Assessing and Tracking Team Performance – Part 8: Taming the Wild Horses

Over the years I have come to regard projects as a boat in the ocean and relationships as the ocean.Michael Wade

Remember the phrases from earlier in the article series? Here they are again.

  • “We’re not moving the delivery date.”
  • “We’ll just have to work harder.”
  • “The team will have to put in more time until we’re caught up.”
  • “We’ll need more people on the project.”
  • “The team will have to work faster.”
  • “We’re to the point of exhaustion.”
  • “I’m losing track of all the pieces.”
  • “There’s no time for training.”
  • “Where did those errors come from?”
  • “We’re waiting on another team.”
  • “Another person quit the company?!?!”
  • “I don’t care. I get done what I get done when I get it done.”

How much more meaningful these are to you now that you understand a little more about the system dynamics that drive projects. Choose just one of these and find where it’s reflected in the model. (Figure 1)1.

Figure 1 (click to enlarge)

Now follow the impact and consequences around the various feedback loops. Reflect for a moment an ask yourself, “What can I do to help keep the system healthy and productive in light of what I now know may be happening?” There’s a lot to consider. We’ll cover several options in this article.

Moving from the outside in, the most visible nodes in the system are also influenced the least by direct intervention. These are Morale, Fatigue, and Experience. “The beatings will continue until morale improves” is, I hope, recognized as a cynical joke. While offering free coffee, Red Bull, and unlimited M&Ms may perk up employees in the short term, the long term health consequences are grim indeed. As for Experience, well, that just takes time and a great deal of effort to fully shape and mature.

Attempting to alter these nodes directly is likely to be wasted effort at best and more probably harmful. Even if some cursory improvement can be made, the underlying systemic influences – the true drivers – will still be present and will exert a far more powerful influence. It’s Conway’s Law, pure and simple. It’s better to thinking of Morale, Fatigue, and Experience as symptoms or indicators to be recognized and tracked rather than root causes to be treated. As indicators, they are incredibility powerful sources of information on whether or not changes made to other parts of the system are being successful. They are to be used, not abused.

We’ll begin by working backward from the disaster that was built up over the last several articles in the series. Let’s imagine we have a demoralized team (or teams) that are exhausted and burdened with an impossible delivery schedule. As it stands, it’s unfixable.  A sprinter has a better chance of breaking the three minute mile than this team has in delivering their project by the stated delivery date.

Let’s also assume the choice is to continue the project. The two major actions for management at the is point are to move the Deadline and reduce the amount of Work to Do in the system. These aren’t choices, they’re actions that need to be engaged thoughtfully.

Simply moving the date to some point in the future that seems “doable” is yet another gamble. Neither will moving the date instantly resolve the other systemic issues. There is a considerable amount of recovery and rebuilding to be completed. It takes time to hire the people needed to rebuild the workforce. It takes time to rebuild trust and morale among the employees that remain. Moving the deadline out will begin to relieve pressure, but it will take time for the inflamed system to cool down and find an optimal working temperature.

The challenge for this first step is: How can you go about finding what is a reasonable date for the deadline? Answering this question is dependent on what is learned by looking to other parts of the system model for data.

  • How depleted is the Workforce and how long will it take to build it back up?
  • How much of the critical talent has remained with the organization (Experience)?
  • Is any compensation (time or money) going to be offered to offset the Overtime put in on the project?
  • How much time will it take to refactor and refine the product backlogs such that work streams can are brought into alignment and Overlap and Concurrence and Task Switching minimized?
  • What tool and process changes need to be made to reduce the Congestion and Communication Difficulties?
  • What’s the Total Known Remaining Work in the system?

Probably, the best thing to do is to declare that for some time boxed period, there will be no deadline date while these and many other questions are explored. This will have a side benefit of signaling to the development teams that management is serious about finding a realistic date. This will help to start rebuilding trust between management and the development teams.

One of the factors to consider in determining whether a new deadline can reliably be set is the Total Known Remaining Work in the system. As has been discussed previously, increasing the Total Known Remaining Work puts pressure on the completion date. Similarly, decreasing the
Total Known Remaining Work by some means will increase the likelihood that the completion date can be met. Actions to take that will allow management to regain control of the work flow include:

  • Revisit the release schedule and take a phased approach with clearly defined minimum viable/valuable product deliverables.
  • Complete a detailed review of the work done to date to get a clear picture of the amount of technical and dark debt in the system.
  • Reassess the sales and marketing strategies so they are in clear alignment with the capabilities of the development and delivery system. What can be eliminated? What can be pushed to future releases? Eliminate “nice to have’s” from this list. Either the feature can be completed in a particular release or it can’t. Those that can’t are bumped to a future release.

