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Releasing new work at the rate you’re completing old work is one of the most productivity-enhancing actions you can take, and yet equally, one of the most challenging improvements to accomplish.
[ Listen to audio version, read by David Hodes]
This is part 3 of the series. For more on this, see Part 1 | Part 2 | Part 4 | Part 5
If we know we are paying for a service by the hour, we like to think we are dutiful when we ensure we keep the providers of that service busy. It matters not if the provider is an employee or a contractor; the mental model is that busyness equates to, or at least somehow correlates with, value. Withholding work from a crew who would otherwise be idle seems almost sacrilege. Thus, ensuring all have work piled in front of them should be an axiomatic virtue built, in whole cloth, from the Protestant work ethic.
“Withholding work from a crew who would
otherwise be idle seems almost sacrilege”
It’s all very well being busy, but the difficulty arises, though, when faced with a variety of work options, as to where to focus your effort. If each discrete piece of work is considered a master to the labourer, then the inclination is to keep each of those masters at least somewhat happy. You never know who is going to ask for a progress report, nor when they might ask that question. So, if you can demonstrate at least some progress since the question of progress was last asked, you provide some protection from blame.
Furthermore, no man (or woman for that matter) is an island, and there is the issue of coordination of work coming from different sources, each with their own dependencies and deadlines. How do you come to know the correct sequence of work which will maximise benefits, minimise costs and keep the customer happy enough to continue coming back for more? If everything is thrown at you as soon as it’s ready from an upstream process, your island will quickly be overwhelmed by the tsunami of the push of work.
Expressed a little more formally, we know from Little’s Law that the long-term average quantity L of inventory in a stationary system is equal to the long-term average effective arrival rate λ multiplied by the average time W that the inventory spends in the system. Expressed algebraically the law is:
Although the formula looks intuitively simple, it is quite a remarkable result, as the relationship is ‘not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else’.
If we think of inventory as being the number of tasks in our work queue, it’s pretty evident that if we have an optimum rate of processing that work—and the rate of arrival of work is faster than the rate at which we process it—then we will have a buildup of inventory. And then we’re into a vicious circle in that the more inventory we have in the system, the lower the likelihood that the worker will be in flow, and the lower the rate of task completion.
Why would productivity suffer when there is a surplus of new tasks arriving faster than the rate at which we can complete the existing tasks? The truth is that we are not well equipped to deal with rapid switching between tasks, otherwise known as multitasking. The part of our brains accountable for executive function, and hence the planning and execution of tasks, is the prefrontal cortex. It’s the most recently evolved part, and it’s not especially big. Think of it as the central processor, having to call whatever is to be worked on into its rather crowded memory bank.
Every time a new task arrives, time is lost in putting down the existing work, assessing the new piece, kitting the mind with whatever it needs to draw on from the deeper recesses of memory and then executing according to whatever drives your sense of priority. Sometimes the decision may be to not change tasks. However, the effort in making the decision still robs the existing task of the benefit of its manager’s ability to work in flow, to focus, and to finish before assessing what to do next.
To test and prove the impact of multitasking on productivity, you can do the very quick experiment outlined below.
– You have been assigned to a program, composed of 3 projects, to be finished ASAP and with high quality.
– The goal for Project 1 is to write 20 letters in alphabetic order from A to T
– The goal for Project 2 is to draw 20 symbols in this repeating sequence:
-The goal for Project 3 is to write numbers from 1 to 20
In the first test, we are going to try to keep all three project managers a little bit happy. Thus, take a blank sheet of paper and divide it into three columns, headed Project 1, Project 2 and Project 3. Now find a stopwatch. (Go ahead, I’ll wait.)
When you are ready, press ‘Go’ on your stopwatch and work across each row: A, followed by ∆, followed by 1; then B followed by O, followed by 2 and so on, all the way until you have got to the row you fill with T, followed by ∞, followed by 20. It’s essential to work across the rows as you make your way down the columns. As soon as you’re done completing all the cells, record the finish time from your stopwatch.
