Como você planeja a coleta de dados para um projeto Seis Sigma?

O primeiro passo é definir claramente o objetivo do projeto e identificar quais variáveis impactam diretamente o problema a ser resolvido. Em seguida, desenvolvo um plano de coleta de dados estruturado, que pode incluir métodos como amostragem, observação direta ou o uso de sistemas automatizados de medição. É essencial garantir que os dados sejam precisos e relevantes, evitando vieses e inconsistências. Para isso, defino critérios de amostragem adequados e asseguro que os instrumentos de medição estejam calibrados. Durante a fase de coleta, treino a equipe para que siga os procedimentos estabelecidos de forma padronizada, garantindo a integridade dos dados.

Simply put, your project must address a pressing need. It must be linked to the company's immediate and longer term goals and must be tangible enough for its success to be identified and measured. Tools like 6Ws2H can help define the problem statement after preliminary data has been gathered and a link to company goals is assured.

I encourage you to always ask the question "What am I trying to learn?". This will help ensure the data you take is actionable.

If you want to plan for data collection, then first step is to - Define the problem -Assess the current state and improvements needed - Select methods of data collection - Using certain tools or surveys. - Create Plan for the data collection - Ensure data collection is consistent among teams - If required , you can pilot the approach before rolling it out. - Implement and monitor the plan

É crucial para qualquer projeto seis sigma criar um plano de coleta de dados que esteja atrelado ao escopo do projeto, costumo usar uma tabela onde ela é formada pelos seguintes itens: - Indicador - Variável a ser validada - Definição operacional - Como deve ser feita a coleta desse indicador e limites dele (inicio e fim) - Fonte de Dados - De onde serão extraídos os dados - Tamanho da Amostra - Quantidade de dados analisados - Responsável - Quem conduzirá a coleta - Quando - Período de coleta - Como - Método e Equipamento - Outros dados que devem ser coletados - comentários adicionais Essa tabela é crucial para gestão da informação do projeto e garante o alinhamento de conceitos e conexão com o escopo e objetivo do projeto.

Determine what you need to address the problem in order to obtain the appropriate data. Next, arrange to locate and gather sufficient data to identify a trend. To see the story in your data, use charts.

In a DMAIC project, data collection serves as the foundation for understanding current process performance and identifying areas for improvement. It’s about gathering accurate, relevant information to establish a baseline, measure against objectives, and track the efficacy of changes. This systematic approach ensures that decisions are made based on empirical evidence rather than assumptions, leading to more effective problem-solving and sustainable process enhancements. By defining clear data collection goals, teams can focus their efforts on obtaining insights that directly contribute to the project’s success, ultimately driving quality and efficiency in organizational processes.

Any problem that does not involve the customers needs, or is not related to solving the customers issues, should not be the main purpose of a project. The purpose of a goal must be defined specifically and, it should be solved by building upon a solid foundation of processes.

It's so important to establish a solid baseline if you want to accurately realise any improvements post-initiative. Remember that the steps you take here are setting up a solid foundation for the next phase, analyse. You want to make sure you are measuring the right things and measuring them the right way. Gage R&R can help you here but I have personally found better results using Control Charts as they usually either already exist or are inexpensive to generate. Either way, tools like these will help you know if the data you are measuring is reliable.

A capability study is also a great way to measure the current state. The main advantage of a capability study is that it allows you to compare the current state to the voice of the customer specifications.

It is said that " What we cannot measure, we cannot understand". In order to clearly measure the data we want to understand, process mapping, Value stream mapping are some methodologies which are really important and can be very helpful. Measuring the data is one crucial part, and visualizing that data using the right methods such as process control charts, histograms, etc.. is also important.

O desafio nessa etapa é conhecer bem o problema que será resolvido e que está definido no contrato do projeto para saber o grau de profundidade e quais as análises estatísticas que serão realizadas de acordo com a quantidade e qualidade dos dados disponíveis.

O primeiro objetivo é você entender bem esse projeto e conhecer ele. Logo em seguida você precisa de ferramentas estatísticas (saber qual usar vai ajudar muito) depois de realizado, faz um comparativo de antes X depois.