It’s been shown that changes in one part of the system will affect other parts of the system, whether by design or not. In this article we’ve discussed how adjusting the Deadline and Total Known Remaining Work can affect each other and the entire system. When adjusted in a way that considers system-wide effects, they can help restore balance and predictability to the overall system.

Previous article in the series: Assessing and Tracking Team Performance – Part 7: “Abandon All Hope,…”

References

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

Assessing and Tracking Team Performance – Part 7: “Abandon All Hope,…”

“…ye who enter here.” So reads the inscription to the Gates of Hell in Dante Alighieri’s epic poem, “Divine Comedy.” Who among us hasn’t felt on occasion that stepping across the threshold to our place of employment is like passing through the gates of Dante’s Inferno? But as the poets have told us, the way to peace is to find the path through our troubles. In this article, we’ll look into just how deeply project system dynamics can adversely affect progress and even whether or not the project is successful.

But I do want to arm the reader with a couple of rays of hope. The concluding article in this series will focus on how this system model1 can be used to good effect, how it can be used to identify problems before they grow out of control. Therein lies the path to peace. Before we get there, we need to understand several more influential feedback loops.

As the Delay to Completion becomes critical, management begins to panic. Not wanting to push the deadline out they work to influence the other three options focused on modifying the behavior of the delivery team. The end result is a team that is caught in the Work Faster, Work More, and Add People loops along with all the other associated downstream loops. The effect is compounded by the emergence of other feedback loops if teams are placed in this position for an extended period of time.

Over time, the shortcuts, hacks, and quick fixes put in place to keep the pace of progress as high as possible settle in as technical debt. They work – for now – so they don’t surface as errors for quality assurance to discover. Down the road, however, solutions hastily put in place as stop-gaps fail when later solutions require existing solutions to be more robust then they are. For example, a software method that doesn’t take advantage of multi-threading may break when a later solution needs that method to scale beyond it’s single thread capacity. The shortcut is now a defect.

Figure 1 (click to enlarge)

If the technical debt remains in place for an extended period of time, it may be covered by several release layers. When it does flip to defect status due to some later stress, it can be much more time consuming and expensive to uncover. The original developer of the code may not be available or even if she is, it could take her quite a bit of time to become reacquainted with the code. This can be thought of as a form of dark debt and is reflected in the Errors Build Errors Loop (Figure 1, J).

As the teams struggle to keep up the pace of progress and reduce the Delay to Completion, work streams start to become out of sequence. One team has an easier time at crafting their solution while another, to which they are dependent on the output, hits a significant snag and is delayed several weeks. In order to stay busy, the first team starts work on something else while the second team finishes their work. When the second team delivers, the first team is not prepared to immediately shift back to their original work stream and so their deliverable is delayed even further. Meanwhile, a third team, that was dependent on the first team’s deliverable has now been delayed by the cumulative delay of the first two teams. Teams and individuals begin to take shortcuts as delivery of interim work products become out of sync with each other. The diminished focus and desynchronization of work streams leads to an increase in the Error Fraction, which in turn leads to a further Delay to Completion. This is the Haste Makes Out-of-Sequence Work Loop (Figure 1, K).

Figure 2 (click to enlarge)

As the effects of the Haste Makes Out-of-Sequence Work Loop build,  team begin switching back-and-forth between work streams depending on who is making the most noise for the completion of any particular deliverable. This is the Thrash and Churn Loop (Figure 2, L). Switching from stream to stream or, in worst cases, task to task, places a tremendous burden on development teams and can do more to slow progress than almost anything else I’ve encountered in team management. Not covered in this model is the type of churn that occurs when parts of the project undergo redesign after work has begun on the existing design. Long term projects are particularly susceptible to adverse impacts from redesign as the changes are often farther reaching. The drivers behind a redesign can range from trivial (a new CTO has a personal dislike for a platform vendor) to critical (a security flaw uncovered in a core technical component.)

If all the loops described to this point in the article series are allowed to run uncorrected the system is likely to crash as the project becomes one massive firefighting effort. A key indicator for when this is happening is employee morale.

Figure 3 (click to enlarge)

The increased Fatigue, the growing burden of Work/Rework to Do, the unsatisfying Task Switching between work assignments all combine to causes a decrease in team Morale. This is the Hopelessness Loop (Figure 3, M). Teams are left with a powerless feeling of being caught on a never ending treadmill. And so, stepping across the threshold to the office is like passing through the gates of Dante’s Inferno.

