ManagementSpeak: I need you to stay late.

Translation: It’s time for some unpaid overtime.

Speaking of which, how about adding your entry to the ManagementSpeak database? This week’s contributor took the time. How about you?

Speaking of reading — last week‘s topic, as in why so many IT professionals don’t do it and what you can do so they do …

Many, many moons ago (1974 to be precise), Edwin Newman authored Strictly Speaking: Will America Be the Death of English? an eloquent diatribe about … well, the title says it all, doesn’t it?

Newman held out “hopefully” as a particularly noxious step on the road to linguistic perdition. It means “In a hopeful fashion,” but it’s most commonly used in place of “I hope,” presumably because the speaker (or writer) doesn’t want to specify who is doing the hoping.

I hope you’ll join me this week in burying an even worse usage, worse because it not only combines bad English with bad math, but takes it from a sports metaphor.

The phrase in question: “We have to give 110%!”

Why I mention it: Several correspondents wrote to point out that making reading mandatory, no matter how it’s done, will likely crash into the all too common practice of oversubscribing staff on the grounds that, for exempt employees, “… this isn’t a 40 hour a week job, you know.”

When employees are already oversubscribed, adding a low-urgency task to the pile of work they already have won’t endear you to their hearts no matter how noble your intentions.

The flaw, though, isn’t with last week’s suggestions. It’s with the practice of considering 100% (or 110%) staff utilization to be a sign of efficient management.

It isn’t. It’s a sign of bad engineering.

To illustrate the point, consider an airport. Any airport has a theoretical limit to its capacity. Modeling it would entail something along the lines of dividing 1,440 (the number of minutes in a day) by the average number of minutes needed for one airplane to take off or land, multiplied by the number of runways that are usable in average conditions.

Imagine the airline industry was foolish enough to try to asymptotically approach this limit. Then imagine something disrupted the schedule — say a flight can’t take off because something in the cockpit broke. The only alternative would be to cancel the flight and rebook its passengers into open seats in other flights to the same destination, because if the airport is operating at capacity there would be no available time slots to reschedule the flight.

The general principle: Systems need enough unused capacity to absorb shocks — unplanned situations that require some of their capacity.

That is, if everyone is giving everything they have already, they’ll have nothing left for handling a crisis. And if their management thinks they do have enough left to handle a crisis, that just means they aren’t operating at capacity, and so should be given even more work assignments.

It’s sloppy thinking, imagining management can make 110% of capacity the new 100%.

Now I’m not so semantically intolerant that I don’t know what a head coach means when he insists players must give 110%. It isn’t that the coach wants everyone to flunk math. It’s that the players are capable of more than they think they are.

Which works just fine when players have time to rest and recuperate after a game. In a retail business it can work well enough when the challenge is handling a spike in pick-pack-and-ship warehouse demand because Cyber Monday sales were off the charts, assuming that by Cyber Thursday everyone can get back to a more reasonable workload.

It doesn’t work so well when programmers are enjoined to go above and beyond when they don’t get time to rest and recuperate after a long week of coding, any more than it makes sense to tell marathoners to try to sprint the entire race.

While we’re on the subject of time management, a phrase of advice on a related subject, multitasking. The phrase of advice: don’t do it.

As, thankfully, no sports metaphors occur to me, let’s talk about virtual memory –temporarily spinning off some RAM contents to disk, so as to load a different computing task into the just-vacated RAM for a few moments of processing, then rinsing and repeating for all other active computing jobs.

This works so long as the number of concurrent jobs doesn’t result in task switching time becoming a significant fraction of total capacity.

What’s true for computers is true for those who program and use them — when we’re forced to multitask the impact of our own switching time is very real.

Which leads to the [stunningly obvious] moral of this story: Don’t undertake workloads that are beyond your organization’s capacity.