Tag Archives: conventional wisdom

Conventional Wisdom & Utilization

As you are most likely aware, “utilization” is a measure of the actual number of units produced divided by the number possible when machines and people work at full capacity.

Conventional wisdom says that the best way to maximize profits is to encourage every department within an organization to achieve 100% utilization. Like so much of conventional wisdom, this has a ring of truth to it; and it has the added beauty of simplicity. We can evaluate and reward each department independently of one another, and if everyone is given incentives to get as close as possible to 100% utilization, then the company will surely be maximally profitable.

But this premise will fail us in the real world… a world riddled with variation.

For example, let’s say a company has three operations:
• Glass Blowing
• Filament Insertion
• Cap & Wrap

Utilization of the 3 departments is 50% in Glass Blowing, 100% in Filament Insertion, and 80% in Cap & Wrap. So where do you focus your improvement efforts? The natural conclusion is that you would focus on increasing utilization in Glass Blowing: either by increasing production (which would simply increase the inventory of bulbs waiting for insertion) or by decreasing capacity.

But if you look at the throughput of the process as a whole, you see that Filament Insertion is the bottleneck. At 100% utilization, they are unable to produce enough to keep the next operation, Cap & Wrap, fully utilized. Furthermore, Glass Blowing, despite the lousy utilization numbers, is already piling up inventories of bulbs waiting for filaments. The utilization numbers suggest that Filament Insertion is the last area needing improvement, but to improve the process flow, it must be the first area to improve.

If the world were perfectly predictable, we could reduce the capacity in Glass Blowing and Cap & Wrap to exactly match Filament Insertion to achieve 100% utilization. But if we did so in ‘Murphy’s world,’ any variation in glass blowing production — such as machine downtime, absenteeism, yield deterioration, material availability or quality issues — will not only impact Glass Blowing utilization numbers, but the bottleneck — Filament Insertion —will also be idle! Production opportunity lost at the bottleneck is lost forever. Instead of trying to optimize individual operations, identify the bottleneck and make sure there is enough capacity in the feeder operations to ensure that any disruptions do not impact the utilization of the bottleneck capacity. Instead of aiming to maximize utilization at each operation, as conventional wisdom would have us do, we must find and eliminate waste at the ‘bottleneck’ or ‘rate-limiting’ step in order to increase profitability.

Conventional Wisdom & Specialization

Conventional wisdom is an asset in so many situations that one can hardly do without it. After all, it “makes sense!” Or at least it seems to make sense… and it can speed-up consensus and increase our confidence in our decision making.

But in a dynamic world when the underlying assumptions shift, we conventional wisdom easily lead our organization to make some big
mistakes.

For example, conventional wisdom holds that specialization is good. A person can get very fast and reliable doing the same thing the same way again and again. The classic example is Henry Ford’s assembly line which broke the complex craft of auto assembly into a sequence of very specialized jobs that could be easily taught to the relatively unskilled labor on the assembly line. Assembly line efficiencies put automobile ownership in reach of a much larger portion of the country and made the benefits of specialization a part of our national business psyche.

Specialization is applied to many jobs today as well, such as dividing a call center into teams of specialists by type of call, or dividing incoming orders so that one person handles all of the especially complicated jobs, or conversely, the easiest tasks may be pruned off and assigned to a junior person to exclusively handle.

When the volume and nature of the work flow is predictable, specialization can increase both efficiency and quality. But when the quantity, timing, or nature of the demand for work is uncertain, specialization significantly reduces efficiency!

For example, when a service organization wanted to speed up throughput and reduce overtime costs for processing new account applications for clients in the Financial Services industry, they organized their processors into different groups to handle different clients. This enabled each processor to complete an account
set-up faster because they could easily memorize the steps and forms for their small group of clients.

Nonetheless, the efficiency of the operation as a whole declined substantially. Variation in the incoming volume resulted in one group being swamped one day and working overtime, while another group was very slow.

So the rule is: sequential specialization, like the assembly line, often speeds up the mastery and execution of the subset of work, but will also reduce the total throughput whenever there is variation in the amount of time that a step may take. Each hand-off is an opportunity for work to be waiting for a worker or for a worker to be waiting for
work. When variation is low, specialization can increase throughput, but if there is variation in incoming quality or in the amount of time needed to execute a step, specialization tends to reduce efficiencies of the operation as a whole.

Conventional Wisdom Can Be an Asset & a Liability

roadchoice copySeveral recent posts focused on “confirmation bias,” which is the tendency to pursue and embrace information that matches our existing beliefs.

A somewhat related concept is “conventional wisdom,” which has been defined as “the body of ideas or explanations generally accepted as true by the public or by experts in a field.”

Interestingly, it can be both an asset and a liability!

On the plus-side, conventional wisdom speeds up consensus and increases our confidence in our decision making, leaving us to focus our attention on challenges for which there is no conventional wisdom to guide us. And conventional wisdom has much truth within it — having been developed over decades of observations.

For example, conventional wisdom holds that specialization is good. A person can get very fast and reliable doing the same thing the same way again and again; and, generally speaking, when the volume and nature of the work flow is predictable, specialization can increase both efficiency and quality. The classic example is Henry Ford’s assembly line which broke the complex craft of auto assembly into a sequence of very specialized jobs that could be easily taught to the relatively unskilled labor on the assembly line.

Beware…!
But in a dynamic world when the underlying assumptions shift, we follow conventional wisdom at our peril as it can easily lead our organization to make some big mistakes!  In fact, when the quantity, timing, or nature of the demand for work is uncertain, specialization significantly reduces efficiency!

For example, when a service organization wanted to speed up throughput and reduce overtime costs for processing new account applications for clients in the Financial Services industry, they organized their processers into different groups to handle different clients. This enabled each processer to complete an account set-up faster because they could easily memorize the steps and forms for their small group of clients. Nonetheless, the efficiency of the operation as a whole declined substantially. Variation in the incoming volume resulted in one group being swamped one day and working overtime, while another group was very slow.

To achieve the benefits of specialization, you need something increasingly uncommon in today’s world: high volume/low variation work. For work that is low volume/high variation, specialization tends to reduce throughput. In such an environment, multi-skilled generalists are far more valuable. Specialization may maximize the speed of the individual, but sub-optimize the process as a whole.

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