Como equilibrar a necessidade de análise de processos com a urgência e complexidade do problema?

Defining the problem you're trying to solve with process improvement is the most important step. It determines the quality of the output and the longevity of the solution. A few questions I always ask are: What is the "day-to-day" issue we are trying to solve for? What are the variables(or tools, system, or data) that we have at our disposal to make a change? What is the ideal outcome? What does the business need in order to complete its responsibilities intuitively?

Another way to look at this is "what is the minimum viable solution?" - similar to an MVP for a product design, this is the MVP for a process design.

Evaluating pain and identifying obvious or hidden bottlenecks is one way to get started, then comparing that to feasibility and other elements. In a smaller group or organization, priorities can sometimes be assigned relatively quickly just by interviewing key stakeholders. In a larger, more complex project, using scoring models and other tools provide a basis for decision-making. Yet even in a smaller firm, using those tools helps you provide proof and justification regarding the effort, creates a level of comfort and transparency which helps gain buy-in from key stakeholders, and is simply an important step in the planning process.

It's helpful to first identify the baseline process - the one that will cover the majority of all use cases - and flesh that out before solving for the edge cases.

Stakeholder involvement in process analysis isn't about consensus - it's about building a complete diagnostic picture. Your stakeholders aren't just sources of information; they're living sensors of process effectiveness. Their daily adaptations and workarounds aren't problems to fix - they're solutions in disguise. The key? Listen for patterns in their pain points. They've been beta-testing improvements long before you started analyzing.

Apply Agile principles by breaking down the problem into manageable parts and tackling them in iterative cycles. Each iteration should include a short analysis phase followed by rapid experimentation, allowing you to learn and adapt quickly. Incorporate feedback loops into your process, where each iteration provides insights that inform the next round of analysis and experimentation. This helps you continuously refine your approach and make informed decisions as the situation evolves.

Balancing process analysis with urgency and complexity involves prioritizing critical tasks, leveraging available data, and iterative improvements. In subsea engineering, urgent maintenance issues are analyzed promptly while complex problems undergo thorough analysis. Similarly, in renewable energy, urgent grid code compliance issues are addressed swiftly, with complex challenges requiring in-depth analysis. In manufacturing, immediate production issues are handled urgently, while complex problems are analyzed systematically to prevent recurrence.