How can you identify the underlying causes of problems in your experiments?

2 Collect data

The next step is to collect data that can help you understand the problem and its causes. You need to decide what data to collect, how to collect it, how much to collect, and how to store and organize it. You can use tools like data collection plans, check sheets, histograms, and run charts to plan and execute your data collection process and visualize your data. You should also ensure that your data is reliable, valid, and representative of the problem.

4 Generate solutions

The fourth step is to generate possible solutions that can address the root causes of the problem and achieve the desired outcomes. You can use tools like brainstorming, affinity diagrams, multi-voting, and matrix diagrams to generate and prioritize ideas and select the best ones for testing. You should also consider the feasibility, effectiveness, and sustainability of the solutions and how they align with your goals and constraints.

5 Test solutions

The fifth step is to test your solutions and measure their impact on the problem. You can use tools like pilot studies, A/B testing, and design of experiments to plan and conduct your tests and compare the results with the baseline. You should also monitor the process and collect feedback from stakeholders and customers to evaluate the performance and satisfaction of the solutions.

6 Implement solutions

The final step is to implement your solutions and make them part of your standard work. You can use tools like action plans, PDCA cycles, and standard operating procedures to document and execute your implementation process and ensure that the solutions are followed and maintained. You should also review the results and benefits of the solutions and communicate them to your team and organization.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?