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.

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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1. Sort (Seiri) 

Identify and get rid of all obsolete items (i.e., anything not used for months or during the last 12 months).

2. Set in Order (Seiton) 

Now that all unnecessary items are removed, organise that which remains.

3. Shine (Seiso) 

Once everything is organised, clean up.

4. Standardise (Seiketsu) 

Maintain cleanliness and orderliness through writing simple procedures so that unacceptable situations will not reoccur.

The benefit of each of the activities is probably as important as the goal of the physical exercise itself.

Some of these benefits are:

• It gives first-line employees the opportunity to have a say in what happens in their work area
• It provides employees with a chance to study problems and to suggest solutions in a structured way – thereby energising the workplace
• Employees learn a very simple, but practical and effective problem solving technique, called the 5 Whys. It is used for identifying root causes and can be used in different situations.
• Employees learn that procedures are there to ensure that bad / poor situations never occur again
• It puts fun into the workplace when doing something other than the normal daily tasks, especially if competition between teams starts developing
• Employees learn the value of teamwork between teams or shifts. Identify and get rid of all obsolete and unneeded items (including items which do not belong to the area).

Many people know the process as the 5S process. In the 20 Keys System, Key 15 – Workplace discipline, fulfills the role of the 5^th S – Self-discipline – Make a habit of ‘clean and organise as you go’, and properly maintain procedures and schedules.

During their continuous improvement journey, Wispeco Aluminium learners discussed the 4S Process with ODI’s MD: Huibie Jones.

Wispeco benchmarks their Key 15 principles on a regular basis and this is evident in their implementation of the 4S process.

To read more about the 5S Method for efficient workplaces, click here.

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The focus of the 2019 Astral 20 Keys Conference was on Key 18 (Using Information Systems), and Key 20 (Leading Technology).

The Astral Workshop Co-ordinators were:

• Evert Potgieter (Director: Risk Management) and
• Nikki Moodley (Operations Improvement Executive)

The Astral 20 Keys conference was opened by Nikki Moodley. Nikki stressed the importance of new ways of thinking, and that all people need to have an awareness of how to manage change. She also encouraged people to always be on the lookout for opportunities in the workplace.

An interesting aspect of the presentation by Jaclyn was the integration between ISO17025, and the 20 Keys.

Jaclyn Otto – CAL

Focussing on Key 18 (Using Information Systems), she emphasised that “Data is Gold”. Jacklyn gave an extensive overview of all data used at CAL. She also indicated the importance of Key 2 (Goal Alignment), Key 3 (Small Group Activities), Key 12 (Supplier Development), Key 13 (Eliminating Waste), and Key 17 (Efficiency Control). For her, the success of information lies in an integrated approach.

Johan van Niekerk – Meadow

Johan emphasised the importance of efficiency and standardisation. Key 11 (Quality Assurance) is of particular importance to him when addressing Key 18 (Using Information Systems). To implement Key 18 successfully takes perseverance, and good documentation. To continuously train people (Key 15) is also very important. He also highlighted that Repetitive Actions do NOT constitute work; a good Key 13 (Eliminating Waste) principle.

Andy Crocker – MD: Commercial Division

Andy highlighted an important 20 Keys principle; how essential people support is in Technological Advances.  Planning and standardisation was the theme during his address. He also pointed out the role that Key 2 (Goal Alignment) plays, as well as the fact that Key 15 (Skills Versatility) underpins the success of new ways of working.

Jerry Richards – Festive

During his presentation, Jerry highlighted how integrated the success of Key 18 (Information Systems) and Key 20 (Leading Technology) is with other Keys. He spoke in particular about Key 11 (Quality Assurance), Key 12 (Supplier Development), Key 10 (Self-Discipline), Key 15 (Skills Versatility), and Key 17 (Efficiency Control). He pointed out the increase in efficiency when an integrated approach is followed.

