. Low-Cost, High-Impact: Democratising AI for SMEs using open-source platforms like TensorFlow and cloud-based tools.
8. Beyond the Plant: Improving supplier quality and last-mile logistics with Kobo360 and Twiga Foods.
9. AI Meets Human-Centered Design: Empowering workers with AI tools for decision-making and skill development.
10. Cross-Industry Lessons: Applying AI insights from Rwanda’s agriculture and Togo’s Novissi program to manufacturing CI.
11. Building Africa’s CI-AI Ecosystem: Collaborations with the African AI Research Network, universities, and Microsoft’s AI for Africa program.
12. Vision 2030: Envisioning a smart, sustainable African factory driven by CI principles and AI.

 Conclusion

AI empowers CI practitioners to transform African manufacturing, enhancing frameworks like Lean, Six Sigma, TPM, and 20 Keys. By addressing challenges and ethical concerns, practitioners can position Africa as a global leader in operational excellence. Through upskilling, collaboration, and stakeholder engagement, we can build a pan-African CI community that not only keeps pace with global change but defines the future of manufacturing.

About the Author: Dr. Ntokozo Mthembu is a technology and industrial innovation specialist and director of ElamiRonto and Sian Consulting. ElamiRonto is an associate of ODI (SA) and collaborates with ODI on social investment initiatives, particularly youth and women. He advocates integrating AI with continuous improvement frameworks to create adaptive, resilient, and inclusive factories.

 References

• AUDA-NEPAD. (2024, July 3). AI and the Future of Work in Africa: How AI is Redefining Opportunities. https://www.nepad.org/publication/ai-and-future-work-africa-how-ai-redefining-opportunities
• Bell Equipment. (2023). Leveraging AI for Predictive Maintenance in Heavy Machinery. https://www.bellequipment.com/news (Note: Verify with Bell Equipment’s press releases).
• Giza Systems. (2023). AI-Driven Industrial Automation for Manufacturing KPIs. https://gizasystems.com/solutions/industrial-automation/
• Gulf Times. (2021, March 14). Togo’s Novissi Program Uses AI and Satellite Data for Cash Transfers. https://www.gulf-times.com/story/693318
• HiQ Africa. (2024). Zata ERP: AI-Powered Solutions for Retail Efficiency. https://hiq.africa/
• InstaDeep. (2024). AI for Decision-Making: Collaborations with Syngenta and Oxford. https://www.instadeep.com/case-studies/
• Kobo360. (2023). How Kobo360 is Transforming African Logistics with AI. https://african.business/2023/05/technology/how-kobo360-is-transforming-african-logistics (Note: Verify with Kobo360’s official reports).
• Lean Institute Africa. (2024). Integrating AI with Continuous Improvement in African Manufacturing. https://leaninstituteafrica.org/
• Manufacturing Africa. (2024). AI Case Studies in African Manufacturing. https://manufacturingafrica.com/insights (Note: Verify with specific case studies for Dangote Cement and Ghanaian textile manufacturer).
• NeoFarm. (2024). AI for Circular Economy: Converting Organic Waste in Senegal. https://www.neofarm.sn/ (Note: Verify with NeoFarm’s official documentation).
• Sappi. (2023). AI-Powered Process Optimization in Pulp Production. https://www.sappi.com/innovation (Note: Verify with Sappi’s sustainability reports).
• Twiga Foods. (2023, March 14). AI-Driven Logistics Platform for Supply Chain Optimization. Google Cloud Blog. https://cloud.google.com/blog/topics/partners/twiga-foods-uses-tech-to-reduce-food-insecurity/
• West, D., & Allen, J. (2018, April 24). How Artificial Intelligence is Transforming the World. Brookings Institution. https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/

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As industries adapt to rapid technological changes, manufacturing evolves into a smarter, more efficient, and environmentally sustainable field. “Powering Industries of the Future in Manufacturing” covers the transformative technologies and strategic advancements guiding the sector into an era of innovation. Below, we explore each area of focus, highlighting objectives, outcomes, examples, real-world case studies, and how it can further drive improvements in South African manufacturing.

1. Digital Transformation and Industry 4.0[1]

Objectives: The integration of smart systems and digital technologies enhances operational efficiency, reduces costs, and improves responsiveness to market dynamics.

Outcomes:

• Real-time data analytics support better decision-making.
• Processes are optimised, minimising human intervention and error rates.
• Enhanced visibility and performance across supply chains.

Learning Point: Implementing digital tools and smart systems is integral to continuous improvement. South African companies should develop Key Performance Indicators (KPIs) that leverage real-time data to monitor operational efficiency, aligning performance metrics with company goals for competitiveness.

Example: Automotive and electronics industries deploy IoT sensors to monitor equipment health, while Industrial IoT (IIoT) platforms enable predictive maintenance and energy optimisation.

Case Study: Siemens Amberg Electronics Plant (Germany)

At its Amberg facility, Siemens has created a fully digitalized production system. Equipped with smart sensors and automation, this plant achieves 99% product quality and operates with high energy efficiency[2].

