[For this series, it will help to have read “System Dynamics and Causal Loop Diagrams 101.”]
In the previous article we learned how to read an important feature of system diagrams. Namely, the interactions – the direction and whether or not the effect of the interaction was direct or indirect. With that understanding in hand, we can begin to look at real-life interactions. Well, real in the sense they are reflections of real-world interactions. These are interactions that take place outside the Work Loop but nonetheless affect the performance of the Work Loop.
By the time we’re done building out the model, you’ll be aware of just how many brake and gas peddles there on in this project management automobile (building on the metaphor used in the previous post for this series!)
We see there are several things that can interact with progress: How productive an individual or team is and how much effort they apply to their work. The green open-head arrow indicates that the relationship between each of these interactions and progress is direct. An increase in Productivity, applied Effort, or both will increase progress. Decrease Productivity or applied Effort and progress slows down.
That seems straightforward. But it isn’t all good news. Being more productive and applying more effort will also generate an unknown increase in Errors. Consequently, the amount of Undiscovered Rework will also increase.
This means that more effort needs to be applied toward discovering the Undiscovered Rework, so the relationship between Undiscovered Rework and the effort to actually discover the rework is direct (the green open-head arrow.) An increase in the amount of Undiscovered Rework results in an increase in the effort needed to actually discover all the hidden rework.
There is an inverse relationship in the mix here, too (the red closed-head arrow.) As the time it takes to discover defects and bugs increases, the rate of rework discovery decreases. This is particularly true with dark debt issues and defects that have been hidden in the system for months or even years. Finding gnarly bugs often takes a lot of time and effort. UI typos and misaligned text box labels, not so much.
So far, so good. But what affect does the additional work from the Rework to Do bucket have on the project schedule?
The system as it stands can only handle so much throughput. (Later in the article series we’ll cover ways to influence this throughput.) Adding Rework to Do to the flow of overall work that needs to be done will also slow down the rate at which original Work to Do gets to Work Done.
If project life is good the amount of Work to Do and Rework to Do decreases so that the amount of total Known Remaining Work decreases. If the amount of Work to Do and Rework to Do are increasing, the amount of total Known Remaining Work increases and project life is bad. (Figure 3)
There could be any number of causes driving the project down the bad road, hopefully only for a short while. Since we don’t know what we don’t know, after work begins on a project discoveries are made about additional work simply by working on known work. It could also be that additional work is added to the project intentionally. Perhaps marketing has discovered a feature that could place the end product in a stronger position or an existing feature needs to be strengthened to help close a sale or a planned approach turns out to be technically unfeasible or…the list is endless.
With the increase in the amount of Known Remaining Work, and all other aspects of the project remaining unchanged, at the very least the Time Required to Complete Work will increase. This in turn pushes out the projected delivery date and therefore increases the Delay to Completion. It’s at this point management starts getting grumpy.
Call out any project management methodology devised by man and it’s a safe bet that it drives toward establishing a predictable completion or delivery date. Agile methodologies are no different. Delivery dates are the interface between work teams and management. When faced with the news that a scheduled delivery date is at risk, management has two basic choices available to them. Either change the delivery date to match the performance of the delivery team or change the behavior of the delivery team such that the originally scheduled delivery date can be met. (A blend of the two is certainly possible but not particularly common in practice.)
The most obvious choice is to make changes that directly impact the Delay to Completion. That is, change the delivery date to accommodate the delivery team’s performance.
This introduces our first feedback loop – the Shift Deadline Loop (Figure 4, B.)
Let’s say the amount of Total Known Remaining Work has increased such that the Delay to Completion has grown to four months. If the decision is made to push the Deadline out by four months the effect is to increase the amount of Time Remaining which in turn decreases the Delay to Completion to zero. (Savvy Agile team members recognize that the shelf life of a zero completion delay is something less than 24 hours.)
But remember, schedule delays make management and other stakeholders grumpy. They’re loath to choose this path unless it is forced upon them by having exhausted all other options. And those options usually involve putting pressure on the system at other points.
If management chooses to follow the path of changing the delivery team’s behavior, the effects can be as far reaching as they can be significant. Depending on the choices made, the effects could be either very good or very bad. Very good results are hard. Very bad results are easy and therefore much more common. We will begin to explore these in the next article for this series.
1The core of the model I use to assess team and organization health is based on the work of James Lyneis and David Ford: System Dynamics Applied to Project Management, System Dynamics Review Volume 23 Number 2/3 Summer/Fall 2007