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

• motivate team members,

• align team and individual goals with business objectives, and

• facilitate continuous improvement within their teams.

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

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

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

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

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

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

From an operations point of view, customer satisfaction depends on

• the continued supply of consistent and reliable quality,

• delivered on time and at a reasonable price.

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

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

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

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

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

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

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

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

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

• Do I know what is expected of me?

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

• Do I have a leader that supports me?

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

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

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

• Are there any growth opportunities?

• Are my ideas and suggestions encouraged and welcomed?

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

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

Author: Esbe du Toit: Senior Consultant

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This is the second series of Prof. Kumar’s lecture on engineering maintenance which deals with the How’s, Who’s and Where’s of maintenance activities. It follows after the first series that dealt with the Why, What, and When aspects.

You will recall that when discussing the “when” maintenance, the primary question here was, when is maintenance required? The answer proffered 5 phases of the product, which are

1 design,

2 procurement,

3 installation,

4 commissioning, and

5 operation.

In the design phase, we discussed two aspects;

1 design out of maintenance (DOM) and

2 design for maintenance (DfM),

which machine and equipment designers need to be aware of, and use intelligently to achieve the least cost and maximum fit-for-use and comfort in maintaining and operating their machinery.

1. The How phase

As stated above and underscored in the previous series, the when maintenance is primarily applied during the design phase. However, in the ‘how’ phase, both DOM and DfM are equally important, which means that the when and the how phases overlap through these maintenance activities. The benefits of both design out maintenance (DOM) and design for maintenance (DFM) have been discussed and hence, need no introduction or mention here. The importance is to highlight that both are concerned and applied during the equipment and machine design phases.

The Who and Where phases

The two phases fall in the province of machine operation, which presupposes that the equipment and machine design phases (including the ancillary procurement and installation phases) are not error-free. Even if they were so at the design and commissioning phases, once the equipment is put into operation, a new phenomenon emerges: wear and tear, that requires practitioners to ensure that the health of the machine/equipment remains close to its pristine phase akin to its original design specifications. A tall order indeed!

2. Who will perform maintenance?

There are operators who perform maintenance using various strategies

• operator or owner 
• original equipment manufacturer/contractor for full-service contracts
  • full-service contract
  • partial service contract / outsourcing
• design phase by “design out maintenance” or “design for maintenance”
• at the operation phase, in-house/outsourced or in the workshop

In-house vs outsourcing maintenance presents what Bill Lydon of Automation calls “The Big Maintenance Dilemma: In-House vs Outsourcing”  for many companies, especially the big and specialised ones. There is also a middle road in the mix, with partial outsourcing on the bedrock of in-house maintenance.

Traditionally, it is inherent in operations and management of equipment, machines, and businesses across the board, especially family businesses to keep everything in-house. However, another side of the coin to consider is “outsourcing.”

Outsourcing

The importance of workshop maintenance is emphasized by Antti Kirmanen, Chief Sales Officer of Trail Systems and Founder at Van Merryn Oy.  Kirmanen provides ‘the Ben Sapper’s perspective’ on workshop maintenance.

• Regular maintenance improves equipment’s productivity due to fewer malfunctions.
• Increased equipment life cycle – more bang for your buck!
• Shorter downtimes.
• Improved work safety – well-maintained equipment is safer to utilize.
• Improved work satisfaction – using working equipment is more pleasant than fighting with glitchy tools.
• Some equipment also involves legal obligations concerning regular maintenance or inspections. If you neglect these, you eventually get in trouble with the law.

Antii recommends a modern fixed asset management system (another story for another day) to ensure that your equipment is maintained and stays in working condition. While the workshop is regarded as a locus of maintenance activities, perhaps it must be mentioned that most maintenance of machine happens on the shop floor. It enjoins the maintenance team to be agile, flexible, and be able to move to the floor with “requisite” tools to maintain the machines in distress. However, moveable or detachable machine components such as gearboxes, pumps and other ancillaries can be moved to the factory workshop for repairs. Moving the whole machine requires different planning, and decommissioning from production to perform preventive maintenance.