2. Additive Manufacturing (3D Printing)[3]

Objectives: Additive manufacturing enables on-demand production with minimal waste and is particularly valuable for creating complex, customised components.

Outcomes:

• Reduces material waste and energy use.
• Allows for rapid prototyping, accelerating time-to-market.
• Supports localised production, reducing supply chain complexity

Learning Point: A focus on waste reduction and efficiency improvement aligns with additive manufacturing’s strengths. By adopting 3D printing, companies can eliminate waste, improve flexibility in production, and lower costs, all while enhancing competitiveness in the market.

Example: Industries such as aerospace and automotive use 3D printing for on-demand spare parts, while healthcare leverages it to produce customized prosthetics.

Case Study: GE Aviation (USA)
General Electric uses 3D printing to manufacture fuel nozzles for jet engines. The method significantly reduces weight, enhances durability, and shortens production time[4].

3. Sustainable Manufacturing and Renewable Energy[5]

Objectives: Reducing environmental impacts and integrating renewable energy sources into production facilities is key to sustainable manufacturing.

Outcomes:

• Lowered carbon emissions and energy consumption.
• Less reliance on non-renewable resources.
• Improved environmental compliance and corporate sustainability profiles.

Learning Point: By adopting sustainable manufacturing practices and standardising waste reduction techniques, companies will ensure that resource utilisation is optimised. This, in turn, can reduce costs and enhance a company’s competitiveness through eco-friendly practices.

Example: Solar-powered manufacturing facilities and waste-to-energy systems provide sustainable energy solutions in various sectors.

Case Study: Tesla Gigafactory (USA)
Tesla’s Gigafactory operates with a significant renewable energy input, including one of the world’s largest solar roof installations, to reduce its environmental footprint[6].

4. Advanced Automation and Robotics[7]

Objectives: Automation enhances manufacturing efficiency, accuracy, and safety, reducing reliance on manual labour.

Outcomes:

• Higher throughput with reduced labour costs.
• Enhanced precision in quality control.
• Safer working environments with fewer injuries.

Learning Point: Automation, combined with continuous improvement principles, can help South African companies reduce costs and improve precision. A continuous improvement approach emphasises the standardisation of processes, which can be further enhanced by robotics to ensure consistency and high-quality outputs across production lines.

Example: Collaborative robots (cobots) assist in repetitive tasks, while autonomous mobile robots handle logistics in warehouses.

Case Study: KUKA Robotics (Germany)
KUKA’s robots are integral to automotive assembly lines, where they perform tasks like welding and assembly with high adaptability, often in collaboration with human workers[8].

5. Artificial Intelligence and Machine Learning[9]

Objectives: AI optimizes manufacturing processes through predictive maintenance, quality control, and improved decision-making.

Outcomes:

• Reduced downtime due to predictive maintenance.
• Optimized production schedules and better resource utilization.
• Enhanced product quality with AI-driven quality controls.

Learning Point: AI technologies can empower managers and supervisors to make data-driven decisions in real-time. Integrating AI with a focus on continuous monitoring and feedback loops ensures that manufacturing operations are always aligned with business goals.

Example: AI-powered predictive maintenance predicts machine failures before they occur, minimizing unplanned downtimes.

Case Study: BMW Group (Germany)
BMW leverages AI to predict maintenance needs in production equipment, minimizing unexpected downtimes and ensuring continuous production[10].

6. Flexible and Resilient Supply Chains[11]

Objectives: Building adaptable supply chains reduces vulnerability to disruptions and allows for faster response to market shifts.

Outcomes:

• Increased resilience to global disruptions.
• Optimized inventory management, reducing excess stock.
• Swift adaptation to changes in demand.

Learning Point: Continuous improvement programs can help South African companies streamline their supply chains by identifying and eliminating inefficiencies. This can be achieved through enhanced visibility and better communication across the supply chain, resulting in reduced lead times and increased agility.

Example: Blockchain provides transparent tracking of goods from suppliers to manufacturers, enhancing traceability.

Case Study: Nike (Global)
Nike’s digital supply chain enables real-time tracking of inventory and shipments, ensuring quick and accurate delivery to customers[12].

7. Workforce Transformation and Skill Development[13]

Objectives: Preparing workers with digital, technical, and collaborative skills is essential for success in Industry 4.0.

Outcomes:

• Higher employee satisfaction and engagement.
• Improved safety through skill development.
• Better operational performance with a skilled, adaptable workforce.

Learning Point: Employee engagement and skill development are essential for success with Industry 4.0. By training supervisors and staff South African companies create a workforce that is capable of adapting to new technologies while fostering a culture of continuous improvement.

Example: Online training programs in AI and robotics help employees upskill and transition to high-tech environments.

Case Study: Amazon (Global)
Amazon trains warehouse staff to work alongside robots, equipping them with skills to operate in an advanced digital workplace[14].

