You're looking to improve your R&D workflow. What's the best way to do it?

It is pivotal to assess R&D workflow time to time to ensure higher efficiency and adherence to goals set by stake holders. To improve existing workflow one must assess the current workflow, processes, collaboration and tools in use. This can help identify gaps leading to lower efficiency. One can implement modern tools, improve skills of team members and bring efficient, open team communications. Encourage continuous learning, provide a conducive environment for experimentation, and regularly reassess and adapt workflows based on feedback for ongoing optimization.

In my experience, knowing if you are in the Research or Development phase of a project is critical to success. The definitions are never perfect but broadly we can regard Development as carrying the rigour and structure of the end-product and being constrained by the available facility and product or service attributes. Research is new in concept and must incorporate innovation and organisational change that is driven by design and creative thought. Development is the refinement of process through iteration or parallel streams and is driven by efficiency. Research is the creation new services, products, or system knowledge and is reliant on the people within the organisation and the organisations openness to change.

1. Understanding the problem you are trying to solve in depth. 2. Reading the literature in depth (there will be lot of to and fro between 1 and 2). Once you are done finalizing the problems. Accessing the closest possible available solution if you can find in the literature. Summarizing everything in a sheet (excel, document, power point) and then seek guidance, suggestions or feedback from people and decide your experiment for proof of principle.

In my experience, there is no thumb rule for anything we do or plan to do .. workflows should be tailor made according to the needs and most suitable for the teams. E.g, a workflow working great for physical team doesn’t work well for virtual team.. even though they work with same projects and technologies. Any workflow should be simple enough to follow and detailed enough to not to miss out the micro activities. Most important part here would be implement a feedback culture. Without proper feedback, any team will fail miserably no matter how good or well designed is the work flow.

Some well-known routines for improvement are as follows: 1)Define, 2)measure, 3)analyze, 4)control and 5)improve Deming PDCA cycle, Plan-Do-Check(Study)-Act Very important thing for any improvement is "problem" itself. This is what you should take into account in the first place then start the "problem" worth solving. Quality problem solvers use some tools for this step such as "cost of quality" which well fit the managers' language to start with, or weighted Pareto diagram to find the priority of the problem to solve. So, even within the challenge of an R&D activity, it is very important look back and make sure the problem we are working on, worth continuing.

Any collaboration without complete clarity of roles and give n take is of no use. There must be clarity on collaboration processes, channels or tools for collaboration and also the timelines.

In my experience, you need to understand the requirements from the whole chain of command. From your direct manager who might require weekly updates, to their manager who could want a visual demo of current capabilities and finally to stakeholders who might only care about their requirements. Based on this you need to create a continuous evaluation & visualization dashboard or demo so you remove the overhead required for reporting and focus on actually improving the model and researching the best way to implement them.

Dans la mise en place d'un flux de travail R&D performant, les outils de communication, de suivi et de reporting sont prépondérants. Deux principes importants : 1/ ces outils doivent s'adapter à l'organisation et aux usages, mais jamais l'inverse. 2/ ces outils ne doivent générer aucune surcharge, sous peine de faire perdre du temps (et donc de la productivité) à vos collaborateurs. La solution consiste à tirer autant que possible les informations de communication/suivi/reporting/KPI des outils métier, nativement. Un document qui sort en GED d'un circuit de signature marque par exemple la fin d'une activité. Mettre en place un flux de travail R&D performant consiste donc - entre autres - à trouver les moyens d'appliquer ces principes.

In order for anyone to be successful in R&D, that person has to be willing to collaborate with other individuals. Your colleagues may have a different perspective than you on the same project. Your colleague's point of view may unlock the key between success and/or failure of the research objective.

💫To improve your R&D workflow use the collaboration tools.Collaborative tools allow easier and more efficient communication and also bring more clarity to tasks and roles assigned. File and data sharing is also a major feature of the collaboration tools that helps you to complete tasks in a timely and efficient manner. 💫Few popular collaborative tools are Trello , ProofHub, Basecamp, Monday.com and Microsoft Teams. These tools also help you can be trusted to work independently without any supervision.

