Here's how you can improve problem solving in Process Design with data analysis and interpretation.

One thing I've found helpful is to identify relevant stakeholders who are a part of the process and try to identify the bottlenecks they will be facing based on their task/input/expectations etc. A good way to gather information is survey, which can get you qualitative data in the form of text comments and quantitative data in the form of scale-based questions. Identify the common factors in the issues coming from various stakeholders and create a research framework which identifies the relationship of these factors with each other. Having a condensed framework will greatly help in structuring the rest of the analysis plan and also helps in flexibility (you can adapt your framework based on additional insights and feedback)

Considering my experience of one year and a half working with data analysis, my perspective is that effective problem-solving in Process Design starts with high-quality data collection. Accurate, comprehensive data is crucial, and automated tools ensure consistency. Cleaning and standardizing data before analysis guarantee reliability. Analyzing data helps identify patterns and trends, while visualization makes insights intuitive. Advanced techniques can be applied when necessary. Interpreting results within the business context reveals their impact. Based on findings, clear recommendations and an implementation plan should be developed. Continuous monitoring ensures improvements, and iterating with new data fosters ongoing enhancement.

When you incorporate gathering tools such as surveys (the least expensive and often weighted least amongst other techniques) it’s crucial to ensure your questions are clear and as unbiased to outcomes as possible. A well written survey question AND answers are chosen (e.g., multiple choice, scale of yes to no, or comment, and length of survey) can improve not only the quality of the your data, but also the percentage of completed surveys. I like to have more than one data gathering technique to balance, validate, or provide realistic assumptions about the data. For instance, if a survey with a return rate of 40% says X, but a three days of interviews provides XY with 90% participation. Better choices if funds are available to do so.

Somethings that have worked for me is to look at a process holistically. Look at the data, the turnaround times, the number of process steps , the number of handovers, the number of stakeholders. Typically higher numbers mean a process may be more complex than it needs to be. Then using design thinking, review the process with multiple stakeholders. Any process analysis should be a combination of data review + people experience review.

En algunas empresas se consumen datos de proveedores para construir reportes robustos, analizar la información y tomar buenas decisiones. A veces dichos proveedores cambian el formato de los datos que comparten o se retrasan en dejar la información. Entonces, es fundamental tener un canal abierto de comunicación y coordinación para que los datos lleguen con el mismo formato y de esa forma poder autorizar el flujo de carga de datos. Asimismo, es importante tener mapeado un contingente por si falla la primera fuente de datos.

To unlock the full potential of your data, choosing the right analysis tools is crucial. Start with familiar tools like spreadsheets for basic insights, but don't hesitate to explore advanced statistical software for deeper analysis. Visual tools such as graphs and flowcharts are indispensable; they transform complex data into clear visuals, making trends and bottlenecks easier to spot. By mastering these tools, you can dissect large data sets and extract actionable insights, leading to more informed decisions and effective problem-solving in your process design.

I want to highlight the potential of spreadsheets, often underestimated, but when the amount of data is not gigantic it is an easy tool to use, and easy to share with peers for review. More advanced tools such as R or Python are absolutely essential but require a deeper knowledge of both software. There are many data analysis tools, and they are all good, the important thing is to understand how to use them and to remember that the use of graphs that simply visualize the main trends and relationships is crucial to show and explain to stakeholders the main findings of the analysis.

In my experience, analysis tools (e.g. Celonis) are vital in process design for identifying inefficiencies. These tools can give you features like: Event-log : to provide a detailed, real-time view of processes, revealing hidden bottlenecks (like a digital X-ray of your process). Conformance checking: essential to ensure that processes adhere to standards and identify deviations. Process Variant Comparison: Compares different versions of a process to determine which is most efficient, aiding in standardization. ..and many more. These features enable accurate diagnosis and continuous optimization, making process designing more effective and empowering!

Additionally, use tools that will translate your results effectively to your audience who is requesting the analysis. If those seeking this information do not fully understand what you’ve provided, they’re less than informed actions on that data set could be detrimental to the best overall results. It’s important to understand how your audience prefers to receive information and if possible how it will be represented, while maintaining the integrity of your research product.

Com a criação dessas ferramentas consegue-se alcançar resultados com clareza e precisão. Esses métodos de padronização de processos permitem interagir e pôr em prática ações corretivas, metas com objetividade, determinar e cumprir metas estimadas sendo assim uma delas exemplo; Ferramenta crucial um "Gêmeo digital de objetivo único".

Interpreting data is where the real magic of problem-solving begins. Don’t just focus on the numbers—consider what they mean for your process. Are there unexpected spikes in process times? Are certain errors recurring frequently? This phase demands critical thinking to translate data into actionable improvements. It's about understanding why the data shows what it does and how you can leverage that insight. By digging deeper, you can uncover the underlying issues and devise strategies to enhance your process design, making it more efficient and effective.