8. Emerging Technologies: Quantum Computing[15] and Blockchain[16]

Objectives: Quantum computing accelerates innovation, while blockchain ensures transparency and security across manufacturing and supply chains.

Outcomes:

• Increased computational power to simulate complex processes.
• Improved lifecycle management and secure transactions.

Learning Point: Integrating quantum computing and blockchain into the continuous improvement process can help South African companies drive innovation and ensure secure, transparent transactions.

Example: Quantum computing simulates material properties, expediting product development, while blockchain verifies product origin in high-value sectors.

Case Study: Volkswagen Group (Germany)
Volkswagen uses quantum computing to optimize logistics, reducing assembly line delays and enhancing production efficiency[17].

9. Decentralised and Autonomous Manufacturing[18]

Objectives: Autonomous systems and decentralized networks allow for flexible, localized production with minimal human intervention.

Outcomes:

• Greater agility and responsiveness in manufacturing.
• Reduced environmental impact through local production.
• Lower dependence on centralized facilities.

Learning Point: Decentralisation can be supported by continuous improvement strategies that encourage flexibility and adaptability in production. Companies implementing decentralised networks and leveraging automation will reduce costs and improve efficiency.

Example: Distributed manufacturing networks use local 3D printing hubs to reduce transportation needs.

Case Study: MakerBot (USA)
MakerBot’s 3D printing technology enables local, on-demand production, allowing customers to print products close to their point of use[19].

10. Data Security and Cyber-Resilience[20]

Objectives: Data security is vital for protecting manufacturing systems from cyber threats and ensuring continuous production.

Outcomes:

• Reduced cyber-attack risks.
• Strengthened trust from stakeholders.
• Minimized downtime through resilience and recovery protocols.

Learning Point: With the increasing integration of digital technologies, companies must also focus on securing data. The approach to continuous improvement should include enhancing cyber-resilience and implementing robust security protocols across manufacturing processes.

Example: AI-powered cybersecurity systems detect and respond to threats in real time, while blockchain secures sensitive data.

Case Study: Honeywell (Global)
Honeywell employs advanced cybersecurity in its systems to protect customer data and ensure operational continuity[21].

Through these advancements, manufacturers are not only becoming more profitable but also reducing environmental impact, increasing resilience, and creating opportunities for skilled labour. The adoption of these innovations promises a future of manufacturing that is smarter, greener, and more responsive to global demands. It also empowers South African companies to achieve higher efficiency, competitiveness, and sustainability in a rapidly changing global market.

[1]           https://www.mckinsey.com/capabilities/operations/our-insights/capturing-the-true-value-of-industry-four-point-zero

[2] https://www.forbes.com/sites/insights-teradata/2019/07/08/revolution-on-the-siemens-factory-floor/

[3]           https://www.raise3d.com/blog/3d-printing-advantages/

[4]           https://d3.harvard.edu/platform-rctom/submission/taking-to-the-skies-with-3d-printed-jet-engines-ge-aviation-already-is/

[5]           https://nap.nationalacademies.org/read/24876/chapter/1

[6]           https:// www.researchgate.net/publication/366057251

[7]           https://www.mckinsey.com/capabilities/operations/our-insights/automation-robotics-and-the-factory-of-the-future

[8]           https://www.kuka.com/en-de/industries/solutions-database/2016/07/solution-robotics-halle-7

[9]           https://www.itransition.com/machine-learning/manufacturing

[10]          https://www.linkedin.com/pulse/case-study-bmws-ai-powered-predictive-maintenance-predcoai-jixif/

[11]          https://www.mckinsey.com/capabilities/operations/our-insights/supply-chains-to-build-resilience-manage-proactively

[12]          https://www.cfobrew.com/stories/2022-07-26-nike-digital-supply-chain

[13]          https://pmc.ncbi.nlm.nih.gov/articles/PMC9278314/

[14]          https://www.vox.com/recode/2019/12/11/20982652/robots-amazon-warehouse-jobs-automation

[15]          https://link.springer.com/chapter/10.1007/978-981-97-5810-4_27

[16]          https://www.oracle.com/blockchain/what-is-blockchain/blockchain-for-supply-chain/

[17]          https://www.dwavesys.com/media/bbximewp/dwave_vw_case_study_v8.pdf

[18]          https://research.polyu.edu.hk/en/publications/towards-resilience-in-industry-50-a-decentralized-autonomous-manu

[19]          https://www.makerbot.com/stories/case-study-3d-printing-in-a-pandemic-thinking-outside-the-box/

[20]          https://www.kroll.com/en/insights/publications/cyber/state-cyber-defense-manufacturing

[21]          https://automation.honeywell.com/us/en/software/smart-energy/cybersecurity

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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/

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The big question is, ‘is there a future for employees in Industry 4.0, or will all the work be taken over by man-made autonomous equipment driven by the ubiquitous artificial intelligence?’ Perhaps put in another way this question is, ‘has our creative abilities to make things easy, faster and cheaper, ran ahead of us to create our own extinction and hand over our power to AI self-organising machines?’

