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.

Such arguments are then the basis for capital expenditure projects where the number of breakdowns and depreciating values are argued and supported with various statistics. But does the fact that machines and equipment are old definitely (the word that should be used when testing a cause-effect relationship) lead to increasing breakdowns?  A logical test of cause-effect indicates that such a statement does not make sense, there must be another condition, making the cause-effect relationship to be “If machines and equipment are old AND we do not properly care for and maintain it, then we will have increasing breakdowns”. Maybe we should add a key performance indicator for assessing machine and equipment performance – how far, beyond the depreciation period, while retaining its earning power, do we keep machines and equipment in service?

Below is a relevant excerpt from the book “Toyota Production System” by Taiichi Ohno. He was a Japanese industrial engineer and businessman and is considered to be the father of the Toyota Production System, which inspired Lean Manufacturing across the world.

“Does the value of equipment really go down? In the case of a worker, years of experience add depth and the worker’s value to the company. A machine, lacking human qualities, is discarded after giving long service.

I want to advocate that, like workers, machines that give long service should be used with great, great care. The language of business economics talks of “depreciation,” “residual value,” or “book value” – artificial terms used for accounting, tax purposes, and convenience. Unfortunately, people seem to have forgotten that such terms have no relevance to the actual value of a machine. For example, we often hear: “This machine has been depreciated and paid off, and, therefore, we can discard it any time without loss,” or “The book value of this machine is zero. Why spend money on an overhaul when we can replace it with a new, advanced model?” This kind of thinking is a big mistake. If a piece of equipment purchased in the 1920s is kept up and can guarantee, at present, an operable rate close to 100 percent and if it can bear the production burden placed on it, the machine’s value has not declined a bit. On the other hand if a machine purchased last year has been poorly maintained and produces at only half its operable rate, we should regard its value as having declined 50 percent. A machine’s value is not determined by its years of service or its age. It is determined by the earning power it still retains. When replacing old equipment, we can look at it economically in different ways. We can compare cost analyses or interest on investment. But can such methods that appear so logical really be used in a plant? We must not lose sight of the fact that these methods are based only on premise. For example, some people think conventional maintenance is the only way. Then they decide that absolute loss can only be based on several premises. In practice, however, these methods cannot even be used as standards. Despite this, faced with a poorly maintained, deteriorating machine, they apply these methods and conclude it would be better to replace the machine. This is completely irrational.

How, then, should we decide whether or not to replace an old machine? My conclusion is that if adequate maintenance has been done, replacement with a new machine is never cheaper, even if maintaining the older one entails some expense. If we do decide to replace it, we should realize that we have either been misled by our calculations and made the wrong decision or that our maintenance program has been inadequate. When we lose an economic argument, we then argue the validity of replacement by saying “it is too difficult to restore the needed precision” or “we want to overhaul it but have no substitute machine.” This reasoning is unsound. It shows we want new machines because we don’t have a better idea. When replacing aged equipment, we should always decide case by case. Whether overhauled or renewed, if the machines are poorly maintained and driven close to death, the costs incurred by replacement is enormous. Computed as maintenance cost, for example, it would mean nothing unless an actual effect was achieved in proportion to the cost increase”.

Key 9 (Maintaining Machines and Equipment) of the 20 Keys System for Continuous Operations Improvement focuses on caring for machines and equipment, based on a joint venture between maintenance and production team.

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Lapologang Laps Letshwenyo: Business Improvement Coordinator at Morupule Coal Mine in Botswana, presented MCM’s case study of business excellence and shared Morupule Coal Mine’s achievements at ODI’s recent conference held on the 29th August 2019 at Emperors Palace in Johannesburg. His topic was:

“MCM Lean Journey – Current status, successes and way forward”

Laps started with a background of MCM, including structure, resources, vision, mission, and values. He then shared the lean journey of MCM. He addressed the challenges, as well as the successes.

To manage change was one of the critical success factors.

Improvement and goals are monitored using the radar chart. These results he shared with the delegates, as well as how this fitted into their journey to be word class.

Many success stories were shared by Laps, including:

• Reduced overtime.
• Increased production.
• Top safety achievements.

He stressed the importance of building a culture of Thinking People. Because of thinking people, and people living the values of MCM, improvements have been made in all QCDSM measures. There is a focus onsite of 100% accountability.

Laps believes that one must share the message of Continuous Improvement in a simple, understandable way, and that this would reduce any resistance against change.