In the DMAIC framework, the crux of the project unfolds as data collected is meticulously examined to reveal underlying issues. This phase employs statistical tools and techniques like ANOVA, Hypothesis testing, Ishikawa diagram etc to sift through data, seeking patterns and anomalies that point to root causes. The insights gleaned are then distilled into a coherent narrative, often visualized through charts or graphs, to convey the story the data tells. Presenting these insights effectively is crucial; it transforms raw data into actionable intelligence, enabling teams to make informed decisions on improvements.

While Analyzing data, we tend to find answers for the questions which we do not define as the purpose of the analysis. Hence its important to not deviate from the purpose of the Analysis whilst retaining extra insights for future purposes.

It is okay if your initial improvement idea didn't work out as planned. Just adjust and try another improvement. Do not be afraid to give up on something that did not work. It is a mistake to continue to move forward with changes that will not improve the product or process.

This is the meat of a Six Sigma Project - the actual implementation to drive improvement. Trust your findings and numbers because sometimes your resolutions don't seem to solve the problem. It could be that there is a further layer of root cause to unravel, so don't be afraid to go back a few steps to analyze and then update your actions. If done thoroughly and without bias, you will deliver the improvement you seek!

After analyzing the collected data in a DMAIC project, the Improve phase is where creative problem-solving meets data-driven decision-making. This stage is about developing, testing, and implementing solutions that address the root causes identified in the Analyze phase. Teams brainstorm potential improvements, prioritize them based on impact and feasibility, and often use pilot studies to test their effectiveness. The goal is to refine processes, eliminate waste, and enhance quality, ensuring that the changes lead to measurable improvements.

Improving processes could either mean simplifying an existing process, by eliminating waste such as extra wait times, re work and so on. Or it can also lead to new modified processes or services for customers. It is important to monitor the before and after observations and understand what worked and did not work.

1. Define the Problem or Opportunity 2. Identify Key Metrics 3. Select Data Collection Methods a. Direct observation b. Surveys/questionnaires c. Existing databases and records d. Sensor data e. Sampling 4. Develop Data Collection Plan a. What data will be collected b. How data will be collected c. Who will collect the data d. When data will be collected e. Where data will be collected 5. Ensure Data Accuracy and Precision 6. Consider Sample Size and Sampling Methods 7. Establish Data Collection Protocols 8. Pilot Test the Data Collection Process 9. Document Data Collection Procedures 10. Implement Data Collection 11. Validate and Clean Data

The following steps can be used to create a data collection plan for a Six Sigma project: Identify the questions that need to be answered. Determine the kind of data that is available. Determine how much data is needed. Determine how to measure the data. Decide who is going to gather data. Determine where the data will be collected from. Decide whether to measure a sample or the whole population. Plan the data collection schedule.

1. Define the project goals and objectives clearly. 2. Identify the critical processes and variables related to the project. 3. Determine the type of data needed (qualitative or quantitative). 4. Design data collection methods, such as surveys, interviews, or process observation. 5. Develop data collection tools, such as questionnaires or checklists. 6. Pilot test the data collection process to identify and address any issues. 7. Implement the data collection plan, ensuring data is collected systematically. 8. Monitor data collection progress and address any challenges or deviations. 9.Validate the collected data to ensure accuracy and reliability before proceeding with analysis

Acredito que inicialmente antes de fazer o DEMAIC podemos fazer o Genba e descobrir os princípios que estão ocasionando desconexões e até mesmo desperdícios, assim podemos mensurar do que se trata e qual o foco de melhoria.

Planning data collection for a Six Sigma project is crucial for obtaining accurate and meaningful insights. Begin by defining the project's goals, objectives, and key metrics. Identify relevant data sources and determine the data types required. Develop a data collection plan outlining the sampling approach, data collection methods, and frequency. Ensure data accuracy and consistency by establishing data validation checks. Involve key stakeholders to gather insights into the process. Pilot the data collection plan to identify and address potential challenges. Continuously refine the plan based on feedback and emerging requirements.

Throughout my journey as an OpEx/CI Transformation Leader and Quality Engineering Lecturer, I’ve emphasized the problem framework Y = f(Xs), where Y (output) is the project metric tied to customer needs (VOC), and Xs (inputs) are the process metrics we aim to improve. Here are some lessons learned: Combine Y and Xs metrics in your data collection plan to test cause-effect relationships. Educate your team on data types (continuous/variable and discrete/attribute) to select appropriate tools. Ensure data reliability by planning your data collection properly and conducting quick Gage R&R (Gauge Repeatability and Reproducibility) studies. Let’s continue driving excellence together!