The painful truth though is that, since the foundations of the Earth, revolutions of all types have brought with them the proverbial collateral damage. Specifically for industrial revolutions, employees, customers, products, processes and systems have been successful candidates of the cancer of collateral damage, but more so are the employees of companies who are on the coalface of change and its disruptive outcomes. Levin, Cunningham (2018)[1] describe the impact of industrial revolutions succinctly as “periods in modern human history where technological innovation resulted in a drastic shift in the socio-economic status of people”. The employees and attendant training programs are always the first casualties of change. When the disruptive changes occur, the efforts to regain the jettisoned human capital are usually not very successful as those skills migrate to other stable environments, and the cost of familiarisation and new skill absorption is usually costly compared to retaining and reskilling existing skills a la the Kawada Robotics Corp method. [2]. Therefore, we can wallow on the painful or dark side of the inevitable. We need to rise above it and do what our forebears have always done: apply our thinking skills and come up with an alternative, as the famous South African affirmation on innovation boldly declares: ‘n boer maak ‘n plan’.

According to Kelly[3](p. 8) on a US strategic engineering supplier, the impact of automation on the US manufacturing workface “doesn’t mean the end of American manufacturing jobs, but it does mean that the nature of the work is will change dramatically. It will affect the manufacturing workforce in the following five ways:

1. Certain jobs will be eliminated
2. Current jobs will be modified
3. New jobs will be created
4. There will be a skill gap between eliminated jobs and modified jobs or new role
5. The manufacturing workforce will keep evolving.”

This is good news coming from the world’s largest economy, ranked second out of 141 countries on the Global Competitiveness Index 4.0 2019 Rankings[4],that measures national competitiveness – defined as the set of institutions, policies and factors that determine the level of productivity[5].

Echoing similar positive sentiments, Professor Tshilidzi Marwala[6], Vice Chancellor and Principal of the University of Johannesburg, quotes the World Economic Forum of 2018 that predict that the Fourth Industrial Revolution will “create massive job losses, but simultaneously pave way for new occupations, especially in areas such as data analysis, computer science and engineering.” The metrics are that by 2022 more than 75 million jobs will have disappeared and been replaced by 133 million new types of jobs. These predictions are encouraging, reinforcing the ingenuity of mankind to always rise to the challenges that upset the balance of its precarious socio-politico-economic stability, triggered by multivariate upheavals from Mother Nature’s copious warehouse of surprises. This phenomenon throws us a gauntlet: are we (As ODI-iers and other service providers out there, and as companies) ready, are we preparing, planning, strategising to meet these challenges? What will our home-grown contribution be to the projected global figure of new jobs?

In reframing Kelly’s predictions and meaning on some of the 4IR new job prospects, what are the benefits of investing in automation? This question reminds me of my early days in manufacturing when we were introducing world-class manufacturing. This was the unions’ famous question to put the spanner in the works, so to speak: “wat is in dit vir ons?” meaning what is in it for us as workers? This is another story for another time. Dewitt and company state three important factors, namely:

1. Automation will reduce production costs and make US companies more competitive in the global market
2. Automation increases productivity and improves quality
3. Manufacturers will re-invest in innovation and R&D.

This is not a Pan-American panacea, but these strategies are applicable across the board, and internationally.

The scholars of the 4IR give us hope that the human species that started it all through their creative efforts will always be saved, as though each revolution provides safety valve for some of the remnants to survive and serve as a nucleic cells upon which to build a new phalanx of the working force. Table 1.0 below gives a snapshot of expected roles for employees in the 4IR workplace based on the researchers in the field.

Table 1.0         Role of employees in the new industry 4.0 order

According to the Career Junction[13] blog, the World Economic Forum report has listed the 10 skills you will need for the Fourth Industrial Revolution:

1. Complex Problem Solving
2. Critical Thinking
3. Creativity
4. People Management
5. Coordinating with Others
6. Emotional Intelligence
7. Judgement and Decision Making
8. Service Orientation
9. Negotiation
10. Cognitive Flexibility

The blog further states that physical and repetitive jobs in industries such a mining, agriculture, retail & trade, transportation, agriculture, administration, office & support, accommodation, food services and production & manufacturing industries will bear the brunt of change ushered by the 4^th Industrial Revolution. The good news however, is that when gazing ahead, this will present enormous opportunities for our children and youth, who will grow in a transformed education, skills and training regime driven by artificial intelligence and automation augmented by critical thinking skills and problem solving. Looking at the required skills for the future, manual work will be less required, although I believe not eliminated, but knowledge driven skills is the future.