He also believes in the importance of coaching your people. Coaches should:

• Be present at invited engagements.
• Live the change.
• Share key change messages.
• Interact with employees regularly.
• Track the progress of the team.
• Coach.

Below are some points that coaches should be aware off:

Laps really encouraged all delegates through his presentation, to be more involved and to carefully manage results.

To read more about the speaker presentations at our recent ODI conference where we celebrated 30 years of Operational Excellence, click on the following links:

1. Len Hansen – Consultant at Astral Operations, presented: “My Journey with Organisational Development International (ODI) since 1991.”
2. Esbe du Toit: Organisational Improvement Manager at Wispeco Aluminium, presented: “Celebrating Innovation … A snapshot of our journey towards world-class.”
3. Theo van Strijp: Managing Director at GFC, presented: “Grain Field Chickens: Road to Excellence Award.”
4. Forona Stephens: Human Resources Business Partner at GWK and her topic of discussion was: “Think Big, Start Small, Unlock Excellence.”
5. Wouter Olivier Managing Director of Sondor Performance Foams, presented: “People are the Foundation to Building a Culture of Continuous Improvement.”
6. Moemedi Merafhe: Strategy & Business Improvement Manager, Debswana Diamond Company and he spoke about : “MBA Lean Implementation Journey”
7. Nikki Moodley: Operations Improvement Executive, Astral Foods and her topic of discussion was “20 Keys… The Astral Way.”
8. Doug February – General Manager at Nampak, presented “Operational Excellence … Good to Great”

Author: Huibie Jones: MD at ODI

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The 27th International 20 Keys Conference in Bulgaria was organised by our Bulgarian 20 Keys Partner, Next Consult – www.next-consult.com

This is the first of a two part series of the conference.

On Tuesday 22 October 2019, we had 8+ world-class speakers sharing their business excellence. Below follows a brief summary of the presentations delivered on the day.

All delegates were welcomed by the Managing Partner of Next Consult, Mr Orlin Dochev

The first speaker at the conference was the owner of the 20 Keys Trademark, Dr Yoshi Kobayashi.

Yoshi gave an interesting history of the 20 Keys.  He also referred to where all 27 conferences have been held to date.

Yoshi spoke about his father, Iwao Kobayashi, who was the developer of the 20 Keys programme in 1982. 

He developed the programme, after his retirement from Mitsubishi Heavy Industries, based on his extensive experience as an original methodology. Iwao Kobayashi worked regularly until the age of 85, and passed away, aged 93, three years ago. Yoshi explained that he himself has worked within industries using the 20 Key programme, for the last 24 years. In Japan, the 20 Keys programme is referred to as PPORF (Practical Program Of Revolutions in Factories). The first 20 Keys book was published in the USA in 1990, and this was when the name 20 Keys was chosen for the programme. 

Yoshi highlighted that Practical Programme, emphasising the fact that it is not just theory. When implementing this programme, one must expect results in Quality, Cost and Delivery. It helps to improve productivity, as well as quality. It is also a programme that encourages all employees to participate. The programme started off as a programme for factories, but it has since been developed to include many other industries such as banks, service industries, mines, agriculture, and many more.

Yoshi spoke about the implementation of the 20 Keys, and how all the Keys influence one another. He highlighted the importance of visual management, and multi-level meetings. He also visually explained the journey of the 20 Keys in Japan. He ended off by saying that when a company realises the value that the 20 Keys programme brings, they don’t stop using the programme, as they see continuous results.

Yoshi closed his presentation by thanking Next Consult, and by asking the delegates to contact Next Consult, so that they can assist them in their Continuous Improvement journey.

The second speaker at the conference was Toshko Petkov, Partner Consulting, Next Consult

” Engaging People Through Lean”

Toshko highlighted the importance of engaging people on your lean journey. 

• Companies can be successful only with happy people. 
• Customers can be satisfied only by motivated people. 

The biggest motivator for people is self-development, and the 20 Keys programme is an excellent opportunity to learn and develop. He focused in particular on the Keys that support an energised workplace, i.e.:

Key1: Cleaning and Organising to Make Work Easy

The main objective of Key 1 is to make work more safe, easier, and more productive, by creating organised and clean workplaces that all people are proud of. This leads to energised work teams, improved motivation, and improved productivity of people and machines. It is a very powerful Key, and creates the foundation for many improvements to follow.

