Will digital replace Lean and Six Sigma?

For the past century or so, modern manufacturing has been guided largely by Lean and Six Sigma principles. But is that all about to change with the rise of the digital factory?

While Lean Manufacturing has a laser-like focus on eliminating waste to streamline operations, improve efficiencies and reduce costs, Six Sigma is a statistics-driven approach to achieving consistency of output and reducing defects. Each framework complements the other in working towards operational excellence.

Navigating the new era of Industry 4.0, we’ve reached an exciting stage where we now have some revolutionary digital tools like AI and machine learning that use big data to radically alter our process efficiencies and product quality, and consequently solve some of our biggest production challenges.?

In this article, our aim is not to explore the much-debated merits, understanding and implementation of Six Sigma versus Lean methodologies, but rather to unpack how digital tools can support their distinct goals and allow us to focus on not only solving problems but also, more radically, preventing them from happening in the first place.

When Lean Manufacturing, which is focused on efficiency and waste reduction, collaborates with Six Sigma's precision and quality-centric approach, and is further augmented by Industry 4.0's data-driven insights and automation capabilities, there is an unlocking of significant advancements in manufacturing performance.

Digital transformation won’t take away the need for Lean or Six Sigma but rather provide the tools to make them work better, reduce variability, and achieve their goals faster. Let’s take a look at just two examples of how both systems can benefit.

Reducing variability: Cycle times

Lean Manufacturing is focused on delivering maximum value to the customer by reducing waste throughout the production line. It does so in part by optimising and reducing resource use. Whatever doesn’t add value, or that which the customer isn’t willing to pay for, is technically waste. While what classifies as waste is a source of debate, it does not only refer to physical resources but also to time.

Cycle times are one of the key metrics in Lean Manufacturing, measuring the average amount of time it takes to complete a production process from start to finish. The goal is to reduce cycle times and ideally keep them closest to the fastest repeatable time.

Today, the development of digital tools allows us to monitor variability in cycle time and identify areas of waste on even the biggest production lines.

The Industry 4.0 difference lies in the ability to collect and analyse data at a granular level, right down to the cycle time of every operation within the production process. Now, Lean experts and process engineers can carry out a variability analysis on individual operations – which is where “ghost bottlenecks” exist.

Ghost bottlenecks are caused by operations that disrupt the performance of the preceding and succeeding stations. Although they meet the targeted cycle time when averaged, these operations are unstable and still cause delays. Much like cars on a highway, they frequently change speed, alternately slowing down the cars behind them, and then racing away over the speed limit, but ultimately arriving late, despite having an acceptable average speed.

Being able to evaluate hundreds of operations in real time allows a process engineer to not only identify and take corrective actions where they see problems but also to use the live trend analysis to forecast potential bottlenecks, where they see increasing deviations in individual operation times.

This makes it much easier to identify and address these invisible bottlenecks to help keep production flowing smoothly and ultimately improve cycle times.

Reducing variability: Quality

Six Sigma, on the other hand, places its focus on quality – often requiring multiple quality inspection points throughout a production line. It places emphasis on process optimisation through statistical analysis and is reactive in nature.

With Industry 4.0 capabilities, manual calculations that were painstakingly done by Six Sigma professionals can now be automated through machine learning.

One of these calculations is the process capability index (Cpk), which measures a process’s ability to produce an output or product within a customer’s specification limits consistently.

A live Cpk analysis can be performed on hundreds of operations concurrently, with the automated analysis arranging them from most to least unstable, thereby directing the quality engineer to the processes that require immediate attention.

Since the analysis is automated, it eliminates the time-consuming admin of manual data capturing and evaluation, enabling engineers to focus immediately on resolving the issues.

Traditionally, process capability indices only measure ‘natural variation’ in a process, and cannot account for deviations which are not expected and due to variables such as damaged or worn equipment.

With the integration of appropriate Industrial Internet of Things (IIoT) sensors for machine monitoring and the consolidation of all measurements into a unified data platform, these evaluations can now be performed in real-time, without manual oversight. Even better, this live production data can feed predictive models that anticipate process deviations before they exceed acceptable limits and compromise quality.

A new paradigm

As the manufacturing environment and its methodologies continue to evolve, the synergy between Lean, Six Sigma, and Industry 4.0 is emerging as a transformative paradigm for this new landscape.

While Lean and Six Sigma offer structured frameworks to minimise waste and maximise quality respectively, their methods often remain reactive and resource intensive. With the integration of Industry 4.0 technologies, these methodologies are not just enhanced, but revolutionised – moving from reactive problem-solving to proactive problem prevention.

Once-manual processes of inspection, data analysis, and decision-making are now accelerated and augmented, making way for more informed, agile, and efficient operations.

The first steps towards digital integration, however, require careful planning and consideration. It all starts with your organisational needs and objectives. It's about identifying where technology can support traditional methodologies and where new approaches might be beneficial.

As manufacturers continue to navigate this space, a balanced approach that respects both the old and the new will likely yield the most sustainable results.

To learn more about ghost bottlenecks, watch the video below.