This is an ideal state because to achieve these things governments have to provide enabling policies and environments accompanied by attendant investments, focusing less on defence spending and more on fighting endemic crime in all its ramifications that siphons off most of countries’ resources, especially in the developing world. Having touched on government, what exactly is its role in helping companies navigate the new world of 4IR. Levin, Cunningham and Nyakabawo (2018:11)[14], authors of the dti TIPS Research report on WEF and the 4^th Industrial Revolution in South Africa, identify seven types of government-led national efforts to adopt and diffuse new production technologies, which need to be customised based on country-specific nuances and a nation’s industrial sector mix:

1. Building awareness
2. Establishing financial incentives
3. Creating a robust legal framework
4. Spurring accreditation of 4IR centric companies
5. Expanding connectivity and data-security protection
6. Promoting R&D&I for Fourth Industrial Revolution technologies
7. Setting up new talent and education programmes.

The challenge is to adapt to changes and create a comfortable, affordable and sustainable future for us and the generations after us. According to DeWitt et al[15] (p. 11), on ‘Building the manufacturing workforce of the future starts now’, it is important to keep this in mind, that ”to remain competitive in the global market, we must establish a high-innovation, high-wage economy where technological advancements don’t simply replace current jobs, but instead create new ones – and perhaps even new industries.”

My wife Debbie is an avid reader of Twitter, especially motivational affirmations by pastors, celebrities and speakers of the trade. This very week she shared with me a motivational talk by the incomparable American TV show host, Steve Harvey. Steve Harvey quoted Albert Einstein quotation that he found when reading a book one day: “Imagination is everything. It is preview to life’s coming attractions”. Therefore, in pursuant of Albert Einstein’s world of ‘imagination’, let us don our own Einsteinian hats to ‘imagine and create’ the uncompromising and unadulterated role of our employees in the 4IR world that is upon us.

Author: Dr. Ntokozo Mthembu – Advisor to the ODI Board

Click here to read more about the 20 Keys synergy with the 4th Industrial Revolution Tsunami.

Sources:

[1] World Economic Forum and the 4^th Industrial Revolution in South Africa. TIPS – Trade and Industry Policy Strategies. TIPS Industrial Report for the Department of Trade and Industry. November 2018. (Date of Usage: 09 December 2019)

[2] http://odi.co.za/balancing-job-losses-amidst-innovation-the-kawada-industries-innovative-thinking/ posted 9^th April 2019.

[3] Dewitt L and McDonald D. The impact of automation on the U.S. manufacturing workforce, in Five Ways automation will change the manufacturing workforce. KELLY What’s next. 1-12. https://www.kellyocg.com/Insights/Whitepapers/983/five-ways-automation-will-change-the-manufacturing-workforce. (Date of Usage: 29 August 2019)

[4] http://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2019.pdf (Date of Usage: 4 September 2019)

[5] South Africa is ranked #60. Singapore is ranked #1. (Date of Usage: 29 August 2019)

[6] Marwala, T. “Mathematics, beauty, poetry and the Fourth Industrial Revolution”. Opionista. The Daily Maverick. 7 February 2020. www.dailymaverick.co.za  (Date of  Usage: 28 March, 2020)

[7] Schumacher A, Erol S and Sihn W.  A maturity model for assessing Industry 4 readiness and maturity of manufacturing enterprises. Elsevier ScienceDirect Procedia CIRP 52 (2016) 161-166

[8] Stock T and Seliger G. Opportunities of Sustainable Manufacturing in Industry 4.0. Elsevier ScienceDirect Procedia CIRP 40 (2016) 536-541

[9] Schumacher A, Nemeth T and Sihn W. Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises. Elsevier ScienceDirect Procedia CIRP 79 (2019) 409-414

[10]Ibarra D, Ganzarain J and Igartua I. Business model Innovation through Indutry 4.o: A review. Elsevier ScienceDirect Procedia CIRP 22 (2018) 4-10

[11] Geissbauer R, Vedeo J and Schrauf S. Industry 4.0: Building the digital enterprise. PwC 2016 Global Industry 4.0 Survey. www.pwc.com/industry4.0

[12] Pessl E, Sorko S and Mayer B. Roadmap Industry 4.0 – Implementation Guideline for Enterprises. International Journal of Science, Technology and Society. Science Publishing Group. 2017 5(6) 193-202

[13] Jobs of the Future – Surviving the Fourth Industrial Revolution. CarrerJUnction.co.za. March 05, 2019

[14] World Economic Forum and the 4^th Industrial Revolution in South Africa. TIPS – Trade and Industry Policy Strategies. TIPS Industrial Report for the Department of Trade and Industry. November 2018. (Date of Usage: 23 November 2019)

[15] Dewitt L and McDonald D. The impact of automation on the U.S. manufacturing workforce, in Five Ways automation will change the manufacturing workforce. KELLY What’s next. 1-12. https://www.kellyocg.com/Insights/Whitepapers/983/five-ways-automation-will-change-the-manufacturing-workforce (Date of Usage: 07 December 2019)

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The Fourth Industrial Revolution like its predecessors before

• 1IR – Water and Steam Power Engine (1784);
• 2IR – Mass Production using Electrical Energy (1870);
• 3IR – Use of PLC and IT systems for Automation (1970)] (1),

shall leave no industry untouched or affected by it. In South Africa in particular, the Financial Services, especially the Banking sector are leading the way, and are undoubtedly the champions of digitalisation.