Gary Arnold – MD: Agricultural Division

An interesting angle to Gary’s presentation was the advances made in Genetics Technology.  He also gave an overview of the applications of technology across different sites, and emphasised the role that the 20 Keys plays in all of this.  Key 18 (Information Systems), in particular, supports the very important aspect of traceability of information.

Basson Viljoen – AFS System

Basson did a most interesting overview of the AFS. The role that people play also featured a lot during his overview. He quoted Steve Jobs as saying that people are very smart, and if given the opportunity, they will improve.

One way in which to ensure improvement is to Eliminate Waste (Key 13) through standardisation, eliminating duplications, and capturing as close as possible to the source.

Nikki Moodley – 20 Keys Electronic Benchmarking

Nikki demonstrated the new electronic 20 Keys benchmarking system. She also spoke about the tried and tested method of benchmarking, using the 20 Keys principles. By doing this benchmarking, a company can see how well they do in terms of world class standards.

Some important reasons to benchmark included:

• Understanding your performance.
• Engaging people.
• Encouraging accountability.

Fostering a climate of continuous improvement.

Michael Schmitz  – MD: Feeds Division

Michael gave an overview on how Key 20 (Leading Technology) is used in the Feed Industry. It was also interesting to hear how Key 18 (Using Information Systems) starts to form a basis for building relationships between different stakeholders.

Mark Surendorff – COO: Meadow Feeds

Mark did an overview of how Meadow Feeds have revitalised the implementation of the 20 Keys, using slogans, colour, and lots of people involvement.

He also presented an excellent example of implementing Key 2 (Goal Alignment).

Throughout the day, Nikki Moodley made valuable contributions.

She highlighted amongst many things:

• The importance of the integrity of data, in order to be useful for analysis.
• Good data makes for better conversations.
• The importance of the CAPDo cycle.
• That we must remember that an SLA with internal suppliers is as integral to the business as an SLA with external suppliers.
• That we must continuously ask ourselves how we can improve.

Nikki spoke about the privilege to have the 20 Keys as the Astral Continuous Improvement Initiative. She spoke about the uniqueness of the 20 Keys programme, and the way in which it provides structure. She also encouraged everyone to use the 20 Keys to its fullest potential.

A great energiser was Nikki’s Spinning Wheel!.

The wheel randomly selected people to share their experiences. People selected included:

• Adri van Heerden
• Allan Moodley
• Disebo Ngwenya
• Michael Molebats
• Karin Brooks
• Alewyn Carstens
• Colin Ntsoane

Great feedback was received from all these delegates.

The day was closed by Evert Potgieter.

He spoke about the important role that people play. Evert highlighted a couple of Trendy Future Developments, and quoted and extract from Steve Jobs saying: “What’s important is that you have faith in people and that you give them the right tools to do the job. “

All Astral delegates left the conference feeling valued and challenged by all the new Technology!

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

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After observations on the line by the team and a root cause analysis, a different method of packing was applied. Feedback from packers and observations during the experiment indicated an increase in efficiency. This will also help with the selection of quality pieces for packing. From an ergonomics point of view some trays need to be installed on the line.

The team presented their ideas to the Production Manager, Lynne Holliday. Any possible negative effects from the change were also discussed (an important part of root cause analysis). During implementation, KPIs for correct packing and quality will be monitored by the team and QC.

Well done to the team, led by Sanah, the Floor Controller!

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

Author: Johan Benadie – Director at ODI

The post Johan Benadie and a SGA in Standerton appeared first on ODI.

Recently I did a 20 Keys progress review at Wispeco Profiles Department. It is quite a large workplace with five 5 extrusion press lines.

Richard Muller and his team took the action list and meticulously scrutinised and implemented each action point. Review (Check) today a strong level 3.5, for a safe, efficient working and workflow, well organised and clean workplace. It is really satisfying to walk on the floor and being approached by operators asking what I think of their workplace? A day of happy people (and I am sure happy customers).

I think Richard will agree that the underlying best practice operations principles are the same, regardless of whether it is an ice-cream or aluminium extrusion factory. And today it almost looked like an ice-cream factory…

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

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