Ah ha! You are going to reopen the in-person office for the PMO.
Buy them at any online book retailer!
... AMBITION, [which] captures an individual’s desire to... achieve their goals.
... ANTICIPATION, [which] is ... looking into the future, knowing that your decisions today will bear their consequences in the future. ...
... REFRAMING, [which] is .... intentionally adopting new points of view and explanations. ...
To this I might add:
Good or bad; fix or ignore?
So is complicated or complex a good thing or bad? How would you know? And, what's to be done about them?
Short answer: Chaos is almost always bad in a system, product, or process -- whatever is the project's outcome. Thus, for that reason alone, complexity may not be your friend.
But, even without chaotic propensity, complexity is usually not a good thing: complex systems are hard to service; hard to modify; difficult to integrate with an established environment; on and on. If such are present, then complexity is present -- that's how you know.
Complicated is usually a matter of cost: lots of parts begets lots of cost, even if there is minimal complexity. Simple is usually less costly and may not necessarily sacrifice other attributes.
So, what do you do?
You've read the theory; now, to action:
I would hardly think today of making my first flight on a strange machine in a 27-mile wind . . .I look with amazement upon our audacity in attempting flights with a new and untried machine under such circumstances.
Yet faith in our calculations and the design of the first machine, based upon our tables of air pressures, secured by months of careful laboratory work, and confidence in our system of control … had convinced us that the machine was capable of lifting and maintaining itself in the air
— Orville Wright, from “How We Made the First Flight” (*)
I hope you were able to read the last sentence, as long as it is -- my editor would have been apocalyptic!
So, what have we got here with O.Wright's statement that can inform project management?
He begins with audacity! Audacity: "a willingness to take bold risks"
To be audacious! Audacity is a risk attitude that is at first personal, but then infects the whole project culture.
But not recklessly bold risks. Audacious is one thing; willful recklessness is entirely different.
Then comes the skill and science
So then comes the science, the engineering, and the risk management to leaven the audacity. Afterall, as we learn from author Jo Nesbo, "Someone will no doubt come up with an opinion with the benefit of hindsight, but we prefer to be wise in foresight".
In this case, wisdom in foresight requires:
And what does the world get with this elixir of bold thinking, careful consideration of risk, and skill-and-science?
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(*) Quotation courtesy of Herding Cats
Harry’s poker-playing friend claimed that probability theory, and how to play your cards according to the rule book, was the easiest thing in the world.
But what separated smart players from the not-so-smart was the ability to understand how their opponent was thinking ...
Harry Hole, Detective,
according to author Jo Nesbo
Now, in project terms, we would like to think we don't have to worry about opponents. But, of course, that's not the case. Most practical projects at scale have a nemesis, either technical, managerial, or political.
Also, it would be great if we could all grasp "Game Theory" which lays out methodologies for assessing what your opponent is likely to do in the context of what you might be likely to do. [You can read some about this in Chapter 12 of my book, cover illustration below, "Maximizing Project Value"]
In effect "Game Theory" combines probabilities, rules for applying game rules, and methodologies for making an 'informed estimate' of what others might do.
Stability and equilibrium
If you are working with probability theory in your project, then you are thinking and acting with some uncertainty in mind. As our detective friend says in the opening quote, just understanding some of the 'rules' around uncertainty estimates is not enough.
Coming to some sort of stable (and predictable) position in the project environment is perhaps the most advantageous outcome one could expect in an environment of uncertainty.
Perhaps the most practical utility of Game Theory, as applied to projects, is developing equilibrium as a stable and desirable outcome. Odd as it sounds, equilibrium is often achieved by accepting a sub-optimum outcome as a compromise outcome between 'adversaries'.
And why would you settle for sub-optimum? You settle because the alternative is more costly without compensating benefit.
The "Nash Equilibrium"
The Nash Equilibrium is an example of such an outcome, and it is explained in Chapter 12. In essence, you and your opponent agree to a compromise plan for which their is no net upside for either of you to divert from the plan. In other words, at the Nash Equilibrium, things are worse for you (and your adversary) if either of you make a change.
The counterpoint is obvious: as long as there are incentives for a more advantageous deal, stability will always be on the knife's edge. Recognizing whether or not you've achieved a Nash Equilibrium may be the smartest thing you can do.
"A well-ordered life has compartments. People that have secrets know that other people have secrets. That's how we all get alongDialogue from "The Paladin" by David Ignatius
Of course, in the pristine description of project methodologies in the PMBOK® and elsewhere, projects are an open book. The project pyramid is transparent from top to bottom; information is readily available.
Real projects are compartmented
I should say that a well-ordered project is strategically transparent but tactically compartmented. Everyone should know and internalize the major mission elements, but tactically, there is value in limitations.
Effective communication emulates life in a lot of ways, and one of those ways is to compartment information. Immediately, the N-squared number of communication channels between N sources is reduced exponentially. From that one action, communication quality goes up:
And, compartmentation is helpful when you need to put space between big egos; separate clashing personalities, and limit people interactions. It's how we all get along.
But also, compartmentation is a tool for limiting information according to sensitivity, proprietary protections, and utility according to function. Sometimes, it's just better to know less, at least with respect to tactical detail.
And, compartments reduce risk.
How so?
In the sailboat business, we speak of "rip stops" in sails. Sails are never one large sheet; they are always compartmented by seams. If a problem arises in one part of the sail, the "rip stop" seam contains the problem, prevents its spread, and usually leaves a lot of the sail useful for powering the boat.
The same goes for a project: compartments are "rip stops". A problem in one aspect of the project is contained, but the rest of the project goes forward.
And, I might add: In system engineering, we build with subsystems linked by carefully constructed interfaces. The interface provides "loose coupling" that helps contain and compartmentalize any issue in one subsystem.
The architecture of project methods
When developing the architecture of your project methodologies, think of what it takes to have a well-ordered project
Why is it so useful that it's the default go-to? Because many, if not most, natural phenomenon with a bit of randomness tend to have a "central tendency" or preferred state of value. In the absence of influence, there is a tendency for random outcomes to cluster around the center, giving rise to the symmetry about the central value and the idea of "central tendency". To default to the bell-shape really isn't lazy thinking; in fact, it's a useful default when there is a paucity of data.
Some caution required: Some useful stuff in projects is not bell shaped. Yes, the bell shape does serve as a most useful surrogate for the probable patterns of complex systems, but no: the bell-shape distribution is not the end-all and be-all.
But if no central tendency?The assumption people make is that, when considering change requests or feature requests from customers, they can identify the “average” size of such requests, and calculate “standard” deviations to either side. It is an assumption (and mistake)... Customer demand is, by nature, an non-linear thing. If you assume that customer demand has an average, based on a limited sample of earlier events, you will inevitably be surprised that some future requests are outside of your expected range.