Lean Six Sigma and AI - Next Generation of LSS

Lean Six Sigma and AI is an exciting combination that is gaining more attention in the business world.

Lean Six Sigma is a methodology that aims to eliminate waste and reduce variability in business processes, leading to increased efficiency and cost savings. Various industries have adopted it since.

On the other hand, Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionise many industries. And it already is revolutionising a few. Its applications range from automation and decision-making to natural language processing and image recognition.

When we talk about Lean Six Sigma and AI, we are discussing how combining the two methodologies can drive process improvements and increase efficiency. In this blog post, we will explore how we can integrate AI into Lean Six Sigma to enhance its effectiveness and boost the results. Lean Six Sigma and AI is a powerful combination that has the potential to bring significant benefits to businesses.

How can Lean Six Sigma and AI come together

Lean Six Sigma mostly is about 4 things if we look at it from a wide lens. First, identifying the problems and issue that the processes or businesses are facing. Second, collecting data and analysing it to get to the root causes. Third, brainstorming solutions around the identified root causes. And lastly, Implementing those solutions, monitoring them and ensuring that the processes stay in control. That they do not roll back to their previous state. That’s it.

Artificial Intelligence, and the various AI tools we have at our disposal, such as prediction modelling, machine learning, data analysis, computer vision and image recognition, natural language processing to name a few, suits perfectly in one or other objectives of Lean Six Sigma methodology.

Lean Six Sigma and AI - Fit for DMAIC Approach — Lean Six Sigma and AI – Fit for DMAIC Approach

AI in DMAIC approach

Even if we look at the classic DMAIC approach, the combination works surprisingly well.

Artificial Intelligence (AI) has the potential to enhance the power of Lean Six Sigma methodology by providing new ways to analyze and optimise processes. We can use AI and integrate it into each of the DMAIC stages to improve the overall efficiency and effectiveness of the process.

In the Define stage, AI can assist in identifying problem areas and prioritizing process improvement opportunities by analyzing large amounts of data, real time and identifying patterns. That’s a brilliant way to automate opportunity generation for Lean Six Sigma projects.

In the Measure stage, AI can help collect the required data through continuous monitoring of process performance in real-time. It can mistake proof data collection and eliminate the need to tools like Gage RnR completely. And save many dollars and huge time spent in collecting required data.

In the Analyze stage, AI can assist in identifying the root causes of process problems by analysing data and identifying patterns and relationships. It can analyse huge quantity of data within seconds and generate customised insights. Qualitative data, images, verbatim, customer complaints, everything can be in scope for analysis.

In the Improve stage, AI can help identify and optimise process improvements. It can browse through the vast amount of information available, look for best practices and suggest optimal, best fit solutions. AI can also predict potential needle movement post implementing a particular solution, to ease decision making and prioritisation. Furthermore, it can also help plan implementation and track progress.

Finally, in the Control stage, AI can assist in monitoring process performance over time and identifying any signs of deviation. It can let the project owners know the effectiveness of implemented solutions. And can also course correct the solutions in case of deviations.

Benefits of AI in Lean Six Sigma

Artificial Intelligence (AI) has the potential to bring significant benefits to businesses when integrated with Lean Six Sigma. The ability of AI to automate repetitive tasks, to assist in data analysis and decision-making, in identifying and predicting potential problems before they occur can have a positive impact on the effectiveness of Lean Six Sigma. This can lead to increased efficiency, cost savings, and improved process performance. Let’s explore some of these benefits below.

Automating Repetitive Tasks with AI

One of the key benefits of integrating Artificial Intelligence (AI) into the Lean Six Sigma methodology is the ability to automate repetitive tasks. By using AI to automate these tasks, businesses can free up time and resources for more important work.

In the manufacturing industry, robots and automation are performing repetitive tasks such as assembly line work. According to a study by McKinsey, the use of robots in manufacturing can lead to a 25-85% reduction in labor costs.

In the healthcare industry, AI-powered chatbots are answering common patient questions. Freeing up time for doctors and nurses to focus on more critical tasks. A study by Accenture found that the use of AI-powered chatbots in healthcare can lead to cost savings of up to 30%.

Data Analysis with AI

Another key benefit of integrating Artificial Intelligence (AI) into the Lean Six Sigma methodology is the ability to analyze large amounts of data and identify patterns and trends. By using advanced analytics and machine learning algorithms, AI can help teams make better decisions and improve process performance.