The ripple effect from a decrease in Morale leads to a decrease in the Workforce as employees leave the organization in search of less stressful, more satisfying work. This is the Turnover Loop (Figure 3, N). The remaining demoralized employees are even less productive and unhappy employees make more mistakes, thus increasing the Error Fraction in the system. The downstream result is that the Delay to Completion increases yet again.

If corrective action isn’t taken the law of diminishing returns becomes evident and the system collapses. The cost overruns become prohibitive and the project is cancelled. Worst case, the organization runs out of resources (money, time, or both) and goes out of business. Those are bad things. In the concluding article to this series, we look at how this model can be used to read the current state of a project’s system dynamics and explore some ways we can intervene such that the system doesn’t run out of control.

Previous article in the series: Assessing and Tracking Team Performance – Part 6: It Lives! But it’s Out of Control!

Next article in the series: Assessing and Tracking Team Performance – Part 8: Taming the Wild Horses

References

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

Assessing and Tracking Team Performance – Part 6: It Lives! But it’s Out of Control!

In the previous article for this series, I described three options managers could consider if moving the project deadline was out of the question.

  1. Increase employee work intensity
  2. Call for overtime
  3. Hire people

On the face of it, they each appeared to offer a path toward returning a drifting schedule to be on time. Now let’s look a little further down the road to see what happens when the juice is applied to each of these options in turn. If we implement any of these options, what are the likely consequences?

We know that errors in the work flow are unavoidable. If we encourage or pressure the development team to finish more work in less time (the Work Faster Loop1, Figure 1, C) this will result in an increase in the errors along with an increase in the amount of Work Done.

Figure 1 (click to enlarge)

This is the Haste Makes Waste Loop (Figure 1, F). In other words, the increase in Work Intensity will have a concomitant increase in the Error Fraction which means there is an increase in Errors generated. The extended consequence of pulling the Work Intensity lever is an increase in Work to Do in the form of extra Rework to Do.

OK. So Option 1 isn’t a get-out-of-jail-free card. There are strings attached. How about Option 2, call for the development team to work overtime?

Figure 2 (click to enlarge)

By increasing Overtime, the risk of Fatigue increases sharply. This results in yet another increase in the Error Fraction (tired people make more mistakes than rested people) and a decrease in Productivity (tired people don’t work as efficiently as rested people.) Both slow down Progress and increases the amount of Rework to Do in the system. This is the Burnout Loop (Figure 2, G).

OK. So Option 2 doesn’t lead to sunshine and roses. There are dark clouds and weeds in the mix. Let’s give Option 3 a go, hire more people!

Figure 3 (click to enlarge)

So we’ve beefed up the Workforce by hiring a bunch of people to join the team. With all those extra people in the mix we’ve also increased the overall Congestion and Communication Difficulties. The email traffic increases, everyone’s Inbox fills up faster, meeting attendee size increases along with the number of meetings. The signal to noise ratio decreases and miscommunication increases. This increases the Error Fraction, decreases Productive, and decreased Progress. End result: the Too Big to Manage Loop (Figure 3, H).

But that’s not all. By hiring extra people, we’ve activated the Expertise Dilution Loop (Figure 5, I).

Figure 5 (click to enlarge)

All those new hires don’t come in off the street ready to go. They decrease the depth of Experience available to focus on making progress. Experienced employees have to slow down and assist new employees in understanding the technical systems, the architecture, and development standards. New employees will need some period of time to become familiar with the work environment, project objectives,  who’s who, and where the coffee is.

As they work to understand and gain experience with the systems, new hires will necessarily make mistakes and increase the Error Fraction. While there are more workers available to focus on the product backlog, the available expertise is spread much more thinly and is collectively less experienced until such time the new workers are up to speed with what needs to be done and how. So the errors go up and Productivity goes down. The down stream effect is often a further increase in the Delay to Completion. As the saying goes, throwing more people at the problem more often than not makes the problem worse.

OK. So no unicorns and rainbows here either. More like a lot of warthogs and rain.

Looks like the first level effects were negated by the second level consequences. That’s bad enough, but the third level consequences can be even worse in that they are often much longer lasting and much more difficult to resolve. We’ll look at those in the next article in this series.

Previous article in the series: Assessing and Tracking Team Performance – Part 5: Welcome to the Labyrinth

Next article in the series: Assessing and Tracking Team Performance – Part 7: “Abandon All Hope,…”

References

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