Considering my 1-and-a-half-year experience working with data analysis, I believe that looking beyond the numbers and asking what they mean for your process is crucial. Are there unexpected spikes in process times? Do certain errors occur repeatedly? This step requires critical thinking to translate data into practical process improvements. It's not just about WHAT the data shows, but WHY it shows it and HOW you can use that knowledge to your advantage. Understanding the context and implications is key to driving meaningful changes.

Poder ver mis allá de los datos y comprender realmente el problema llevan a una mejora de procesos inminente. De nada sirve tener los datos correctos si no se tiene la visión global del problema y de todos los pequeños factores que no son tomados en cuenta al momento de la evaluación. A veces un paso menospreciado es el que realmente necesita ser modificado.

Interpreting data opens up a world of possibilities. It is not just about looking at numbers or results and their constant changes, it is also about analyzing trends. For example, how often can the results vary? Is the variance positive or negative? How big is the variance?. Among many other things, which although they may seem small, help us enormously when making a long or short term plan to reach the correct and desired goal.

With my experience as a process analyst, I can try and give hypothesis testing roadmap with examples: Formulate Hypotheses: “Implementing automated inventory management will reduce stockouts and lower inventory costs.” Design Experiments: Set up an A/B test comparing automated vs. manual systems for accuracy and cost-effectiveness. Collect Data: Track metrics like stockout incidents, inventory costs, and order fulfillment times. Analyze Results: Use statistical tests to check if the new system significantly improves outcomes. Refine Hypotheses: Adjust strategies based on test results and retest as needed. Implement Changes: If successful, apply the new system across departments for broader impact.

Experimentar é uma parte super importante do trabalho, especialmente quando estamos focados no resultado final de um produto ou serviço. Isso nos ajuda a dar vida às ideias e, ao pensar de forma holística surgem soluções que realmente atendem ao público.

To enhance hypothesis testing, clearly define specific, measurable hypotheses based on your data insights—design controlled experiments or trials to test these hypotheses, managing variables to isolate the effects of changes. Use statistical techniques like significance testing and confidence intervals to analyze results and determine if changes are significant. Document outcomes, refine your hypotheses, and iterate based on findings to improve solution effectiveness. Additionally, seek feedback from peers or experts to gain diverse perspectives and identify potential biases. This structured approach ensures that hypotheses are validated with robust evidence, leading to more informed and effective problem-solving strategies.

Considering my 1 year and a half experience working with data analysis, I believe that it should be an ongoing process, just like implementing improvements. Use the insights gained to make changes, measure the results, and adjust accordingly. This cycle of analysis, interpretation, and application fosters a culture of continuous improvement. By integrating this into your process design routine, you ensure that problem-solving is proactive and perpetually evolving.

In my experience as a data analyst, the first and most important thing to consider when data gathering is the objective or goal of what you are doing. The objective determines or greatly affects how you gather, interpret, and analyze the data.

To boost continuous improvement, establishing a feedback loop where data insights inform decisions and adjustments can be effective. Implementing real-time monitoring of key performance indicators (KPIs) and updating analytical methods with new technologies helps maintain relevance. Encouraging a culture of learning and idea-sharing among team members can further enhance process improvements. Integrating these practices can drive innovation and maintain a competitive edge by continually improving efficiency and effectiveness.

It is key to regularly review processes and iterate for improvements. Methodologies like Lean, Six Sigma, or Agile can support change initiatives over time by embedding them into organizational practices and culture.

One thing I’ve found helpful is to remember that I am only in control of gathering, analyzing, formulating and presenting the data to the requester. The requester is ultimately responsible whether or not your analysis is implemented or in what way it may be implemented. It’s important to remember where your responsibility to your part of work begins and ends. It has helped me better frame my purpose and overall contribution to a project and improves my own workload metrics.

Feedback will allow you to detect positive or negative sentiments to the changes implemented as a result of you initial analysis. Gaining insights as they come into on the newly implemented process will determine if you are addressing the cause or if further work needs to be done.

Objetivos e metas: Entenda claramente o propósito do processo e quais são os resultados esperados. Isso ajuda a direcionar o design para alcançar esses objetivos de forma eficaz. Tempo e Prazos: Estabeleça cronogramas realistas para cada etapa do processo, considerando tempos de ciclo, prazos de entrega e possíveis pontos de atraso. Responsabilidades e Funções: Atribua claramente responsabilidades a indivíduos ou equipes para cada etapa do processo. Isso ajuda a garantir que todos saibam o que é esperado deles e minimiza falhas de comunicação.