Site (typical of manufacturing equipment and machines)

Most of the maintenance and repairs required in manufacturing plants are done on the shop floor, while production activities are going on. Most of us are familiar with the phenomenon. Professor Kumar makes an interesting example involving a train, passenger pr goods. If the train is broken, or any component is not in a good working order, maintenance engineers do not haul the whole train back into the workshop. Instead, they send experts to repair and fix whatever is wrong with it. The site maintenance is typical of construction sites where heavy machinery needs regular check-ups and repairs where necessary but in situ. A good example of the how to maintain heavy equipment off-site is given by “the Constructor” a mouthpiece of Construction Engineering and Management. It offers seven (7) strategies for conducting equipment maintenance in construction sites:

1 Make an inventory of assets that require maintenance

2 Involve both equipment operators and the maintenance team

3 Create proper equipment maintenance checklists

4 Create, schedule, and assign preventive maintenance tasks

5 Leave room for modifications

6 Use manufacturers’ manuals, guidelines, and recommendations

7 Educate both equipment operators and maintenance technicians

The lessons from the construction sites are universally applicable whenever and wherever one deals with equipment and machines, no matter the size. Equipment and machines provide the vital spark for our transformational operations to function, and must be treated the same way we treat ourselves, the living organisms. They are also the ‘living inanimate’ created by human beings, using the blueprint of living organisms.

The key concepts of Key 9 of the 20 Keys System for Operations Improvement are:

• Effective maintenance is a joint venture between operators and the maintenance/engineering function
• Improving overall equipment effectiveness (OEE)
• Keeping equipment logs, and performance history for equipment
• Preventive maintenance of machines/equipment (roll-out process by starting with important machines/equipment)

Keep an eye out for Part 3 of this series.

To read part 1 of The Kumar’s Why, What, When, How, Who and Where Concepts of Engineering Maintenance, click here.

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ODI’s Occupational Certificate: Project Manager ID101869 (NQF Level 5), is aligned with two of the key global project management standards

• The Project Management Body of Knowledge (PMBOK) and

• PRINCE2

and provides project management best practices, tools and techniques.

Driven by their quest for excellence across all disciplines of publishing and printing, the CTP Group of Companies enrolled a team of learners to complete the Occupational Certificate: Project Manager (11 Modules). Some learners were in Cape Town and others joined online, facilitated by ODI’s MD: Huibie Jones.

ODI has primary accreditation with the Services Sector Education & Training Authority (decision number 0228). ODI also has further programme approval to provide qualifications, learnerships, and skills programmes through merSETA and SABPP.

The learnerships offered by ODI are carefully selected, to support companies on their journey of Continuous Improvement.

To contact us, click here.

The post ODI’s Occupational Certificate: Project Manager ID101869 (NQF Level 5) with Huibie Jones and the CTP Group of Companies appeared first on ODI.

A safety culture is one in which employees take an active role in promoting and maintaining a safe working environment. To establish a safety culture, it is essential for leaders to demonstrate and promote safe behaviors.

Some of the vital behaviors for Safety Leadership include:

• Lead by example – Leaders should be the first to follow safety protocols and procedures.

• Communicate openly – Leaders should encourage open communication of safety concerns and be responsive to employees’ needs.

• Encourage employee involvement – Leaders should involve employees in identifying and solving safety problems.

• Recognize and reward safe behaviors – Leaders should reward and recognize employees who exhibit safe behaviors.

• Continuous improvement – Leaders should continuously look for ways to improve safety and reduce accidents.

Safety Leaders must also continuously educate themselves and their employees about safety regulations and procedures. By demonstrating their commitment to safety and promoting a culture of safety,

• leaders can help prevent accidents,

• reduce losses, and

• improve overall workplace morale.

In conclusion, Safety Leadership plays a critical role in establishing a safety culture within an organization. By demonstrating vital behaviors such as leading by example, communication, employee involvement, recognition and continuous improvement, leaders can foster a culture where safety is a top priority.

Author: Maryke Snyman: Senior Manager: Learning & Development

Gold Fields South Deep Mine understands the importance of Safety First and together with ODI’s Maryke Snyman, enrolled members of staff to complete ODI’s Safety Leadership Skills Programme.

Although this is a carefully selected Skills Programme, the ODI offering is not limited to this one only. Where requested, ODI has clustered certain Unit Standards to specific customer needs. To read more about our Skills Programmes, click here.

The post Safety Leadership Skills Programme with Gold Fields South Deep Mine & Maryke Snyman appeared first on ODI.

This is an opportunity for learners to demonstrate integrative understanding and application of concepts learned during the learnership. Learners who have arrived at this point are now better equipped to make contributions to operations excellence.

The mentors of the learners, as well as any other stakeholders in management are invited to attend and in so-doing show their appreciation and support.

The NQF3 Supervisory Learnership, amongst others, in module 6, covers effective presentations in great detail, starting with the 3P’:

• Plan,

• Prepare,

• Practice.

Also included are points that you need to think about while preparing, e.g.

• Who am I going to present to?

• What is the purpose of my presentation?

• What am I going to talk about?

• How will I present it?

• How large is my audience?

• How much do they know about the subject of the presentation?

• What is their general attitude likely to be, e.g. will they be interested, indifferent, skeptical, friendly, curious?