For the second test, we want to simulate working in flow, without forcing the brain to change continually between letters, symbols and numbers. Thus, once again mark up a blank piece of paper with three columns, headed Project 1, Project 2 and Project 3. Start your stopwatch and go down the whole of column 1, marking off all the letters from A to T, before moving across to the repeating pattern of symbols. Once you have placed the infinity symbol in the last box, jump over to Project 3 and complete the column with numbers 1 through 20. Once again, as soon as you have completed the entries into all the cells, record the finish time on the stopwatch.
I would be very surprised if you took longer working in flow than you did when multitasking. Even when you are in the process of completing each box, when executing the multitasking option, you can feel the brain-strain of switching from letter to symbol to number and back again. By contrast, when working in flow, there is far less mental effort running through first the alphabet, then a symbolic sequence and finally a logical numeric one.
Think of your work as soft rubber balls flowing through a well-oiled pipe, in the middle of which is a valve. Imagine that the valve has a diameter which is 25% smaller than the pipe on either side of it. At the beginning of the flow, you’ve installed a bloody great pump, calibrated to deliver a steady flow of these soft rubber balls, based on the diameter of the pipe—not the valve. Pretty soon, you’re going to gum up the works, and the whole system will come to a standstill, smashing the pump, breaking the valve or puncturing the pipe—perhaps all three.
Wouldn’t it be better to have a pump controlled by the rate at which the squishy balls exited the valve? Wouldn’t you want to have the release of new work tied by a rope to the exit of existing work from the production system? To achieve this end, you have to control work release. It allows the people who have started work, to focus and finish it—provided, of course, they are fully kitted and are working off true data.
You enable production flow by matching the release of work to the capacity of the system constraint—the bottleneck. In Theory of Constraints (TOC), we call this bottleneck the ‘drum’ because its beat synchronises all parts of the whole. Push work into the system faster than the drum can complete it and you create a logjam. Release work too slowly and the drum misses a beat. Flow stutters and production drops for minutes or hours, with the inevitable consequence of degraded performance.
When you know the capacity of the drum, you can release the appropriate amount of work to keep things flowing through the bottleneck at the optimum speed. TOC’s production and project management systems—Drum Buffer Rope and Critical Chain—were invented to solve the challenge of releasing work in the Goldilocks zone: not too much, not too little, but just right.
But, a sound method is not enough. So important is the idea of the control of work release and its companion idea of full kitting, that each of those activities should separately have a manager with these specific accountabilities included in their job role. The work-release manager depends on the full-kitting manager to have everything ready for the timeline dictated by the schedule. Crucially, the work-release manager must have the full authority to prevent work from starting whenever he or she knows that whoever is allocated a task cannot complete it.
You now appreciate why releasing new work at the rate you’re completing old work is one of the most productivity-enhancing actions you can take. So why is it so challenging to accomplish?
Most of us have no trouble, in theory, of grasping the concept of controlling the flow of work. In practice, however, it’s remarkably difficult to convince our own brains not to jump from task to task—despite the knowledge that we’re hobbling our productivity. If we struggle even with tasks we’ve assigned ourselves, imagine how much harder it is to manage the flow of work through our whole system—and actually encourage those working at non-constrained points to mindfully manage their slack.
To know what work to control, and how to control it, as a start, use the 5-Step FOCUS to identify and optimise the constraint. Then take time out to think about how you would implement the 5 Commandments of High Performance Execution. Previously we covered Maintain True Data and Work Fully Kitted. We’ll have a more detailed look at the other two commandments in the last two articles in this series: ‘Resolve Issues Rapidly’ and ‘Act by Priority’.
This article is part of our series: Five commandments for high-performance execution
Part 1: Maintain True Data
Part 2: Work Fully Kitted
Part 3: Control Work Release
Part 4: Resolve Issues Rapidly
Part 5: Act by Priority
The change from standard thinking to Theory of Constraints (TOC) is both profound and exhilarating. To make it both fun and memorable, we use a business simulation we call The Right Stuff Workshop.
We’d love to run it with you. To learn more:
[Background image: Traffic Lights, Tim Gouw on Unsplash]
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