It is indeed beneficial to enhance the R&D workflow by automating repetitive tasks. However, it's crucial to recognize that in certain industries, particularly those with high-risk factors, standardised protocols are essential. Before deciding to implement automated tools, thorough consideration of diverse scenarios is necessary to mitigate potential risks and ensure the effectiveness of the automation process. Striking a balance between automation and human involvement is key, as excessive automation can lead to losing the human touch and flexibility, which are vital aspects of successful R&D.

Draw a clear line between repetitive and real R&D work. Also working out the difference between research(pathfinding) and developing that research into technology (proof of concept). Dividing the workflow in between these 3 key steps can significantly improve any R&D workflow.

Identify any repetitive and time-consuming tasks in your workflow. Integrate automation tools or scripts to handle these chores, allowing your team to devote important time to more complicated and strategic areas of the R&D process. Scripts, macros, bots, and APIs can be used to automate data gathering, analysis, visualization, report production, document preparation, and email sending. However, you must be careful not to over-automate your workflow, since this will result in the loss of the human touch and flexibility required for R&D.

To improve your R&D workflow, start by assessing your current processes using flowcharts, diagrams, and feedback from team members and stakeholders. This helps identify areas for improvement and set measurable goals. Next, adopt collaboration tools like project management, file sharing, and video conferencing to streamline communication and foster innovation. Automate repetitive tasks using scripts, macros, or APIs to save time and increase accuracy. Ensure not to over-automate and maintain the necessary human touch. These steps, combined with regular reassessment and stakeholder involvement, will enhance efficiency and innovation in your R&D workflow.

Automate R&D tasks using Python or R scripts for data collection and analysis. Employ web scraping tools like BeautifulSoup, Selenium, or APIs for data retrieval. Implement machine learning algorithms for predictive modeling. Utilize version control (e.g., Git) and CI/CD pipelines for code management and deployment. Leverage cloud services for scalable resources and set up monitoring tools with alert systems. Streamline collaboration through tools like Slack. Generate reports using LaTeX or Markdown. Ensure security measures in automation scripts. Regularly update and test processes for accuracy and reliability, enhancing overall efficiency and accelerating research and development.

DMAIC - proven to work for any improvement project. One of the best trainings I ever had … Lean Six Sigma Black Belt! That’s where I learned to apply DMAIC.

Make sure you have dedicated positions in your R&D team for Data Scientists. It's a little pricy position but the turnover is much higher when data is well structured. The gain from data integration is really useful and important to explain R&D findings.

Streamline data collection and analysis by combining many data sources. Use tools such as Tableau or bespoke APIs to ensure a consistent and complete view of pertinent data. This integration improves decision-making and speeds up the research process. You can also use tools like ETL (extract, transform, load), data pipelines, or data connectors to move, transform, and connect your data from different sources.

In an R&D project, we integrated dispersed data using databases and ETL tools, enhancing workflow. Data integration is about creating an accessible, usable data ecosystem. Deploying data pipelines and connectors automates this process, ensuring up-to-date data. This empowers teams to generate insights and make informed decisions. Remember, data integration is key to a successful R&D workflow.

In R&D,Data integration will help in combining data from different sources to provide a unified view of the data and easier access to it. Data integration is a must because it’s becoming increasingly complex for organizations to enable easy access to information. The biggest challenge of integration is combining different formats. This is also known as structured vs. unstructured data. Structured data is what’s stored in rows and columns in a relational database. All other data falls under the unstructured data.

Do not be afraid of tough feedback loops and include all stakeholders in those loops. Especially those that you would rather not include as they will bring a unique point of view that can help you find new insights.

Create regular feedback loops throughout the R&D process. Encourage teammates to communicate openly and provide constructive feedback. This iterative technique provides continual progress by enabling your team to adapt to changing requirements and adjust their work based on insights.

In my experience feedback is the most useful tool companies and managers can use to evaluate at any given time their efficiency, their strengths and weaknesses. Design feedback loops at several stages of an R&D workflow and give attention to details such as bottlenecks of processes, time consuming steps or any other concerns from stakeholders and employees.

