Data-Driven-Frameworks-in-RandD.pptx by Engr. Kennedy Rodriguez and Engr. Rico Jay Laxa

Data-Driven-Frameworks-in-RandD.pptx by Engr. Kennedy Rodriguez and Engr. Rico Jay Laxa

1. Data-Driven Framework in Research and Development by Engr. Kennedy V. Rodriguez, MECpE , PAFR Engr. Rico Jay S. Laxa , MSE-ECE, PAFR

2. A brief introduction to the topic and its relevance in today's business environment. The increasing availability of data in today's business environment has made it possible for organizations to leverage data-driven approaches to decision-making and planning in research and development A data-driven framework can provide valuable insights that can help organizations to optimize their R&D processes, identify opportunities for innovation, and gain a competitive edge. In this presentation, we will discuss the key concepts of a data-driven framework in research and development, its benefits and challenges, and best practices for its implementation. By the end of this presentation, you will have a clear understanding of how a data-driven approach can be used to drive innovation and success in R&D.

3. Data Definition of Data The Data Value Chain Data-Driven Framework Steps in Data-Driven Framework in R&D Importance of Data-Driven Framework in R&D Benefits Data-Driven VS Non Data-Driven Data-Driven Innovation Expectations in Data-Driven Framework in R&D Evolution of Data-Driven Process Paradigms Roles of Engineers in Data-Driven Frameworks The Future Towards Data-Driven Frameworks in R&D I. OUTLINE

4. DATA

5. Definition of Data “A gift from yesterday that you receive today to make tomorrow better.” – from the book Finish: Give Yourself the Gift of Done by Jon Acuff (2017)

6. Definition of Data There is no unanimous definition of the term “data” (Nupur Jalan & Jan Winterhalter , 2021). The definition of data varies from different disciplines.

7. Definition of Data Any observation collected in respect of any characteristic or event.

8. The Data Value Chain (Curry et. al, 2014)

9. DATA-DRIVEN FRAMEWORK

10. Data Driven Framework in Research and Development A Data Driven Framework in Research and Development refers to a systematic approach of collecting, analyzing, and interpreting data to guide decision-making and planning in research and development projects. It is a methodology that uses data to inform and optimize various aspects of the R&D process, including design, testing, evaluation, and improvement. A data driven framework involves the use of quantitative and qualitative data to identify patterns, trends, and insights that can guide the development of new products, services, or processes. The framework typically involves a series of steps, including data collection, analysis, interpretation, and reporting, as well as ongoing feedback and refinement.

11. The different steps involved in a data driven framework in research and development A data driven framework can help guide decision-making and planning in research and development projects by leveraging data to identify insights and inform strategic actions.

12. The importance of a Data Driven Framework in Research and Development A data-driven framework in research and development is essential for organizations that want to stay competitive and innovative in today's rapidly evolving business environment. By leveraging data to guide decision-making and planning, organizations can gain valuable insights and competitive advantages that can help them to succeed in the long run.

13. Benefits of Data Framework in Research and Development

14. Improved Descision -Making Improved decision-making is one of the key benefits of a data-driven framework in research and development. With access to a centralized repository of data and analytics, decision-makers can make informed decisions about R&D initiatives, project prioritization, and resource allocation. Data-driven insights can provide a more accurate and objective view of R&D processes and project performance. This information can help organizations to make informed decisions, prioritize initiatives, and allocate resources more effectively. By using data to inform decision-making, organizations can reduce the risk of costly mistakes and improve their chances of success.

15. Increased efficiency and Productivity Increased efficiency and productivity can help organizations to stay competitive and drive growth and profitability. By using data to optimize their R&D processes, organizations can maximize the impact of their investments and achieve better results.

16. Enhanced Accuracy and Precision Enhanced accuracy and precision can help organizations to achieve better results and reduce the risk of failure. By leveraging data-driven insights, organizations can make more informed decisions and optimize their R&D processes to achieve their goals more efficiently and effectively.

17. Strategies for collecting and analyzing data in a research and development setting

18. DATA-DRIVEN VS NON DATA-DRIVEN

19. Data-Driven VS Non Data-Driven In a data-driven approach, decisions are made based on analysis and interpretation of data. This means that data is collected, analyzed, and used to inform decision-making. Data-driven approaches rely heavily on statistics, data mining, and machine learning techniques to extract insights from large datasets.

