The Future of Six Sigma — Machine Learning Is Redefining Industrial Precision
By: Hedi Hunt
Tags: Efficiency, Machine Learning, Six Sigma, Data Science, Manufacturing

Six Sigma is a set of methods for improving business process capabilities. Following these methods allows companies to define metrics to measure, analyse, improve, and control processes, resulting in increased efficiency.

Machine learning has become a major component of Six Sigma techniques. Companies are harnessing the power of artificial intelligence (AI) to analyse big data and refine their operations.

The use of machine learning may redefine industrial precision when aided by Six Sigma practices. Find out how these two concepts work together to produce more effective results.

What Is Machine Learning?

Machine learning is defined as the process of teaching a computer to predict changes to data by monitoring past and current data. It is one of many examples of how artificial intelligence (AI) is revolutionising the manufacturing industry.

Before the use of machine learning, humans had to manually input data into computer systems. This technology allows for the automatic collection of data from various sensors and equipment outputs. The data is then used by AI programs to make predictions of future performance or output.

The uses of machine learning are broad. Companies have used this technology to enhance preventative maintenance, predict variations, and monitor changes in performance based on multiple variables.

Some of the ways that machine learning is already improving smart manufacturing processes include:

Improving the efficiency of processes

Assisting product development

Improving supply chain forecasting

Increased defect detection rates

The extra insight provided by machine learning technology can lead to increased productivity while reducing waste, wear and tear, and overall costs.

Requiring humans to perform the same tasks leads to decreased efficiency and an increased risk of errors. The average computer can handle about two billion calculations per second, making machine learning a vital part of the future of manufacturing.

Improving the Efficiency of Processes

One of the main advantages of machine learning is the ability to improve the efficiency of manufacturing processes. Using data science in manufacturing helps engineers detect bottlenecks that may increase the timeframe for production runs.

Along with detecting bottlenecks, machine learning helps detect issues that may decrease the efficiency of operations. For example, the technology may help uncover unprofitable production lines, equipment malfunctions, and redundant processes.

Assisting Product Development

Machine learning gives engineers more information for improving the development of products. Based on the predictions of machine learning technology, engineers may find ways to yield better results.

For example, along with collecting data from manufacturing equipment, the software may obtain consumer data or market data. This information helps companies analyse the potential profitability of a product before investing in its production.

Improving Supply Chain Forecasting

Most manufacturing processes involve multiple stages. Materials need to be delivered to and from the manufacturing facilities. When a production run takes longer than expected or a vendor changes their order, the manufacturer may be left with unused materials or products. In some cases, errors in communication may result in shortages.

Machine learning helps prevent those problems. Manufacturers can use AI to estimate the needs of the supply chain more accurately, reducing shortages and overages. In fact, machine learning may reduce supply chain forecasting errors by up to 50% and reduce lost sales by 65%.

Increased Defect Detection Rates

Machine learning is increasingly used for quality control. By automatically monitoring quality testing, using machine learning can increase defect detection rates by up to 90% while promising productivity increase in quality control of up to 50%.

The benefits discussed depend on the accuracy of the data used by machine learning technologies. This is where Six Sigma comes in.

What Is Six Sigma?

Six Sigma is a set of methods used to improve processes. It was introduced at Motorola in 1986 by American engineer Bill Smith. In 1995, it became a core part of General Electric’s business strategy.

The overall goal of the Six Sigma strategy is to reduce defects and increase efficiency. The methods used aim to improve the quality of a process by identifying and removing defects or variances. It relies heavily on a combination of statistical analysis and empirical evidence.

There are two primary methodologies used for implementing Six Sigma — DMAIC and DMADV. DMAIC is used when improving an existing process while DMADV is used for creating new processes.

The DMAIC methodology includes the following five phases:

Define Measure Analyse Improve Control

The DMADV methodology replaces the “improve” and “control” phases with “design” and “verify.” Manufacturers looking to improve existing processes often use the DMAIC methods, which start with a definition of the system and the goals of the project.

After defining the project, technology is used to collect and measure relevant data. Machine learning is employed for the third phase — analyse. The machine learning software analyses the data to help detect the causes of defects.

Engineers can then use the results of the machine learning software to improve and control future manufacturing processes.

How Does Six Sigma Support Machine Learning?

The effectiveness of machine learning depends on the accuracy of the data. If you supply incomplete or incorrect information to AI systems, the systems are likely to produce unpredictable results.

Six Sigma supports the accuracy of data collection by reducing variance. Variation creates waste and errors, resulting in a continual cycle of decreased efficiency and additional errors.

Variation is typically defined as a lack of consistency. Consistency is something that manufacturers strive for. Inconsistency can result in reduced quality control, setbacks, and decreased productivity.

Variations decrease reliability and drive up costs. Most variations occur due to changes in processes, environment changes, equipment wear and tear, and measurement mistakes.

Six Sigma provides a way to review every process that is vulnerable to variations. By implementing Six Sigma techniques, you can identify the causes of the variance and find ways to eliminate or account for the variance. This gives you more accurate data for your AI systems to analyse.

Last Thoughts on Six Sigma and Machine Learning

Six Sigma is not a new concept. It was developed decades ago to improve manufacturing processes. However, the use of machine learning and AI has increased the need for an effective way to analyse processes.

Using the standard Six Sigma methodology, you can easily identify variations, analyse the cause, and then prioritise the best method for removing the variation.

The bottom line is that if you plan on implementing machine learning, you may also want to implement Six Sigma.