How Data Science and AI Complement Lean Six Sigma

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Lean Six Sigma continues to remain relevant as a way to improve business process capabilities. Artificial Intelligence and data analytics go hand in hand with Lean Six Sigma in terms of searching for truth in data to improve processes. 

Lean Six Sigma practitioners and Data Scientists are able to collaborate because their skillsets fit so well together. Machine learning and data science help to process the large amounts of data Lean Six Sigma practitioners require to perform their roles. 

Data analytics can enhance Lean Six Sigma tools and Lean Six Sigma helps to identify areas for applying data analytics. When applying them together, it is possible for businesses to become more efficient and competitive.

Lean Six Sigma is very beneficial for processes that manage, collect, and analyze large data sets. Following Lean Six Sigma allows businesses to define metrics to measure, analyze, improve, and control processes. The end goal is to improve quality, meet deadlines and reduce errors. 

Value Stream Mapping is a Lean tool used to visually map out a process in detail. Data analytics can help with this. Creating this detailed picture of how a process works makes it possible to find out where errors occur and what steps can be eliminated as they do not add value to the final product.

When data analytics are coupled with Lean Six Sigma, this helps to build businesses that can quickly meet changing market demands. It is only by gaining predictability that they can maintain the processes that achieve measurable results and meet customer demands. 

Artificial Intelligence (AI) replicates activities, like facial or speech recognition, that normally need human involvement. Businesses can offload routine tasks to AI to remain agile. AI can work in the background to streamline processes and make the most current information accessible. 

AI can streamline ongoing email communication and feedback. It can sift through data and discover patterns to enable better decision-making. It can lead to deeper insight into the voices of customers with the use of Natural Language Processing (NLP) which solves the problem of underutilized unstructured data.

Machine learning is teaching a computer to predict changes by monitoring past and current data. The extra insight provided by machine learning leads to increased productivity and reduces wear and tear, wastage and costs. One of the main advantages of this is to improve the efficiency of production processes. 

Data analytics is regarded as a priority in many businesses to stay competitive and to grow. They gain insights from data to innovate in many areas, such as managing inventory and managing supply chain to improving manufacturing operations and customer service. 

Best leveraging data and knowing what to analyze is where Lean Six Sigma can come into play. This will depend on having people trained in Lean Six Sigma who know how to apply the methodology. Six Sigma online courses offered by 6sigma.us consist of self-paced and virtual classroom training programs. 

The effectiveness of machine learning depends on the accuracy of data. Lean Six Sigma supports the accuracy of data collection because it reduces variance or a lack of consistency. It provides a way to review every process that is subject to variations. 

Using Lean Six Sigma techniques, it is possible to identify the causes of variations and prioritize the best way to remove them. The use of machine learning and AI has increased the need to effectively analyze processes and Lean Six Sigma can provide this.