AI-powered predictive analytics in retail industry can help analyze customer data and identify patterns and trends. This can lead to better decision-making, such as optimising inventory levels, improving customer service, and increasing sales. A study by Accenture found that the use of AI-powered predictive analytics in retail can lead to a 5-15% increase in revenue.

AI-powered fraud detection tools can analyze transaction data and identify patterns that indicate fraudulent activity in banking and finance industry. This can lead to better decision-making, such as blocking fraudulent transactions and preventing financial loss. A study by PwC found that the use of AI-powered fraud detection in finance can lead to a 50-70% reduction in fraud losses.

Problem Prediction and Solution Recommendation with AI

A significant benefit of integrating Artificial Intelligence (AI) into the Lean Six Sigma methodology is the ability to predict potential problems and recommend solutions. By using advanced analytics and machine learning algorithms, AI can assist in identifying and predicting potential problems before they occur, allowing teams to take proactive measures to prevent them.

AI-powered predictive maintenance can predict when equipment is likely to fail. By identifying potential problems before they occur, teams can take proactive measures to prevent equipment downtime, which can lead to increased efficiency and cost savings. A study by McKinsey found that the use of AI-powered predictive maintenance in manufacturing can lead to a 30-50% reduction in downtime.

Another example is in the transportation industry. AI-powered traffic prediction can predict traffic congestion and recommend routes to reduce travel time. By identifying potential problems before they occur, teams can take proactive measures to reduce travel time and improve efficiency. A study by the World Bank found that the use of AI-powered traffic prediction in transportation can lead to a 10-20% reduction in travel time.

This can span from predicting if an invoice will be paid on time, predicting if a customer is likely to default on mortgage payment to predicting customer churn to competition. The scope is limitless.

Forecasting and Modelling with AI

Another key benefits of integrating Artificial Intelligence (AI) into Lean Six Sigma methodology is the ability to create accurate forecasting and modelling for Lean Six Sigma projects. By using advanced analytics and machine learning algorithms, AI can assist in creating accurate predictions for future process performance and identifying potential issues.

For example, AI-powered predictive modelling can forecast patient outcomes and identify potential issues. By creating accurate predictions, teams can take proactive measures to improve patient outcomes and reduce healthcare costs. A study by Deloitte found that the use of AI-powered predictive modelling in healthcare can lead to a 20-30% reduction in healthcare costs.

AI-powered forecasting predict demand and optimise supply chain operations. By creating accurate predictions, teams can take proactive measures to improve efficiency and reduce costs. A study by MIT found that the use of AI-powered forecasting in logistics can lead to a 30-50% reduction in supply chain costs.

Process Optimisation with AI

Another key benefit of integrating Artificial Intelligence (AI) into the Lean Six Sigma methodology is the ability to optimize processes through anomaly detection and improved measurement accuracy. By using advanced analytics and machine learning algorithms, AI can assist in identifying process deviations and inefficiencies, and suggest improvements.

AI-powered anomaly detection identify and predict machines and systems failures. By identifying potential issues before they occur, teams can take proactive measures to prevent downtime and reduce maintenance costs. A study by McKinsey found that the use of AI-powered anomaly detection in manufacturing can lead to a 20-30% reduction in maintenance costs.

AI-powered process optimisation can improve the accuracy of financial measurements and predictions. By creating more accurate predictions, teams can take proactive measures to improve decision-making and reduce risk. A study by PwC found that the use of AI-powered process optimisation in finance can lead to a 10-20% reduction in risk.

Real-world Examples of AI in Lean Six Sigma

One of the most powerful ways to demonstrate the potential of Artificial Intelligence (AI) in Lean Six Sigma is through real-world examples of companies that have successfully implemented AI in their processes. Here are a few examples of how companies have leveraged AI to improve their Lean Six Sigma performance.

AI in Medical Imaging at GE Healthcare

GE Healthcare is one of the leading companies that have successfully implemented AI in their Lean Six Sigma processes.

One notable example is their use of AI-powered image recognition technology, Edison, to assist radiologists in identifying and diagnosing medical conditions. Edison is a machine learning platform that is trained to identify and diagnose medical conditions in images, such as CT scans and MRI scans. It can detect, classify, and localize over 50 different conditions, including tumors, infections, and fractures.

According to a study published in the Journal of Digital Imaging, the use of AI in image interpretation led to an average improvement of 8.5% in diagnostic accuracy in general and 11% in diagnostic accuracy for breast cancer. This is in addition to a 15% increase in diagnostic confidence and a 25% reduction in reading time for radiologists. And they can now look at 30% more cases per day. Edison is helpful in reducing the number of false positives and false negatives, which improves patient outcomes and reduces the risk of unnecessary treatments.