The good news about the implementation of the 9 pillars that form the foundation for Industry 4.0 is:

1. Most of them are already in use manufacturing (2), and in other industries.
2. By a way of natural progression, we all owe it to the 3^rd Industrial Revolution which has bequeathed to us the electronic revolution characterised by the use of PLSs and IT systems for Automation.

I remember when I was a still a rookie process engineer many moons ago, programmable logical controllers (PLCs), statistical process control (SPC), supervisory control and data acquisition (SCADA), flexible manufacturing systems (3) (FMS), computer integrated manufacturing³ (CIM) and computer numerical control (CNC) machines were the holy grails of manufacturing, and some of them still are. They were efficient systems, but were disparate and not integrated. One of the key fundamentals of 4IR is integration of enterprise assets on the bedrock of digital platforms.

Let’s look at what we currently have (manufacturing assets) in our enterprises (from the 3IR) to what we are expected to have in the 4IR world against the 9 pillars (4) as a base as shown in Figure 1 below.

Figure 1: The 9 Pillars of 4IR unpacked (Gerbert et al. 2015)

Re-positioning

I guess the question of re-positioning ourselves as manufacturers to take advantage of the new technologies offered by the 4IR should be the next logical step. European companies assisted by universities and research institutes have already begun on this journey as far back as 2011. But the results of their research have begun to emerge in the last 3 years or so, led by Germany. The terms ‘maturity, readiness and maturity models’ (7) are pan-European constructs commonly used to describe the journey towards full 4IR compliance where, for example, a maturity of 1 means, means the company is at the as-is state, largely defined by the 3IR sophistication. Schumacher et al define the term ‘maturity’ as referring to a ‘a state of being complete, perfect, or ready’ and implies some progress in the development of a system’ (8).  Using the 9 pillars explained above, companies can conduct a self-assessment to determine what their maturity levels are. This would save them a lot of money and effort before calling 4IR pundits and specialists to help with transformation into 4IR. A full rendition of maturity and readiness models could be reserved for another day.

A glimpse of the re-positioning approach, or what I would like to term as ‘the road map’, is provided by PwC’s 2016 Global Industry Survey (9), in what they call the “blueprint for digital success”. PwC has concretize the following 6 steps towards realizing the digitalization of enterprises on its march towards Industry 4.0:

1. Map out your Industry 4.0 strategy
2. Create initial pilot projects
3. Define the capabilities you need
4. Become a virtuoso in data analytics
5. Transform into a digital enterprise
6. Actively plan an ecosystem approach

Perhaps I must qualify the PwC blueprint by quoting its introduction:

“To move forward with Industry 4.0, digital capabilities are all-important. These take time and concentration; step-by-step approach is important. But move with deliberate speed, so that you don’t lose the first-mover advantage to competitors.”

Conclusion

I have just put a spotlight on the road ahead for manufacturers to move towards the 4IR compliance in order to be highly competitive in the e-driven market and e-commerce. If readers are able to understand that we are not facing the mighty Red Sea in front of us to cross, but the humbly River Jordan, then we are okay. But Alas! The River Jordan is also known for its famous tranquility that beguiles its dangerous under-currents. Then my friends and colleagues, I will have succeeded in my self-imposed duty of sounding this clarion call of ‘4IR readiness and maturity’ to cross the proverbial Rubicon, a ‘la Alfred Lord Tennyson’s The Charge of the Light Brigade (10).

Author: By Dr Ntokozo Mthembu, Pr. Eng., MSc, PhD, MASME, Independent Consultant and 4IR Researcher

Resources:

(1) Vaidya S, Ambad P and Bhosle S, Industry 4.0 – A Glimpse. 2^nd International Conference on Materials Manufacturing and Design Engineering. Procedia Manufacturing 20 (2018) 233-238.

(2) Gerbert P, Lorenz M, Russmann M, Waldner M et al, Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Boston Group 2019 (April 09, 2015) 1-16

(3) Basl J and Doucek P. A Metamodel for Evaluating Enterprise Readiness in the Context of Industry 4.0 MDPI Information 2019, 10, 89; doi:10.3390/info10030089. 13pp

(4) Gerbert P et al. The Future of Productivity and Growth in Manufacturing Industries.

(5) Aerosud/Paramount in Centurion RSA “thanks to its potential for design complexity and the fact that it can make more lightweight parts, which is obviously important when you’re high above the ground” from https://3dprint.com/203210/paramount-aircraft-3d-print-parts/ (Date of Issue: February 09, 2018)

(6) (a) A helicopter stuck in remote place in Africa need to deliver food. The next mechanic is some 17 flight hours away and need helicopter back in air within 2 hours. With the help of augmented reality glass on the pilot’s head connected to central computer that would know every details about the chopper. The repair action is performed with the help of augmented reality.