Feedback is a pillar of scientific research. It is very important to keep track of your progress, your results and their short and long term effects.

When you have a mechanism to collect, analyze, and act on feedback, you create an environment where everyone is encouraged to share their ideas and insights, and where mistakes are seen as opportunities for learning and improvement. Implementing feedback loops is not just about using a new tool or technique. It’s about transforming the way you work and learn. It’s about creating a culture of continuous learning and improvement. And most importantly, it’s about empowering your team to achieve their best work.

In my opinion there must be some trial period of any process before actually implementing it for evaluations. Or at least keep the flexibility to change the process during implementation by some narrow margin

Enhancing your R&D process calls for an ongoing cycle of evaluation and development. Make use of instruments like audits, assessments, and reviews to gauge the productivity, efficacy, and degree of satisfaction in your process. Use metrics, benchmarks, and key performance indicators (KPIs) to assess how effectively your workflow conforms to objectives, industry best practices, or standards. Frequent assessments ensure that your R&D team not only achieves but surpasses its goals by paving the road for problem discovery and resolution, process improvement, and continual innovation. With this iterative process, a dynamic and flexible workflow is established that changes to meet the ever-evolving needs of the research and development env.

These are very idealised suggestions somewhat divorced from the reality of research careers. In general, and most certainly in the STEM disciplines, a researcher will slowly accumulate the skills sets appropriate for their immediate goals. Generically, the understanding of personal time management, and project planning and execution will be acquired through doing. Few will enjoy professional training until they are post-graduate and possibly not until they are post-doctoral. With a predominance of small and medium sized businesses it is unlikely that many researchers are able to enjoy the continuous professional development outside of global corporate entities. This presents a major impediment to enabling faster or more streamlined research

O bom e velho modelo de PDCA amplamente difundido pela norma ISO 9001, descreve que um projeto/rotina deve ser continuamente melhorada e revisada quando necessário. É fundamental que o fluxo de trabalho de todos envolvidos estejam claramente descritos e mapeados. De outra forma, sempre haverá falhas no processo de melhoria contínua, por exemplo

I believe that a good starting point is to ensure that project participants know the general objective, that each one has correctly defined functions and has a particular objective that contributes to the overall achievement. This allows us to evaluate individual and team progress, showing which areas need to be improved in communication, training or facilities to accomplish their tasks.

Beyond traditional strategies, consider the broader ecosystem affecting your R&D. 🌐 Engage with external communities and forums to stay ahead of emerging trends and technologies. Networking can open doors to collaborations and partnerships, enhancing your research capabilities. Also, prioritize sustainability and ethical considerations in your projects to align with global standards and consumer expectations. 🌱 Lastly, leverage big data and analytics for predictive insights, helping to steer your R&D efforts more effectively towards market needs. Embrace a holistic approach for a cutting-edge R&D workflow. 🚀

Establishing a continuous improvement plan, wherein the workflow is periodically reviewed, bottlenecks are identified, and solutions are proposed, can be very helpful. Also, training the work team in data management and productivity can be very beneficial, as this will provide the team with tools to help them optimize and prioritize their time and assigned tasks.

While I agree with most of the points here - in my experience the biggest challenge with working in R&D is not tying the activities back to Objectives and Key Results. Workflow should be designed such that it meets the required objectives and achieves key results. Workflow design can change depending on the objectives and key results, and should be streamlined for speed, efficiency or resource management depending on the situation. In time, R&D projects can be categorized into long/short term, budget etc. and each of these will have a specific workflow pattern that works best for it. For example, the nature of documentation or repetition of tasks necessary can be different and the workflow is aligned to achieve the best outcome or result.

Stop bothering your Software Engineers, they know what they're doing. You cant put R&D innovation on a Gantt Chart. Describe the problem, make sure there's a clear protocol for them to request resources, and go away.

Do not lose your own flow amidst all the tools and tasks. Resilience, mindfulness, and joy in your job are crucial for reliably and sustainably guiding and managing R&D projects with all their tasks. Be yourself!