The best approach to managing experimental science projects depends on factors such as project nature, uncertainty, and requirements. Considering the dynamic nature of experimental science, Agile, Iterative, or a Hybrid approach, combining flexibility with structured planning, are often considered effective. The choice should align with the project's need for adaptability, frequent adjustments, and evolving objectives. Regular review and adjustment of the chosen approach based on ongoing feedback enhance the project's chances of success. Saving Changes...
As uncertainty increases, highly predictive approaches are unlikely to work well. Adaptive or hybrid approaches recognize the need for frequent product & process feedback as well as the need to know quickly if the current direction is invalid.
Six Sigma is a natural fit. It has a large toolbox for testing and analyzing variables.
Define Measure Analyze Improve and Control (DMAIC) is really applied scientific method to test variables with statistical rigor. While many of its methods are oriented around process controls, developing and testing hypotheses and Design of Experiments (DoE) for example come straight out of the laboratory. It's very similar to the Deming cycle of Study Do Check Act, but much more data focused.
Senior Projects Manager | Field & Marten AssociatesNew Westminster, British Columbia, Canada
Nov 10, 2023 12:17 PM
Replying to Keith Novak
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Six Sigma is a natural fit. It has a large toolbox for testing and analyzing variables.
Define Measure Analyze Improve and Control (DMAIC) is really applied scientific method to test variables with statistical rigor. While many of its methods are oriented around process controls, developing and testing hypotheses and Design of Experiments (DoE) for example come straight out of the laboratory. It's very similar to the Deming cycle of Study Do Check Act, but much more data focused.
Senior Projects Manager | Field & Marten AssociatesNew Westminster, British Columbia, Canada
Your best bet for science related projects is to adopt an adaptive agile approach. Saving Changes...
Thomas WalentaGlobal Project Economy ExpertHackenheim, Germany
Everybody who has written a thesis has read a little bit about how to execute a scientific project. The research methods and philosophies (!) are manifold. Many types of experiments (surveys, interviews, field studies, ...) exist and require specific preparations. There is a vast and expanding body of knowledge about how to do research. If you do a PhD you learn about this for about 3 years and still only know a thin slice.
Just read an interesting paper about the role of serendipity in scientific research. This could be another concept for the agile community to pursue.
It did not come to my attention that project management concepts or frameworks are of relevance in this field. For example, iterations are not a feature but the nature of scientific work.
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1 reply by Keith Novak
Nov 11, 2023 1:02 PM
Keith Novak
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Unfortunately, the lack of attention to PM concepts is a fatal flaw to many science projects. It comes down to the old metaphor: Do you want to fish, or cut bait? (also referred to as "analysis paralysis").
Whether it is a semester class at university, writing a thesis, a research grant, or a professional scientist in private industry, at some point results are expected. In industry vs. academia, results are expected sooner than later.
In the most basic model of Hypothesis, Test, Analyze, Apply with iteration assumed as a given due to the uncertainty in science projects, far far too often people fail to budget their time and efforts become all test/analysis and no time for application. All the time is spent cutting bait and none used to catch fish.
By contrast, I estimate how long it takes to perform an iteration, how many iterations will lead me to the serendipitous finding, and how long it takes to apply that new knowledge and claim success. Just like with other projects, I can fast track the schedule by identifying dependent vs. independent variables so I can run some tests in parallel vs. series, trying 2 types of bait at once hoping one attracts the fish.
Everybody who has written a thesis has read a little bit about how to execute a scientific project. The research methods and philosophies (!) are manifold. Many types of experiments (surveys, interviews, field studies, ...) exist and require specific preparations. There is a vast and expanding body of knowledge about how to do research. If you do a PhD you learn about this for about 3 years and still only know a thin slice.
Just read an interesting paper about the role of serendipity in scientific research. This could be another concept for the agile community to pursue.
It did not come to my attention that project management concepts or frameworks are of relevance in this field. For example, iterations are not a feature but the nature of scientific work.
Unfortunately, the lack of attention to PM concepts is a fatal flaw to many science projects. It comes down to the old metaphor: Do you want to fish, or cut bait? (also referred to as "analysis paralysis").
Whether it is a semester class at university, writing a thesis, a research grant, or a professional scientist in private industry, at some point results are expected. In industry vs. academia, results are expected sooner than later.
In the most basic model of Hypothesis, Test, Analyze, Apply with iteration assumed as a given due to the uncertainty in science projects, far far too often people fail to budget their time and efforts become all test/analysis and no time for application. All the time is spent cutting bait and none used to catch fish.
By contrast, I estimate how long it takes to perform an iteration, how many iterations will lead me to the serendipitous finding, and how long it takes to apply that new knowledge and claim success. Just like with other projects, I can fast track the schedule by identifying dependent vs. independent variables so I can run some tests in parallel vs. series, trying 2 types of bait at once hoping one attracts the fish.
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1 reply by Thomas Walenta
Nov 12, 2023 5:16 AM
Thomas Walenta
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Agree Keith.
Anything can benefit from other perspectives and capabilities.
I have supervised 30+ thesis projects for MBA, on PM topics, and a duration of less than 6 months. NONE of them had a comprehensive plan, though you may guess I demanded it. All of them finished in time, most with good results, and a few with excellent results.
The need for serendipity was circumvented by preparing the thesis projects well, selecting a topic, area of research, and methods, and having regular checkpoints.
As the project is run by the student, for the student, and with the student alone, team-oriented concepts (like agile) are less relevant.
As for a PhD thesis, the duration is 3 years, and there are many plan templates available, which reflect the wide range of methods. Here, stakeholder management is more important, as you will work with your doctoral mentors, faculty, and other students.
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Thomas WalentaGlobal Project Economy ExpertHackenheim, Germany
Nov 11, 2023 1:02 PM
Replying to Keith Novak
...
Unfortunately, the lack of attention to PM concepts is a fatal flaw to many science projects. It comes down to the old metaphor: Do you want to fish, or cut bait? (also referred to as "analysis paralysis").
Whether it is a semester class at university, writing a thesis, a research grant, or a professional scientist in private industry, at some point results are expected. In industry vs. academia, results are expected sooner than later.
In the most basic model of Hypothesis, Test, Analyze, Apply with iteration assumed as a given due to the uncertainty in science projects, far far too often people fail to budget their time and efforts become all test/analysis and no time for application. All the time is spent cutting bait and none used to catch fish.
By contrast, I estimate how long it takes to perform an iteration, how many iterations will lead me to the serendipitous finding, and how long it takes to apply that new knowledge and claim success. Just like with other projects, I can fast track the schedule by identifying dependent vs. independent variables so I can run some tests in parallel vs. series, trying 2 types of bait at once hoping one attracts the fish.
Agree Keith.
Anything can benefit from other perspectives and capabilities.
I have supervised 30+ thesis projects for MBA, on PM topics, and a duration of less than 6 months. NONE of them had a comprehensive plan, though you may guess I demanded it. All of them finished in time, most with good results, and a few with excellent results.
The need for serendipity was circumvented by preparing the thesis projects well, selecting a topic, area of research, and methods, and having regular checkpoints.
As the project is run by the student, for the student, and with the student alone, team-oriented concepts (like agile) are less relevant.
As for a PhD thesis, the duration is 3 years, and there are many plan templates available, which reflect the wide range of methods. Here, stakeholder management is more important, as you will work with your doctoral mentors, faculty, and other students. Saving Changes...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
Trying to add something to great comments above Agile based approach using a iterative-incremental life cycle could help. Saving Changes...
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