By automating repetitive tasks and reducing the workload of radiologists, GE Healthcare has been able to improve the speed and accuracy of diagnoses, leading to better patient outcomes.

AI-powered logistics optimisation for UPS

UPS, one of the world’s leading logistics companies, has successfully implemented AI in their Lean Six Sigma processes to optimize their delivery routes. By analyzing large amounts of data on package volume, traffic patterns, and weather conditions, UPS has been able to reduce the number of miles driven by its delivery trucks by more than 30 million miles per year. This has resulted in significant cost savings and reduced carbon emissions.

In a pilot program in 2018 itself, UPS was able to reduce the number of miles driven by delivery trucks by 10%, equivalent to saving 3 million gallons of fuel and reducing carbon emissions by 31,000 metric tons.

This has helped UPS in efficient delivery routes planning, enhancing customer satisfaction by quicker delivery time and on-time deliveries and also contributed to their sustainability goals.

Lean Six Sigma and AI for efficient production line at Jabil

Jabil, a manufacturing company, has successfully implemented AI-powered process optimization to improve the efficiency and quality of its production lines. By using machine learning algorithms to analyze data on production processes, Jabil has been able to identify and eliminate inefficiencies. This has led to improved production yields and reduced costs.

Jabil has reported a significant increase in production efficiency, resulting in improved production yields and cost savings. For example, Jabil was able to reduce the rate of defects in one of its production lines by 50% by identifying and addressing the root causes of defects using AI-powered process optimization.

The implementation of AI in Jabil’s production processes has had a positive impact on the company’s bottom line. The cost savings and improved production yields have allowed Jabil to remain competitive in the manufacturing industry and improve customer satisfaction.

One specific example of Jabil’s use of AI in process optimization is in its circuit board assembly line. By using machine learning algorithms to analyze data on production processes, Jabil was able to identify and eliminate inefficiencies in the assembly process, resulting in a 25% increase in production yields and a 20% reduction in costs.

These examples demonstrate how companies in different industries have successfully implemented AI in their Lean Six Sigma processes, leading to improved efficiency, quality and cost savings. By leveraging AI, companies can gain a competitive advantage and achieve better results in their Lean Six Sigma initiatives.

Challenges and considerations in Lean Six Sigma and AI integration

Implementing Artificial Intelligence (AI) in Lean Six Sigma can bring significant benefits to a business. However, there are also potential challenges that we need to consider before embarking on this journey. Let us look at the main challenges of implementing AI in Lean Six Sigma, and how to overcome them to ensure successful implementation.

Data Privacy and Security

One of the main challenges of implementing AI in Lean Six Sigma is ensuring the privacy and security of the data used for training and testing the models. The data used for training AI models often contains sensitive information, such as personal information, medical records, and financial data. Ensuring that we keep this data protected against unauthorised access and breaches is crucial to the success of the implementation. This can be particularly difficult in industries involving sensitive information, such as healthcare and finance.

To overcome this challenge, companies can implement strict data governance policies and procedures, such as data encryption, access controls and robust security protocols. Regularly monitoring and auditing the data to identify and respond to any potential threats is also of foremost importance. Additionally, companies can work with trusted third-party providers to ensure the safe handling and storage of their data.

Model Interpretability

Another challenge of implementing AI in Lean Six Sigma is interpreting the results of the models. AI models are often complex and opaque, making it difficult for human experts to understand how they arrived at their conclusions. This can make it difficult to identify and correct errors in the model, and can also make it difficult to explain the results to stakeholders. This again is particularly important in industries which are highly regulated, such as healthcare and finance. In such industries, decision-making processes must be transparent and accountable.

To overcome this challenge, companies can use techniques such as feature visualization. Explainable AI (XAI) and Model interpretability techniques such as LIME, SHAP, and ELI5 can be useful. This aims to make the inner workings of AI models more transparent. Additionally, companies can use techniques such as model explainability, which aims to provide human-readable explanations of the results of the models.

Ethical Considerations

Third challenge of implementing AI in Lean Six Sigma is ensuring that the models are used ethically and do not discriminate against certain groups of people. As AI systems become more prevalent in decision-making processes, it becomes increasingly important to consider the ethical implications of their use. This includes issues such as bias, fairness, and accountability. AI models can inadvertently perpetuate biases and discrimination if the data used for training the models is not representative of the population it is intended to serve.

