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Wednesday, September 25, 2024

The SpaceX approach


In the September 2-15, 2024 edition of Aviation Week and Space Technology there is an article about the 5-step process at SpaceX for getting the most effective project outcome. In a few words summarized here, the steps are:
  1. Challenge the Requirements. Interpret this as: your requirements are dumb; find a way to make them less dumb!
  2. Find a way to eliminate a process step of poor value, or a part or component that can be simplified
  3. Find a way to make it easier, faster, cheaper to reproduce or manufacture
  4. Prioritize speed and responsiveness in everything you do or will have done.
  5. Automate everything! Take people out of the manufacturing and production process whereover possible.


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Saturday, September 21, 2024

Einstein's methodology



"When I have one week to solve a seemingly impossible problem, I spend six days defining it, and then the solution becomes obvious."

Albert Einstein



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Wednesday, September 18, 2024

The ideal number of project workers is ....


One theory of project staffing is that the ideal number of project people is ZERO.
Don't believe it?
The objective of the project is to deliver something to the business in a timeframe and a budget.

At present, it takes people to do that. But ideally, the number of people would be vanishing small if they were super optimized, super talented, and only touched a process when it needed an initiation or some other budget.

Not buying this?
Well, at the business level, the same applies. 
Take a read .... 5 min or so... of this essay which makes the point, and then the counter point for the future of 9-5 jobs generally. 

From the essay (actually, the essay ends on an upbeat, so don't take the following as the only worthy content):
The ideal number of employees in any company is zero. If a company could run and make money using no people, then that is exactly what it should do.

Nobody owes anybody a job. Literally the only reason anyone has one is because there was a problem at some point in that business that required a human to do some part of the work. Building on that, if that ever becomes not the case, for a particular person or team or department of human employees, the natural next action is to get rid of them.

and just now —starting in 2023 and 2024, it is actually becoming possible to replace human intelligence tasks with technology.


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Sunday, September 15, 2024

A.I. Risk Repository


MIT may have done us a favor by putting together a compendium of risks associated with A.I. systems.
Named the "A.I. Risk Repository", there are presently 700 or so risks categorized in 23 frameworks by domain and cause, organized as a taxonomy for each of these characteristics.

The Causal taxonomy addresses 'how, when, and why' of risks.
The Domain taxonomy goes into 7 domains and 23 subdomains, so certainly some fine grain there. 

YouTube, of course
This is a public resource, so naturally there's YouTube on what it's all about and how to use it.

There's a lot of stuff
If you go the link, given in the first paragraph, and scroll down a bit, you will be invited to wade into the database, working your way through the taxonomies. There's just a lot of stuff there, so give it a look.   



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Tuesday, September 10, 2024

Slow is smooth; Smooth is fast


The blog title is actually from the mantra of the U.S. Navy SEALS
Slow is smooth; smooth is fast.

U.S. Navy SEALS

Now, this bit of wisdom may strike you as similar to the project tips we've been working with for years, to wit: "quality is free", and "it's cheaper and faster to do it right the first time" which recognizes the cost and schedule penalty of rework.

It's about rhythm and balance

From the SEALS website, we learn: "This phrase isn't just about being slow or fast; it's about finding a rhythm that balances precision and pace, ultimately leading to swifter progress. The SEALs swear by it... but how can we apply it beyond military contexts?

More depth:

Of course, there's a YouTube on "Smooth and Fast"

On the website, link given in the first sentence, there is a long-form article on the concept. Two chapters stand out:

Applying "Slow is Smooth, Smooth is Fast" Beyond Military Contexts

Incorporating the Mantra into Business Practices 
Using the Mantra for Project Management

The Role of "Slow is Smooth, Smooth is Fast" in Team Dynamics

Promoting Smoothness in Team Operations
The Mantra's Impact on Team Efficiency

In the PM domain, the recommendations are: 

  • Be deliberate; take the time to consider and prepare
  • Quality trumps speed (the cost of rework is embedded in this one)
  • Keep refining (Sort of a Bayesian idea, not so much continuous improvement)
In team dynamics, few errors and quality outcomes build trust, confidence, and high morale.



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Friday, September 6, 2024

Stability! It counts for a lot



Stability!
It counts for a lot.
It implies -- for behaviors and management decisions -- predictability, reliability, under-control (but not risk-free, of course), coherent narrative, steady-state goals, and a strategy that is understandable to those who have the job of implementing it.

