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Project Management Tutorial

By knowledgehut ., 1. what is project management, 2. activity-based costing, 3. agile project management, 4. basic management skills, 5. basic quality tools, 6. benchmarking process, 7. cause and effect diagram, 8. change management process, 9. communication management, 10. communication blocker, 11. communication methods, 12. communication channels, 13. communication model, 14. conflict management, 15. critical path method (cpm), 16. critical chain method, 17. crisis management, 18. decision making process, 19. design of experiment, 20. effective communication skills, 21. effective presentation skills, 22. enterprise resource planning, 23. event chain methodology, 24. extreme project management, 25. gantt chart tool, 26. just-in-time (jit) manufacturing, 27. knowledge management, 28. leads, lags & float, 29. management best practices, 30. management styles, 31. management by objective (mbo), 32. monte carlo analysis, 33. motivation theories, 34. negotiation skills, 35. organization structures, 36. pert estimation technique, 37. prince2 project management methodology, 38. pareto chart tool, 39. powerful leadership skills, 40. process-based management, 41. procurement documents, 42. procurement management, 43. project activity diagram, 44. project charter, 45. project contract types, 46. project cost control, 47. project kick-off meeting, 48. project lessons learnt, 49. project management methodologies, 50. project management office, 51. project management processes, 52. project management tools, 53. project management triangle, 54. project manager goals, 55. project portfolio management, 56. project quality plan, 57. project records management, 58. project risk categories, 59. project risk management, 60. project scope definition, 61. project selection methods, 62. project success criteria, 63. project time management, 64. project management software, 65. project workforce management, 66. quality assurance and quality control, 67. raci chart tool, 68. rewards and recognition, 69. requirements collection, 70. resource levelling, 71. staffing management plan, 72. stakeholder management, 73. statement of work (sow), 74. stress management techniques, 75. structured brainstorming, 76. succession planning, 77. supply chain management, 78. team building program, 79. team motivation, 80. the balanced score card, 81. the halo effect, 82. the make or buy decision, 83. the rule of seven, 84. the virtual team, 85. total productive maintenance, 86. total quality management, 87. traditional project management, 88. work breakdown structure, design of experiment.

Design of Experiments (DOE) is also referred to as Designed Experiments or Experimental Design – are defined as the systematic procedure carried out under controlled conditions in order to discover an unknown effect, to test or establish a hypothesis, or to illustrate a known effect. It involves determining the relationship between input factors affecting a process and the output of that process. It helps to manage process inputs in order to optimize the output.

A simple example of DOE:

While doing interior design of a new house, the final effect of interior design will depend on various factors such as colour of walls, lights, floors, placements of various objects in the house, sizes and shapes of the objects and many more. Each of these factors will have an impact on the final outcome of interior decoration. While variation in each factor alone can impact, a variation in a combination of these factors at the same time also will impact the final outcome.

Hence it needs to be studied how each of these factors impact the final outcome, which are the critical factors impacting the most, which are the most important combination of these factors impacting the final outcome significantly.

The interior designer can plan and conduct some experiments. Get to know more about DOE with our PMP online training .

Basics of DOE

The method was coined by Sir Ronald A. Fisher in the 1920s and 1930s. Design of Experiment is a powerful data collection and analysis tool that can be used in a variety of experimental situations.

It allows manipulating multiple input factors and determining their effect on a desired output (response). By changing multiple inputs at the same time, DOE helps to identify important interactions that may be missed when experimenting with only one factor at a time. We can investigate all possible combinations (full factorial) or only a portion of the possible combinations (fractional factorial).

A well planned and executed experiment may provide a great deal of information about the effect on a response variable due to one or more factors. Many experiments involve holding certain factors constant and altering the levels of another variable. This "one factor at a time" (OFAT) approach to process knowledge is, however, inefficient when compared with changing multiple factor levels simultaneously.

A well-performed experiment may provide answers to the following such as:

What are the key factors in a process? (both controllable and uncontrollable)
At what settings would the process deliver acceptable performance?
What are the key, main and interaction effects in the process?
What settings would bring about less variation in the output?

A repetitive approach to gaining knowledge should be taken up, typically involving these consecutive steps:

A screening design that narrows the field of variables under assessment.
A “full factorial” design that studies the response of every combination of factors and factor levels, and an attempt to zero in on a region of values where the process is close to optimization.

A basic approach to a Design of Experiment

We need to follow the below steps in sequence for conducting a DOE.

Define the problem(s)
Determine objective(s)
Design experiments
Conduct experiments and collect data
Analyse data
Interpret results
Verify predicted results

DOE has been in use for many years in manufacturing industry. Below are some of the benefits/improvements we can expect from conducting DOEs:

reduce time to design/develop new products & processes
improve performance of existing processes
improve reliability and performance of products
achieve product & process robustness
evaluation of materials, design alternatives, setting component & system tolerances, etc.