The structure of your presentation is also explained, which includes an introduction, body and conclusion. In your introduction you have to get the audience’s attention and stimulate interest. Points to consider for your introduction include welcoming the audience, identifying yourself; name, job, background, and why you are qualified to speak about the topic of your presentations, the agenda with time frames, structure, breaks, etc. and ground rules; when to ask questions, etc.

On a more personal note, I normally tell my learners that they should treat their presentation as an interview, in other words dress for success, prepare thoroughly, pay attention to the cosmetics of their presentations, practice, practice, practice, because you never know who will come and attend and it’s an opportunity for you to show a different side of yourself than the one that people see on a regular day; basically an opportunity to shine!

I think every facilitator will agree that from the moment that you mention the final presentation, usually after a problem-solving module, you can see the stress and anxiety levels increasing. Learners will come up to you and tell you that they’ve never done it before, that they won’t be able to do it, etc. As a facilitator, you have to then put their minds at ease by telling them that it’s part of the learning and that they should take confidence in the fact that they will be discussing elements from their own work areas and that they are the experts thereof.

This moment will always remind me of my own first experience and I would normally share it with them.

I still remember it, as if it happened yesterday. I started working for De Beers Group of Companies in 1999 as an ABET Educator, teaching Math and English to voluntary members of the workforce. It was the first project of its kind and I would be the first facilitator, so basically no-one to show me the ropes. Wanting to do my best, I organised my own awareness campaigns.

Part of the awareness plan, was to do a presentation at the monthly business lunch. My anxiety levels were rising exponentially, but the bottom-line was to pull myself together, put together a few slides with important information on them, and have at least three dry-runs. My manager at the time, Annamie Engelbrecht, and some of my other team-mates in training and development, would listen, give feedback and I would make changes accordingly.

I remember Annemie telling me to take along a container to dish my food in, as a take-away. At the time I didn’t understand why she said that, so I shrugged my shoulders, (in my mind, of course) and didn’t take the container along, obviously to my regret, but which I would only realise when it was too late.

As Murphy’s law would have it; my presentation was directly after lunch. Now, just to give a bit of context, the business lunch was attended by all managers, from the front-line leaders, to the middle-managers, to senior management, including the General Manager, a very big room full of people who were all above my level; my nerves, my nerves…

So there I was, feeling like a lamb on its way to the slaughterhouse, with a massive hole in my stomach, and I couldn’t eat! And then the penny dropped; I should’ve brought the food container, but it was too late and I couldn’t stomach a single piece of food and let me tell you, at these events the tables were laid.

And so, after lunch, they called my name and I took to the podium. I will tell my learners that even your own voice will sound strange to you in that moment, another reason why you should practice. Everyone who has done it before, though, will know that from the minute you finish your first sentence, the nerves will start settling somewhat and before you know it, your presentation will be over. I even answered a few questions from the floor, which, to this day, I can’t remember what they were.

After that, the usual “hey, you’ve done well” or “hey, I didn’t know you could speak so eloquently”, basically washed over me. I was just mad at myself for missing all of that glorious food☺

I know that when you’re in a panicked state, not much sinks in, but you hope nonetheless that what you share with the learners will somehow help to soothe the nerves, but I think that we can all agree that in the end, the learners step up and deliver.

Final presentations are a beautiful and meaningful way of ending off any development programme and what a rewarding moment it is when every learner from A to Z walks up to the “podium” and shine bright like a diamond.

Author: Valery Hansen

The post The purpose of ODI’s FISA presentations appeared first on ODI.

ODI’s Marni de Witt facilitated this group of CTP learners.

Based in Cape Town, CTP Printers are award winning specialists in the production of books, diaries, magazines, commercial products and newspapers. With state-of-the-art print and finishing technology and a skilled team to support it, CTP continues to provide their clients with products of the highest quality.

This qualification consists of one Overview Module, to create a common vision, and 9 practical Modules, to make operations more competitive.

The post CTP NQF3: Production Technology learnership with Marni de Witt appeared first on ODI.

Angela writes:

“My first exposure to the programme came early on in 2019 when I started peripherally assisting with the on-lining of this programme. Once it was completed and open for registration I signed up as quickly as I could. In the first virtual support session I remember asking some really ‘interesting’ questions, the answers to which became part and parcel of the everyday use of the course, but at that time I was still on track for the paper qualification.

At the start of 2021 I was invited to collaborate in the roll-out of the Operations Excellence programme on-site at a plant in Port Elizabeth. I was asked to assist in the facilitation and assessment of the NQF3 (Supervision for Energised Workteams) programme. The Port Elizabeth Operations Excellence programme involved approximately 13 NQF3 learners and approximately 8 NQF5 National Certificate in Productivity, learners. At that time, I was still enrolled – but hadn’t really progressed in the NQF5 blended learning programme.

