On managing projects

Projects go wrong. They often do. They tend to go wrong inexplicably, when everything was just about done. They go wrong by orders of magnitude, we hear about a massive project costing billions being closed every other week. What the hell? How come? I mean, these people get paid and get paid well and they still can’t manage a project to be on time, on budget and bang on functionally?

Well, I guess they just can’t help it. The reason, as point out previously, is that humans are notoriously bad at predicting the behavior of even simple dynamic systems let alone a billion dollar 3-year project involving thousands of people in tens of companies.

And you all know what’s coming now. SD to the rescue! Simulate!

This and I suspect a couple of following posts will be on project management and thus it would make sense to establish the basics before plunging into modeling details. This is the basic model structure we’ll be using:

The model assumes that there is a set of work to do and that the work is divided into tasks. The tasks might be writing code, digging holes, it doesn’t matter. The main thing is that work flows out of the “Work to do” box towards two others: “Work done” and “Undiscovered rework”. You see, when you do something it might be OK or it might need changing later. Because you messed up, because somebody else messed up, it does not matter. The main thing is that you don’t know in advance if your work is indeed correctly done or needs to be re-done. That undiscovered rework flows back into work to do via process of rework discovery. Which for us, software folks, is simply called testing. We go “oh, dang” and more work appears on the todo list. Finally, there is a stream flowing in to undiscovered rework called “Deconstruction work”. This one counts for the need to demolish the incorrectly done work. When you pour 200 square feet of concrete incorrectly, you need to bang it to tiny pieces with hammers before it can be poured again. That sort of thing.

Of course, the model as depicted is just a scaffolding. The whole model (based on schoolwork in certain MIT courses but heavily modified) is too complex to go into detail here but the surrounding details can be roughly divided into following parts

  • Scope changes like scope creep, customer changing their mind etc. These things mainly influence the “Work to do” box
  • Personell issues like employee turnover, staffing decisions and such. This is going to have an impact on work flowing out of the Work to Do box. In trade magazines, this is called “productivity”
  • Rework discovery and impact. When and how testing happens and what is the nature of the bugs discovered including the amount, extent and dynamics of deconstruction work

At this point you should be going “dang, this is complex”. You are? Good. Because projects can be incredibly complex. For once, it is not trivial to estimate what the actual amount of time spent on the project would be. Even if you know what the estimates for the factors are, the math is non-trivial and you’d unlikely to be right on the money based on a gut feeling.

We’ll going to go into some more details in the following posts but here are some things this sort of modeling can do for you:

  • Deadline and resource estimates for large projects. Given the process model of your project, given the conditions, project size etc., what does the work estimate and load dynamics are going to be?
  • Process optimization. What happens if we change our development process? What happens if we start testing earlier? What happens if we start doing regular instead of continuous deliveries? Changes in staffing policies?
  • What-if analysis. Given our current project management framework, what happens if half of the team leaves? Customer adds a ton of new requests?
  • Root cause analysis. Our project went like so. OH GOD! WHAT HAPPENED? Model your process, make the result match your project and see if the results improve if you change the policies

Project management is one of the areas, where system dynamics has the most immediate and tangible practical application so the next couple of weeks are going to be interesting!

Allright, that’s all for now. Take care and observe System Dynamics in Action!


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