Key2: Rationalising the System/Goal Alignment

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

In order to find, supply and keep customers, the organisation must provide products and services at a competitive price (cost), highest quality, shortest delivery time, AND they must provide excellent customer service. This is also the goal of the internal supply chain. The relationship of the different mini-businesses in the supply chain must, therefore, be a partnership. In a partnership, suppliers and customers work together to achieve a common goal. The partnership must benefit both partners at the end of the day.

He indicated the relationship between the first-line manager, mini-business customers, suppliers, team members and the manager, as per the diagram below.

In the mini-business, the aim is to empower all team members, in order to encourage their involvement, and to strengthen their ability to make decisions. It must be very clear to all what is expected from them, how it must be done, and where to get help from, if needed. 

Key3: Small Group Activities

Key 3 involves forming small groups, who get together to solve problems. These problem solving groups are called SGAs (small group activities). An SGA consists of a small group who “brainstorm” specific problems relating to quality, cost effectiveness, delivery, safety, etc., and who then addresses identified issues by using the SGA methodology, and various tools and techniques. Key 3 also engages everyone with thinking about improvements, which might not necessarily require a small group for improvement (suggestions and innovations)

One of the most powerful ways in which to engage people to improve performance, is through the CAPDo cycle.

Toshko shared examples of the strength of this cycle, and the importance of using it in the workplace. He discussed the process of motivation, as well as how you can motivate your people through empowering them. 

Key10: Workplace Discipline

This Key focuses on the creation of a positive atmosphere, maintaining a high level of commitment in the workplace, and respect amongst people. Attitudes that are promoted include adherence to values, organisation policies and rules, safety, Key 1 discipline, and time discipline. The emphasis is on the application of self-discipline by all.

Key15: Skill Versatility and Cross Training

Skill versatility and cross training is about ongoing implementation of learning and development as part of the business strategy, to develop individuals to have more skills, and higher levels of competencies. Apart from individual development, the aim is also to have flexible teams, with a high level of skill versatility within, and between all teams; in production and supporting functions.

Toshko is a very engaging speaker, and his presentation was thoroughly enjoyed by all delegates

The third speaker at the conference was Huibie Jones, Managing Director, ODI, South Africa – “Produce Better”

Huibie sent greetings from the South African team, and informed everyone that ODI has just celebrated 30 Years of Excellence at a national conference. 

She spoke about first meeting Iwao Kobayashi, the developer of the 20 Keys programme at the 5th International 20 Keys Conference in 1997. 

Huibie focussed on the Keys that support Quality, in order to Produce Better in the workplace.

How to measure the cost of Quality was discussed, as well as all the critical concepts of these Keys. The importance of doing things right the first time was emphasised.

Focus was also placed on the importance of SGAs (small group activities), to find the root causes of quality problems.

Extracts from several case studies were shared, and the presentation was ended with a wish for all to have a To-Do list, with lots of actions on how to do things better.

The fourth speaker at the conference was Shereen Mosallam, General Manager, Symbios Consulting Group – “Deliver Faster”

Shereen has been a senior 20 Keys consultant since 2017. She shared the journey of the 20 Keys in Egypt, as well as several case studies from different companies. They have to date supported over 400 companies in 12 different countries. 

She spoke with specific reference to the Keys employed to Deliver Faster

Shereen spoke about the frustration of trying to squeeze the same orange over and over again, when there is already no more juice in it.

She challenged people to think differently, and to, with the help of the 20 Keys, start achieving different results. She illustrated this with some fun graphics.

Shereen emphasised that we do all of this in order to become better companies, and at the same time, to deliver faster. By implementation of the relevant Keys, we will reduce inefficiencies, thereby streamlining all processes. 

The importance of several other points was also discussed, such as:

• Reducing inventory.
• Using value stream maps.
• Proper planning.

Shereen truly captured the attention of the audience, and below are some of the recommendations that she shared with everyone:

• Keep Moving, and Keep Improving.
• Use an infinity cycle.
• Be Better–Faster–Cheaper, and Energise your people.
• Learn from failures, before you learn from successes.
• Fail in a smart way.
• Start by strengthening your operational excellence strategy, before choosing technology; not the other way around.
• Your people are your biggest asset.

Author: Huibie Jones: MD at ODI

To view more images of the recent 27th International Conference in Bulgaria, click here. Keep an eye out for the second blog about the recent conference.

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