I started attending the NQF5 discussion sessions and, more importantly, I was included in all of the plant inspections and Gemba walks & checks. This is where I started to get insight as to how little I knew and understood. Soon, some of the terminology from the NQF5 programme and the factory jargon started coming to life for me – including, and importantly, the concept of the Gemba walk. I did have a lot of questions, but fortunately I also had my NQF5 class and an excellent mentor to assist me.

The NQF5 and the NQF3 roll-outs were scheduled to run in parallel and both were offered from the same training venue and plant. The NQF5 class being the senior supervisors to the supervisors, first-line managers etc., the majority of whom were enrolled in the NQF3.

Initially, I was very nervous about committing my thoughts and understanding to paper, in the workplace activities because I realised how much of an outsider I was to the actual operations excellence programme. However, I realised that to get the best understanding of the whole process I had to work through it, so that I would be better able to reflect and use all of the learning in each situation.

One of the things that worked out to be very useful or was intentionally designed to be very useful, was that quite a lot of the contents of the NQF3 and the NQF5 complemented one other, so what I was learning in NQF5, I was teaching – at a different level – in the NQF3 programme. For instance, initially I was going to work with some of the other members of the NQF5 to create and be part of their mini-business, however, at the NQF3 level the training department needed to create its own mini-business, and so I became a member of the training department’s mini-business. We had to create our mini-business’s vision, mission, values etc and for our vision, we settled on:

“We will be a centre of Excellence for Learning and Development for the footwear industry, aiming to increase the productivity and sustainability of South African products and footwear producers.“

Ultimately this turned out to be very ironic – but we’ll get to that later.

One of the things that I hear often, which I sure most facilitators hear is: “Are the bosses being told this?” – and in this roll-out I could answer in the affirmative, and even show the NQF3 learners the page reference!

At the end of last year, I was encouraged to push past the rest of my group to finish ahead of schedule. This, of course, had its pros and cons but I felt that by then I had sufficient real-life information to do the course some justice. I did my final FISA presentation at the end of last year.

Some of the areas that have inspired me the most are:

• When my NQF3 delegates learnt about the leadership element of courage, and they realised that they have an obligation to stand up for their teams.
• My NQF3 learners saying that they can see the NQF5 learners trying to make the changes, the physical ones to the plant are relatively the easy ones in comparison to the personal changes being undertaken. The fact that parties have realised that they need to act differently is inspiring.
• The learner who tried to get out of the course for at least the first 3 modules, is now one of the trail blazers in handing in work on time, or ahead of time.
• My NQF3 learners saying that at the start of the training they couldn’t understand why they should be doing this training and why they needed to be involved – they have done things like this for the last 28 years, why change now, and now they are seeing the changes and enthusing about the why change now and showing their teams and new recruits the importance of the new ways.
• The physical changes made at the plant – and the learners asking why it took so long to make these obviously beneficial changes.
• The vision that we set as the vision for the training department was, as set out above, to be “a centre of Excellence for Learning and Development for the footwear industry”. Although this was displayed on our mini-business dashboard, I made mention of it in my FISA presentation, and I was surprised when I went to the plant the next time and found that they had taken the vision to hand and re-designed the storage area into an extended classroom, including such details in the plans to adjust the plant’s air conditioners to cool the room in summer and reverse them to warm it in winter, so that it could become a true ‘centre of excellence’.

Initially, the changes, as all change processes are, were resisted and somewhat opposed, but now the changes have a positive infectious effect:

• Where one area gets a model machine, other areas want model machines.
• Where more flow is successfully achieved in one area, other areas want less work-in-progress.

Hopefully it will also be the case that where some see NQF5 & NQF3 qualifications from ODI, others will also want to get those qualifications.”

For many businesses, it is a priority to improve the skills of their employees, and face-to-face classroom training is not always possible. ODI, however, believes that a blended approach, where there is an element of classroom, as well as technology-based learning, is stronger.

To view our online Qualifications and Learnerships index, click here.

The post NQF5 National Certificate in Productivity: An Operations Excellence game changer appeared first on ODI.

GWK is proud to be one of the largest and most diverse agri businesses in South Africa. This diversity is reflected in their most important asset, their people.

For this reason, GWK enrolled selected delegates to complete ODI’s NQF2 Production Technology learnership, faciliated by Valery Hansen.

This qualification will equip learners with the necessary skills and knowledge to efficiently perform tasks and activities, in order to continuously improve operational excellence.

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