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Design of Experiments for Project Managers

By: Megan Bell, MPM, PMP on April 14th, 2021

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Design of Experiments for Project Managers

Experiments are not just for scientists; they are in fact a tool project managers and engineers have used for years to better understand and refine processes. In the context of project management, an experiment is not in a secret lab with bubbling liquid in beakers; instead, the testing is done in a controlled manufacturing setting. In project management, the quality planning tool of setting up tests for a process is known as “Design of Experiments.”

Define design of experiments

The design of experiments (DOE) is a tool for simultaneously testing multiple factors in a process to observe the results. Credited to statistician Sir Ronald A. Fisher , DOE is often used in manufacturing settings in an attempt to zero in on a region of values where the process is close to optimization . At its core, Design of Experiments is a statistical model enabling simultaneous testing rather than iterative testing of single factors.

What you need to know about design of experiments for the PMP®

Project Managers use the Design of Experiment tool in the Quality Planning process to determine the factors of a process, the way to test those factors, and what impact each has on the overall deliverable. Project Managers and those preparing for the Project Management Professional (PMP®) certification need to know of the DOE regardless of the industry in which they work. PMP® exam questions about the design of experiments are testing your understanding of when the tool should be used, how to set up experiments, the types of questions the test will answer, and when to use it instead of other tools.

The experiment is more than the process is it also about the people. Project Managers using Design of Experiments in their Quality planning must have excellent communication skills. Knowing what to ask about the process (or product) being studied, knowing how to extract information from subject matter experts, and knowing how to share the results of the experiments are critical; communication is an important part of the planning and running of your experiment.

When are design of experiments used

When conducting an experiment that tests only one factor, the impact of factors upon each other is missed. Design of experiment enables the project manager to learn about what happens when factors interact, thus providing a more accurate evaluation of quality. Project managers can use the data from a well-designed experiment in their Quality planning.

Knowing when to use design of experiment is as important as knowing what it is. It is a powerful tool in the Quality planning process and can be used when seeking answers for questions such as:

What are the factors in a process that are controlled?
What are the factors in a process that cannot be controlled?
What are the settings for each factor?
How do the factors impact each other?
At what settings does the factors impact each other?
What are the types of interactions among factors?

In the planning stage, enough time needs to be included in the overall project timeline to plan, execute, evaluate, and document the Design of Experiments. The time put into the experiments can save time later in the project and better ensure the level of quality of the final outputs.

How and why is design of experiments used

Project Managers who excel in planning will be able to apply that skill to the running of a Design of Experiments for their project. Specific tasks must be conducted in a certain sequence to achieve statistically relevant results.

Note that the tool is called Design of Experiments, plural; a single experiment, even with multiple factors, will not provide enough data. One experiment may provide results which indicate a different problem to solve thus requiring the design of additional experiments.

A common example of the DOE tool is the baking a cake. With the diagram, you see different factors such as the equipment (oven) and ingredients. And within those factors, there are variables. For example, with an oven, is it conventional or convection? Is it powered by electricity or gas? Is the cooking rack in the bottom, middle, or top? In your design, you must capture the factors specifically so that, just as in any experiment, you can replicate them. If your cake burns on the bottom, is it the heating process (conventional / convection), is it the powering source (electrical or gas), and/or is it the placement of the racks (bottom, middle, top) within the oven? Or do all three aspects of the oven factor impact the interactions among other factors and your final result? You must accurately document each factor to better know how changes may shape the outcome.

In a manufacturing setting, the design of experiment should reflect all factors that work together in the process under study. Consider the equipment, the raw materials, the people, and the environment; each plays a role in the process and changes in some can impact all. The Design of Experiment provides a line of sight into a process so that levels of factors can be manipulated in a controlled manner to better manage the overall quality.

At the most basic level, the design of experiments, if conducted correctly, can result in reduced costs, reduced production time, and more reliable results. Benefits can include:

All PMP ® credential holders need to know about design of experiments

Although the design of experiments is associated with manufacturing, all project managers should know what it is, what it is used for, and what the potential benefits can be. It takes knowledge of the tool to determine when it will be beneficial to the project and company. Not only as part of preparing for the Project Management Professional (PMP®) certification exam, but also as a tool that can be used in the right situation. It also takes knowledge of the design of experiments tool in order to correctly answer the hypothetical and situational questions in the exam.

About Megan Bell, MPM, PMP

A multi-hyphenate of corporate training, higher education, and creative agency work, Bell’s passion for connecting people to impactful information fuels an evolving career journey. Her portfolio includes conducting learning analytic research and reporting, managing a corporate mentoring program, authoring a blog series, facilitating leadership and career programs, serving on a non-profit board, and even occasional voice work. Bell’s education background encompasses UNC-Chapel Hill, Western Carolina University, and North Carolina State University.

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