Perhaps you are aware, as many are, that stability requires feedback to effect error correction and trap excesses and blind alleys. 
Ah yes!
We know about feedback.
Open loop systems -- those with outcome but no feedback -- are prone to many uncontrolled and unexpected responses. Who can predict what a stimulus will do to a system that has no feedback? Actually, that's a really tricky task.

So, what about feedback? 
What's to know?
  • Timing is everything! Getting the feedback "phased" in time such that it has a correcting effect rather than a destructive effect is vital. The former is generally called "negative feedback" for its corrective nature; the latter is generally called "positive feedback" for its reinforcing rather than corrective nature. And, when its too late, it's generally called ineffective.

  • Amplitude, or strength, or quantity is next: It has to be enough, but not too much. Tricky that! Experimentation and experience are about the only way to handle this one.
What could possibly go wrong?
Actually, a lot can go wrong.

No feedback at all is the worst of the worst: the 'system' is 'open loop', meaning that there are outcomes that perhaps no one (or no thing) are paying attention to. Stuff happens, or is happening, and who knows (or who knew)?

Timing errors are perhaps the next worst errors: if the timing is off, the feedback could be 'positive' rather than 'negative' such that the 'bad stuff' is reinforced rather than damped down. 

Strength errors are usually less onerous: if the strength is off, but the timing is on, then the damping may be too little, but usually you get some favorable effect

Practical project management
Feedback for correcting human performance is familiar to all. Too late and it's ineffective; too much over the top and it's taken the wrong way. So, timing and strength are key

But, the next thing is communication: both verbal and written (email,etc). Closing the loop provides reassurance of the quality and effectiveness of communication. You're just not talking or writing into the wind!

And, of course, in system or process design, loops should never be open. Who knows what could happen.

I should mention:
The study of feedback systems generally falls within what is called 'cybernetics'. As described by sciencedirect.com, MIT mathematician Norbert Wiener defined cybernetics as “the study of control and communication in the animal and the machine." 

From Wikipedia, we learn: The core concept of cybernetics is circular causality or feedback—where the observed outcomes of actions are taken as inputs [ie, feedback] for further action in ways that support the pursuit and maintenance of particular conditions [ie, 'ways that support' requires the correct timing and strength]



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Monday, September 2, 2024

The least Maximum schedule


To minimize your maximum schedule is a good thing. Or, it should be.
Here's how to do it:
  • Subordinate all other priorities to the most important tasks. This begs the question: is there an objective measure of importance, and from whom or what does such a measure emanate?
  • If you can measure 'importance' (see above) then do the densest tasks first, as measured by the ratio of importance to time.

    Note: a short time (denominator) will "densify" a task, so some judgement is required so that a whole bunch of short tasks don't overwhelm the larger picture. In the large picture, you would hope that the density is driven by the numerator (importance)

  • Always do an 'earliest start', putting all the slack at the end. You may not need it, but if you do it will be there.
  • Move constraints around to optimize the opportunity for an earliest start that leads to least maximum. See my posting on this strategy.

  • If a new task drops into the middle of your schedule unannounced, prioritize according to 'density' (See above). This may mean dropping what you are doing and picking up the new task. Some judgement required, of course. It's not just a bot following an algorithm here. 

  • If some of your schedule drivers have some random components, and you have to estimate the next event with no information other than history, then "LaPlace's Law of Succession" may be helpful, to wit:
    • To the prior random (independent) outcomes (probability) observed, add "1" to the numerator and "2" to the denominator to predict the probability of the next event. (*)

      So, by example, if your history is that you observed, measured, or obtained a particular outcome independently 3 of 4 times (3/4), LaPlace's Law would predict (3+1)/(4+2) as the probability for the next similar outcome, or 4/6. This figure is a bit more pessimistic, as you would expect by giving extra weight to the number of trials (denominator).
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(*) (n+1)/(d+2) isn't just a guess, or throwing a dart at a board. It is a rigorous outcome of an algebraic limit to a long string of 1's and 0's with historic probability of n/d. Although LaPlace did the heavy lifting, Bayes gets the popular credit for the idea of using prior observations as the driver for new estimates with a modified hypothesis.


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