How important is the physical workspace to knowledge workers generally,and software developers specifically? Everybody agrees it'simportant. Talk to ten people, though, and you'll get nine differentopinions about what aspects are important and how muchthey impact effectiveness. But there are some classic studies thatshed some light on the subject; looking around recently, they haven'tbeen refuted. At the same time, a lot of people in the softwareindustry don't seem to have heard of them.

Take the amount and kind of workspace provided to each knowledgeworker. You can quantify this (number of square feet,open/cubicle/office options). What effects should you expect from,say, changing the number of square feet per person from 80 to 64? Whatwould this do to your current project's effort and schedule?

There's no plug-in formula for this, but based on the available data,I'd guesstimate that the effort would expand by up to 30%. Why?

"Programmer Performance and the Effects of the Workplace"describes the Coding War Games, a competition in which hundreds ofdevelopers from dozens of companies compete on identical projects. (Also described in Peopleware: Productive Projects and Teams.) Thedata is from the 1980's, but hasn't been replicated since as far as Ican tell. The developers were ranked according to how quickly theycompleted the projects, into top 25%, middle 50%, and bottom 25%. Thecompetition work was done in their normal office environments.

It itself, this doesn't give us an answer for the question we startedout with (changing from 80 square feet to 64 square feet per person,and bumping up the people density commensurately). As a firstapproximation, let's assume a linear relationship between dedicatedarea per person and productivity ratios. 64 is just over halfwaybetween 46 and 78, so it seems reasonable to use half of the 2.6factor, or 1.3, as a guesstimate. So using this number, a project thatwas going to take two weeks in the old environment would take 1.3 timesas long, or around two and a half weeks, in the new environment. (Inthe long term, of course.)

To put this into perspective, it appears that increasing an organization's CMM level by one generally results in an 11% increase in productivity, and that the ratio of effort between worst and best real-world processes appears to be no more than 1.43.

You can't follow the numbers blindly here. This probably depends a loton the kind of work you actually do, and I can think of dozens ofcaveats. My gut feeling is that the penalty is likely to be more like10% than 30%, assuming you're really holding everything else (noise,interruptions, etc.) as constant as possible. I suspect that theorganizations which are squeezing people into ice cube sized cubiclesare likely to be destroying productivity in other ways as well. But,these numbers do provide some guidance as to what to expect in terms ofcosts and consequences of changing the workplace environment.

Links and references:

Take the amount and kind of workspace provided to each knowledgeworker. You can quantify this (number of square feet,open/cubicle/office options). What effects should you expect from,say, changing the number of square feet per person from 80 to 64? Whatwould this do to your current project's effort and schedule?

There's no plug-in formula for this, but based on the available data,I'd guesstimate that the effort would expand by up to 30%. Why?

"Programmer Performance and the Effects of the Workplace"describes the Coding War Games, a competition in which hundreds ofdevelopers from dozens of companies compete on identical projects. (Also described in Peopleware: Productive Projects and Teams.) Thedata is from the 1980's, but hasn't been replicated since as far as Ican tell. The developers were ranked according to how quickly theycompleted the projects, into top 25%, middle 50%, and bottom 25%. Thecompetition work was done in their normal office environments.

- The top 25% had an average of 78 square feet of dedicated office space.
- The bottom 25% had an average of 46 square feet of dedicated office space.
- The top 25% finished 2.6 times faster, on average, than the bottom 25%, with a lower defect rate.
- They ruled out the idea that top performers tended to be rewarded with larger offices.

It itself, this doesn't give us an answer for the question we startedout with (changing from 80 square feet to 64 square feet per person,and bumping up the people density commensurately). As a firstapproximation, let's assume a linear relationship between dedicatedarea per person and productivity ratios. 64 is just over halfwaybetween 46 and 78, so it seems reasonable to use half of the 2.6factor, or 1.3, as a guesstimate. So using this number, a project thatwas going to take two weeks in the old environment would take 1.3 timesas long, or around two and a half weeks, in the new environment. (Inthe long term, of course.)

To put this into perspective, it appears that increasing an organization's CMM level by one generally results in an 11% increase in productivity, and that the ratio of effort between worst and best real-world processes appears to be no more than 1.43.

You can't follow the numbers blindly here. This probably depends a loton the kind of work you actually do, and I can think of dozens ofcaveats. My gut feeling is that the penalty is likely to be more like10% than 30%, assuming you're really holding everything else (noise,interruptions, etc.) as constant as possible. I suspect that theorganizations which are squeezing people into ice cube sized cubiclesare likely to be destroying productivity in other ways as well. But,these numbers do provide some guidance as to what to expect in terms ofcosts and consequences of changing the workplace environment.

Links and references:

- In How office space affects programming productivity(IEEE Computer Vol. 28 No. 1; Jan 1995, pp. 7676) Capers Jones gives aguideline of at least 80 square feet of space per person, with fullwalls and doors, for optimal productivity.

- The most well-documented planning exercise for knowledge worker facilities is IBM's Santa Teresa facility; a discussion is here.
- Steve McConnell gives a good overview of this and other issues in Quantifying Soft Factors (IEEE Software Vol. 17 No. 6: Nov/Dec 2000, pp. 9-11).
- T. DeMarco and T. Lister , "Programmer Performance and the Effects of the Workplace", Proc. 8th Int'l Conf. Software Eng., ACM Press, New York,1985,, pp. 268-272.
- A great anecdote: Joel Spolsky, Bionic Office. He's betting a lot of money that it's effective to equip his company with spacious, private offices.

I think it is possible to fit a well defined mathemtical function to this data. In the degenerate case, squeezing a programmer into a black hole, whose effective volume (modulo the event horizon) we can define as 0, will indeed reduce productivity to 0. In this context productivity can be defined as work. Energy output as measured by Hawking anti-particles, originating from the programmer inside the horizon, evaporating at the event horzion, is on the order of 10**-32 eV. So the lower bound is well defined. At the upper bound, puting a programmer into an infinite space may not result in infinite productivity, though as anyone who lives in Silicon Valley knows, with the cost of housing and office space being what it is, the cost of even a finite amount of space rapidly approaches infinity, so its safe to say that a programmer with an infinite amount of space is either god or works for god, so by definition productivity is infinite. However, for the set of non-theistic solutions in bounded space-time, we can postulate that productivity is asymptotic to some upper limit P for an area A as A approaches an upper bound A(max). Quantum degeneracy pressure means that no two programmers can occupy the same energy level within a radius R where R = R(P,A,v($)) where v($) is the cost vector per hour expressed as a Greenspan eigenvalue. It is then a simple matter to deduce that if

ReplyDeleteA >= 3000 sq. ft. and |v($)| >= $840 per hour

then P == P(max).