Tag: deadline10

--Originally published at TI2011 – Roger's Rad Records

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This chapter felt different from all the prior ones, so this entry will probably do too. Mr. Tompkins was staying in Rome for a few days and Johnny Jay, his old boss, wanted him to meet with somebody that also happened to be in Europe at the time. Since Mr. T respected Mr. Jay so much, he agreed to meet the guy.

Dr. Abdul Jamid had been working on management dynamics. Specifically, on a program that could translate the gut feeling that has been mentioned several times throughout the book into a controlled model that would be able to simulate different scenarios in a similar way to an algorithm. Mr. T was unsure about the viability of such a program, but after a couple of demonstrations and about two days of exchanging thoughts with Dr. Jamid he was convinced and acquired the software.

The book states that the software used by Dr. Jamis is a visual programming language that was marketed as ‘iThink’. Its actual name is a little longer: Systems Thinking, Experimental Learning Laboratory with Animation (or STELLA for short). It was introduced way back in 1985 as a program for system dynamics modelling.

I had never heard the term ‘system dynamics’, so after doing a little research I found that that’s the name given to certain approach that allows to model and simulate the behavior of complex systems by using several components, including loops, time delays and table functions. This small website explains that one of the main applications of system dynamics in business is, precisely, one of the topics from my last entry: improving performance over time. It also explains that when system dynamics is translated into the business/management context it is sometimes

Continue reading "DEADLINE, Chapter 10. “Run that simulation again!”"

--Originally published at TI2011 – Miguel’s Blog

In this chapter, Tompkins goes to Rome and gets in contact with a lawyer. They talk a bit about Tompkins’ old boss. Apparently, Tompkins really liked his old boss. His old boss arranged for Tompkins to meet with someone, Abdul Jamid. They start to talk about hunches. Hunches are prominent in this book, for example, Belinda deciding if she would hire someone. Hunches are generally in your head, but Abdul proposes a way to measure them and improve them. And it makes sense in some way, hunches are data inside you and there are algorithms to make a decision based on those hunches. By putting those hunches in a model, you would be able to improve your hunches. They mention a really clear example of why this is needed in the book that I really liked. If you and someone else feel unsure about something, how do you know who is more unsure? How do you measure that?

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However, using the whole mechanism for modeling hunches may be overkill for just one, but the model is supposed to be used by dozens of them. To put this to the test, Abdul asks Tompkins some simple questions about a project with one hundred people in it that will take one year, will two hundred people complete it in six months? It may seem simple at first, but as they keep talking, this simple model evolves and includes many other factors, such as people quitting, the training period for newly hired people, etc.

Another huge factor that was brought up in their conversation is the size of projects. And it’s clearly stated that smaller teams are more efficient. A study done in 2005 revealed that a small team (less than 5 people) completed a project of 100,000 lines of code Continue reading "Deadline, Chapter 10"

--Originally published at Project Evaluation and Management Blog

Finally a chapter where Webster is out of Morovia, it’s kind of refreshing to imagine a different scenario in your head when you read. But what for sure is different than any chapter is the proposal from Dr. Jamid, it seems we jumped from management to artificial intelligence, specifically a way to measure hunches.

There is a famous expression in the field of AI (artificial intelligence), and it says “When a solution that seems to require AI is created, then it’s no longer AI”. Somehow this expression is very accurate, as AI is always related to what is impossible, but once you make it possible it means there is a tangible solution, and suddenly AI is nothing more than machine learning, lots of graphs, and lots of ifs, in short, AI becomes the quantification of what didn’t seem possible to make numbers out of.