How do you leverage data and analytics to support Lean Six Sigma decision making and problem solving?

Defining the problem by mapping the process flow creates a framework for identifying friction points. It's important to look at a problem holistically and prioritize where you want to focus your efforts.

The alignment of Lean Six Sigma with data analytics has been always crucial. I believe that data is simply the language of clarity. It will clarify the Scope, VoC, VoP, Quantify Impact.

Defining problem seems easy as it first comes to the table, however once you start documenting it, the complexity starts. Defining problem and alignment with stakeholders and team on the same is very crucial part for project success. It involves clarifying the issue at hand, understanding its scope, and identifying its root causes. A well-defined problem statement is specific, measurable, achievable, relevant, and time-bound (SMART). It should address what, where, when, who, why, and how questions to provide a clear understanding of the situation. By accurately defining the problem, you lay the foundation for developing effective solutions and guiding subsequent problem-solving efforts.

Leveraging data analytics to support problem definition in Lean Six Sigma involves systematically collecting and analyzing relevant data to clearly identify and articulate the issue. This begins with gathering preliminary data to understand the problem's scope and impact, ensuring the problem statement is based on factual evidence. Analytics tools such as SIPOC diagrams and process maps visualize the process flow and highlight potential issues. Data visualization and statistical analysis pinpoint specific problem areas and quantify their impact, setting measurable objectives. This data-driven approach provides a solid foundation for defining the problem and facilitates stakeholder buy-in with a clear, evidence-based rationale.

Firstly, create a process map of all the activities required to convert raw materials into output (Y). Then, identify the Critical to Quality (CTQ) factors in the process. Process map helps to identify the inefficiencies or wastes in the process. This also helps to determine the critical steps to collect the data.

Keep in mind that selecting KPIs is not just about measurement but about understanding the story behind the numbers. It's important to choose indicators that truly reflect process efficiency and customer value.

In measure phase we are basically baselining the process performance which we wants to improve. Thus, the 1st step is to draw the As-Is Process and and identifying the KPIs which is important to the customer and we need to improve it. These KPIs are also called CTQs. Before collecting the CTQs data, team must agreed on the operational definitions of these CTQs and then prepare a data collection plan and check-list. Also, it is very important to assess the measurement system before collecting data to avoid GIGO effect (Garbage In, Garbage Out)

Os KPI’s selecionados devem servir de suporte para justificar mexidas de ponteiro maiores, geralmente vistas em OKR’s. é importante entender que toda mudan?a de processo n?o afeta só os números ligados diretamente a ele.

Sem a devida medi??o, n?o podemos avaliar o que está acontecendo, e, consequentemente, n?o obteremos resultados significativos

Data and analytics are essential tools for Lean Six Sigma practitioners to identify problems, measure performance, analyze root causes, implement solutions, and monitor results. First step of any Lean Six Sigma project is to define the problem that you want to solve. Data and analytics can help you define the problem by providing facts, evidence, and insights that describe the current situation and the gap between what is expected. You can use data and analytics to collect customer feedback, map the process flow, and validate the implementation. Further it can help you implement the solutions by providing frameworks, tools, and criteria that enable you to prioritize, optimize, and monitor data.