The speed of a to-do list

Do you have items on your to-do list that sit there forever?

Little’s law can shed some light on this problem. Instituted by John Little, a University professor in operations research, the law itself is simple. It states that

Average response time = Number of items in queue / Throughput

Let us consider a couple of examples to unpack that equation. The average waiting time at a hospital, with a queue of 9 patients and a throughput of thirty patients per hour would be 9 / 30, or 0.3 hours (18 minutes). On your to-do list of 9 items, say you tackle things at a rate of 3 items per day, the average response time for a task is 3 days.

As you can see, the law itself is highly intuitive. But let us consider some of its broader implications. As humans, low response time motivates us. A team which has a moving to-do list is likely to make more progress overall. The satisfaction of striking things off a list keeps us going. But items that stay on the list forever do the opposite. They drain our motivation. So our main goal is to reduce response time. This goal has two simple implications:

1. Keep the number of items in the queue to a small number, thereby reducing the numerator

2. Keep each item small, thereby increasing throughput and the denominator

Although this seems obvious, we often have the opposite tendencies. When a project slows down, the manager’s immediate impulse is to throw more items into the to-do list, expecting it to move faster. Under pressure, they are also likely to make each item more substantial. Alas, both these measures only slow their team down further.

Few things motivate us like forward motion. On a long trek in the mountains, one of my group leaders mentioned how the key to sustain momentum is to take smaller steps. By doing so, we go farther in the long-run.

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