(b) Another example is that of telemedicine in South Africa (South African Telemedical Resources (TMR)) which links academic hospitals (Groote Schuur / Tygerburg ) with clinics and understaffed hospitals in remote provinces  to provide radiology and pathology expertise

(7) Schumacher A, Nemeth T and Sihn W. Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises. 12^th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2018, Gulf of Naples, Italy. Procedia CIRP 79 (2019) 409-414.

(8) Schumacher A, Erol S and Sihn W. A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 52 (2016) 161-166.

(9) Geissbauer R, Vedso J and Schrauf S. PwC, 2015 Global Digital IQ Survey, September 2015. www.pwc.com/industry40

(10) My Form 1 English teacher’s (Mr Nqgonqgoza Ntuli) favourite poem – recently invoked by Advocate of the High Court of South Africa, UP’s Prof Christo Botha’s Preface to Statutory Interpretation. An Introduction to Students. Fifth Edition – one of my wife’s (Debbie) voluminous collections.

To read more about ODI’s Continuous Operations Improvement System, click here.

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Chandrajit P Ahire & Anand S Relkar, KK Wagh Institute of Engineering Education and Research, Nashik, India

The saying ‘if you can’t measure it, you can’t manage it’ tantamount to a cardinal proverb in management science. The 20 Keys framework provides a structured process approach in measuring the progress of keys implementation from level 1 to level five, providing tangible practical steps to be followed from one level to the next. There are a couple of keys that lend themselves to more process measurements, but require technical measurement intervention to improve their effectiveness, such as key 9: Maintaining equipment. Kobayashi states the core function of key 9 is to prevent breakdowns and eliminate three evils of machine breakdowns (2) which are:

1. contamination,
2. inadequate lubrication, and
3. misoperation (disoperation).

The 20 Keys System does not prescribe any methodology on the measurement of machine effectiveness beyond the level-based process steps towards perfection at level 5.

There are many methods that have been developed to calculate the machine effectiveness based on the elimination of losses that impact on its effectiveness such as

• Overall Equipment Effectiveness (OEE),
• Machine Effectiveness (ME),
• Equipment Effectiveness and Reliability Model (EPR), and
• Equipment Effectiveness.

All these methods or models mentioned above are based on the technical fundamentals developed by Seiichi Nakajima (3) to measure equipment and / or machine efficiency by looking at the product of machine availability, performance efficiency and quality rate. The product of these combined measurement leads to what Nakajima coined Overall Equipment Effectiveness (OEE).

This blog will focus on the OEE measurement in order to achieve a pointed approach to the Key 9 technical measurement of equipment and machines to augment its process based measurement as explained above.

In order to maximise machine and equipment effectiveness, Nakajima identified 6 big losses that must be managed in order to maximise overall equipment effectiveness. Table 1 below shows the classification of the six big losses and their origin.

 Table 1. The Six Big Losses and OEE Measurements (Nakajima 1988 14)

Calculation of the Overall Equipment Effectiveness (OEE) (4)

The calculation of the OEE is a clever manipulation of three technical parameters, namely machine or equipment availability during its operation, its performance efficiency and the quality rate of the products it produces as shown below in Table 2.

Table 2: World Class Performance Scorecard (Abdul, Kamaruddin & Abdul Azid, 2012) (5)

Measuring Availability

Availability or operating rate is based on a ratio of operation time, excluding downtime, to loading time. It is expressed mathematically as follows:

Measuring Performance Efficiency

The operating speed rate of equipment is based on the discrepancy between the ideal speed and its actual operating speed. It is expressed mathematically as follows:

Measuring Quality Rate

Therefore: Overall Equipment Effectiveness = Availability x Performance Efficiency x Rate of quality products

The ‘world class’ performance measurement on availability, performance efficiency and quality rate have been provided by researchers Samat H. Abdul et. al. from the School of Mechanical Engineering Universiti Sains Malaysia, Engineering Campus in Malaysia. This gives an OEE of 85%. It is worth noting that in the 20 Keys Implementation System, the quoted OEE ‘world class’ figures is equivalent to Level 3 which gives an OEE of 85%. In 20 Keys, the highest Level is 5 with an OEE of 95%.

The OEE case study – The Aussie Connection

Researchers (6) from Monash University in Australia and Lund University in Sweden collaborated to investigate the impact of implementing OEE amongst 18 Australian companies. Of the 18 firms, only 6 responded, and whose results were analysed. The summary of their findings is presented below.

(1) Drivers and motives

• Intra/inter firm benchmarking and removal of waste (and identification of losses)
• Be considered as part of a programme to change the organisations operational culture.