20. Data-Driven VS Non Data-Driven In a non-data-driven approach, decisions are made based on intuition, experience, or other non-quantifiable factors. This approach may rely on expert opinion or qualitative research to make decisions. Non-data-driven approaches may be used when data is not available or when subjective factors such as human emotions and values are considered important.

21. Data-Driven VS Non Data-Driven Both approaches have their strengths and weaknesses. Data-driven approaches can provide objective and measurable results, but they may overlook important factors that are not captured in the data. Non-data-driven approaches may take into account important qualitative factors, but they may be subject to bias or lack objectivity.

22. Data-Driven VS Non Data-Driven

23. DATA-DRIVEN OPTIMIZATION

24. Definition of Data-Driven Optimization Data-driven optimization refers to the process of using data and advanced analytics techniques to optimize a system or process. This approach involves collecting and analyzing data to identify patterns and insights that can be used to improve the performance of a system or process.

25. Application of Data-Driven Optimization Data-driven optimization is commonly used in various industries, including manufacturing, logistics, transportation, and healthcare, to improve efficiency, reduce costs, and enhance quality. In manufacturing, for example, data-driven optimization can be used to optimize the production line by identifying bottlenecks and optimizing production schedules. In healthcare, data-driven optimization can be used to optimize patient care by identifying patterns in patient data and optimizing treatment plans.

26. The DDO process model (Izadi et al., 2020)

27. The DDO process model (Izadi et al., 2020) Collecting relevant data from various sources, including sensors, databases, and other systems.

28. The DDO process model (Izadi et al., 2020) Analyzing the data to identify patterns, trends, and insights using various analytics techniques, including machine learning, statistical modeling, and data visualization.

29. The DDO process model (Izadi et al., 2020) Using the insights from data analysis to optimize the system or process, which can involve changing the parameters or algorithms used in the system or process.

30. The DDO process model (Izadi et al., 2020) Testing the optimized system or process to ensure that it is working as expected and achieving the desired results.

31. DATA-DRIVEN INNOVATION

32. Definition of Data-Driven Innovation Innovation aims to create new products, services and systems, new demands, and new markets. The creativeness and newness that define innovation result in uncertainty in the process of pursuing innovation.

33. Definition of Data-Driven Innovation DDI is the process of innovating that draws information and inspiration from big data (of users, innovators, stakeholders, science and technologies, processes, environments, etc.).

34. The DDI process model ( Jianxi Luo, 2022)

35. The DDI process model ( Jianxi Luo, 2022) The opportunity space is the set of alternative opportunities where the innovators want to and can create value via designing new solutions, products, services, or systems to address human or societal needs.

36. The DDI process model ( Jianxi Luo, 2022) The design space is the set of design alternatives that the innovators come up with to address the specified opportunity.

37. Examples (Owen Hunnam, 2019)

38. Data Definition of Data The Data Value Chain Data-Driven Framework Steps in Data-Driven Framework in R&D Importance of Data-Driven Framework in R&D Benefits Data-Driven VS Non Data-Driven Data-Driven Innovation Expectations in Data-Driven Framework in R&D Evolution of Data-Driven Process Paradigms Roles of Engineers in Data-Driven Frameworks The Future Towards Data-Driven Frameworks in R&D I. OUTLINE

39. EXPECTATIONS IN DATA-DRIVEN FRAMEWORK IN R&D

40. Evolution of Data-Driven Process Paradigms

41. Evolution of Data-Driven Process Paradigms

42. Evolution of Innovation Process Paradigms

43. Roles of Engineer and Managers in Data-Driven Framework Create designs from available data Integrate DDI framework to the Engineering Design Process Collaboration between academe-based engineer researchers and industry-based engineer researchers

44. The Future Towards DDI Creative Artificial Intelligence The key enabler for DDI down the road Requires both machine learning and machine creation

45. The Future Towards Data-Driven Frameworks in R&D Creative Artificial Intelligence

46. The Future Towards Data-Driven Frameworks in R&D

47. The Future Towards Data-Driven Frameworks in R&D Data-driven innovation is still at the early stage of development and adoption (Cockburn et al., 2019).

48. THANK YOU!