To ensure that AI systems are used ethically, companies can implement guidelines and protocols for responsible AI development and use. Organizations like the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems provide such guidelines. Companies can also use techniques such as bias detection and correction, data cleaning, and algorithmic transparency. And can ensure that their AI models are transparent and accountable. Additionally, they can also establish a strong governance framework and ethical guidelines to ensure that their AI systems are aligned with their values and principles.

Importance of Lean Six Sigma and AI experts collaboration

One of the key factors in ensuring a successful implementation of AI in Lean Six Sigma is collaboration between Lean Six Sigma and AI experts. By working together, these experts can combine their knowledge and skills to make the most of the technology and overcome any challenges that may arise.

For example, AI experts can provide the technical knowledge and expertise needed to effectively implement and use the technology. Lean Six Sigma experts can provide the process and quality expertise needed to ensure that the technology is implemented in a way that is aligned with the overall goals of the organization. Together, these experts can develop a comprehensive strategy for using AI to improve processes and achieve desired outcomes.

Collaboration between Lean Six Sigma and AI experts also helps to ensure that the technology is used in a way that is consistent with the organization’s values and ethical considerations. By involving both groups in the decision-making process, organizations can ensure that any potential risks are identified and addressed, and that the technology is used in a way that is consistent with the organization’s overall mission and values.

In addition, collaboration between Lean Six Sigma and AI experts can also help to ensure that the technology is used in a way that is consistent with data privacy and security regulations. By involving both groups in the decision-making process, organizations can ensure that any potential risks to data privacy and security are identified and addressed, and that the technology is used in a way that is compliant with all relevant regulations.

The Future of AI in Lean Six Sigma

The future of Artificial Intelligence (AI) in Lean Six Sigma is rapidly evolving, and organizations need to stay informed about the latest advancements in the field to stay competitive. With the integration of Lean Six Sigma and AI, organizations can achieve improved efficiency, better decision-making and increased cost savings. However, to make the most of these advancements, organizations must be aware of the latest developments in AI, and how they can be applied to their specific business processes.

One of the key areas where AI can have a significant impact is in process optimization. Machine learning algorithms can analyze data on production processes and identify inefficiencies, leading to improved production yields and reduced costs. Another area where AI can have a positive impact is in decision-making. AI-powered decision support systems can help organizations make better decisions by providing them with valuable insights and recommendations based on data analysis.

In the near future, AI is expected to evolve in several ways. One of the most significant advancements is the integration of AI with Internet of Things (IoT) devices. This will enable organizations to collect and analyze large amounts of data from various sources, leading to better insights and more accurate predictions. Additionally, the development of edge computing will enable organizations to run AI algorithms on devices at the edge of the network, reducing the need for cloud-based infrastructure.

The future of AI in Lean Six Sigma is bright, with advancements in the technology expected to continue shaping the way organizations approach process improvement. To stay competitive, organizations must stay informed about the latest advancements in AI and consider how they can be incorporated into their Lean Six Sigma processes.

Concluding thoughts

Artificial Intelligence (AI) has the potential to revolutionise the way organizations approach process improvement and efficiency. One of the ways that AI is being applied in this space is through the integration of AI into Lean Six Sigma methodologies. By using machine learning algorithms to analyze data, inefficiencies can be identified and eliminated, resulting in improved production yields and reduced costs.

However, implementing AI in Lean Six Sigma is not without its challenges. Data privacy and security, model interpretability, and ethical considerations are all important factors that need to be taken into account. To overcome these challenges and ensure a successful implementation, collaboration between AI and Lean Six Sigma experts is crucial. By working together, they can make the most of the technology and overcome any obstacles that may arise.

AI has the potential to greatly enhance the effectiveness of Lean Six Sigma methodologies, but organizations must approach implementation with caution and be prepared to overcome any challenges that may arise. By staying informed about the latest advancements in AI and fostering collaboration between AI and Lean Six Sigma experts, organizations can stay competitive and achieve significant improvements in efficiency and quality.

Master Lean and Six Sigma Acronyms in No Time!

The Ultimate Guide to LSS Lingo – Yours for Free

Subscribe and Get Your Hands on the Most Comprehensive List of 220+ LSS Acronyms Available. No more searching for definitions, no more confusion. Just pure expertise at your fingertips. Get your free guide and other ebooks and templates today. Download Now!

Sachin Naik

Passionate about improving processes and systems | Lean Six Sigma practitioner, trainer and coach for 14+ years consulting giant corporations and fortune 500 companies on Operational Excellence | Start-up enthusiast | Change Management and Design Thinking student | Love to ride and drive