(2) Critical success factors

• operator involvement, education and understanding, plus visibility of data/target

(3) Common difficulties, barriers or pitfalls

• Resistive cultures
• Data entry and display was delayed
• Reduced motivation

(4) Critical success factors

• Simplicity of data capture, storage, display and benchmarking;
• Enabling role that management should play in the system (role must shift to the enablement of simplicity (perhaps through automation) to ensure the system’s continuity)

(5) Main benefits or specific outcomes         

        Primary

• Tangible aspects of performance metrics
  • improvements in CTQ [critical to quality] family
  • financial dimensions of throughput efficiency
  • waste removal

          Secondary

• Intangible domain
  • HR empowerment,
  • engagement and
  • morale

(6) Main challenges

• How to maintain the shop floor (HR) engagement and commitment to the system
• Integrating the increasingly demanding objectives of the business

In a nutshell, the researchers’ conclusion is that they found that “the implementation of OEE is typically based on the motivation to use a basic reference measure for analysing and comparing the utilization of resources at the plant. The use of OEE can also be transformed to a system for analysing production data to identify potential areas of improvement, and supporting lean initiatives”.

The implementation of the OEE is very difficult if it is implemented manually because the calculations are complex and cumbersome, and this can be compounded further if one is dealing with a large battery of machines and or workstations. This does not discourage companies from setting up an initial base even if it is for a few machines. Practitioners of TPM are beginning to use computerised systems to optimise the OEE calculations. The Romanian researchers (7) at Valahia University of Targoviste have presented two case studies of OEE computerisation systems. The first one is by a Turkish Doruk Automation Enterprise which implemented a computerized system that monitors the work on line and displays the values of OEE for each workstation in real time. The second one is a Romanian domestic appliances manufacturer ARCTIC Gaesti that implemented FICO XPRESS optimizer to calculate the OEE.

On the South African shores, there has been a number of companies implementing OEE. For example, Nampak Mono Containers in Maitland, now Huhtamaki South Africa based in Springs, used Excel spreadsheet to calculate OEEs for a battery of their thermoforming machines as part of their TPM world class manufacturing initiatives (experience by yours truly). Roland Rohrs, Chairman of ODI is one of the best authorities in OEE implementation. He is currently teaching OEE methodology to industrial engineering students at the University of Pretoria. Having had the privilege to attend some of his lectures as a guest student, he possesses a rich picture of OEE case studies that he uses to demonstrate his OEE art to his students.

The best way forward however, would be to invest in the 4th Industrial Revolution (4IR) technologies such as predictive analytics, real-time asset health monitoring, and asset tracking, amongst others. All these technologies link directly with Key 9: Maintaining machines and equipment.

As the doyen of PPORF (Practical Program Of Revolutions in Factories) Iwao Kobayashi puts it succinctly when he says: “When factory workers and managers use equipment without properly maintaining it (“we’re too busy for that,” they say), they eventually run into a bigger problem\ breakdown and line, stoppages’ (8). Who can argue against such vintage wisdom?

The 20 Keys System does not specify a particular methodology to augment its process based improvement techniques. However, it leaves a wide room for companies to adopt any method appropriate to their industries to effect and augment the process initiatives within the 20 Keys blueprint, where warranted. Some keys are adequate to achieve highest levels of improvement without an injection of technical measures such as Keys 1 and 3, while others require a drill-down to specifics. Key 9 is such an example. That said, OEE may not necessarily be the only method of enhancing Key 9, but is one of the simplest methods that encompasses machines performance within available operating time, speed and quality to achieve the 20 Keys mantra “better, faster and cheaper”

• Author: Dr Ntokozo Mthembu, Pr. Eng., PhD
• Affiliations | Mthembu-Heath Consulting Engineers; Innovation & Technology: ODI; ASME; ECSA

To read Dr Ntokozo Mthembu’s blog about ‘Balancing job losses amidst innovation – The Kawada Industries’ innovative thinking’, click here.

REFERENCES: 

1. Kobayashi I 20 Keys to Workplace Improvement Revised Edition (Productivity Press Portland Oregon 1995) 91
2. Nakajima S Introduction to Total Productive Maintenance (Productivity Press Portland, Oregon 1988) 21-29
3. Nakajima S Chapter 3 Total Productive Maintenance 21-29
4. Nakajima S “Maximizing equipment effectiveness” in Chapter 3 Introduction to Total Productive Maintenance (Productivity Press Portland, Oregon 1988) 21-29
5. Samat H. Abdul, Kamaruddin S & Abdul Azid I “Integrating of overall equipment effectiveness (OEE) and reliability method for measuring machine effectiveness” South African Journal of Industrial Engineering May 2012 Vol 23(1): pp 92-113 100
6. Sohal Amrik, Olhager Jan, O’Neill Peter and, Daniel Prajogo “Implementation of OEE – issues and challenges” https://www.researchgate.net/publication/228974091 (Date of use: 17 May 2019)
7. Mâinea Marin, Duţă Luminiţa, Patic Paul Ciprian and Căciulă Ion  “A Method to Optimize the Overall Equipment Effectiveness” (2010 Management and Control of Production Logistics University of Coimbra, Portugal September 8-10, 2010) 237-241
8. Kobayashi I 20 Keys to Workplace Improvement 91

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