Tag Archives: the perils of conventional wisdom

Another Pitfall Akin to Confirmation Bias

Thinking outside-of-the box?

Our previous post shared some thoughts on the pitfall of “confirmation bias,” which is the tendency to pursue and embrace information that matches our existing beliefs.

A somewhat related concept that can, surprisingly, be equally as dangerous 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.” It is frequently referenced as “inside-the-box” thinking, as opposed to taking an approach that challenges convention (i.e., “outside-the-box” thinking).

But contrary to popular belief (or, to ‘conventional wisdom’ – ha ha!), this seemingly safe practice 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, a-la Henry Ford’s production line.

However, while specialization can increase both efficiency and quality when demand is consistent at optimum levels, it can quickly become counterproductive, costly, and even wasteful if the demand for work is uncertain.

For example, a commercial bakery could purchase one large capacity mixer that could produce 100,000 loaves for far less cost per loaf than two smaller mixers. The large mixer produces large batch sizes; that’s how it gets its great efficiencies. But if the market is looking for variety, none of which is ordered in bulk, the large mixer results in the worst of both worlds: you either produce large batch sizes and have a lot of scrap if the demand does not materialize in time, or you waste the purchased capacity by preparing batch sizes more closely tied to current demand for the product variety. Either way, you can never really produce enough variety for the market, because the equipment produces only one variety at a time.

So like many things in life, when we find ourselves needing to research the marketplace, assess root causes, or study work processes, we must beware of both confirmation bias and its kin conventional wisdom, lest we make sub-optimum (or worse!) choices that feel good at the start but come back to bite us in the end.

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.

Is Bigger Better?

Is Bigger Better?

Conventional wisdom holds that bigger is better, and there are numerous factors that support this perspective. Consider that when you’re an organization or a business and you’re doing better, then you’re going to get bigger — so, therefore, bigger is better!

And certainly issues such as bulk buying discounts or other economies of scale favor size.

But, the tumultuous forces of an uncertain world might just favor agility. Consider that while a cost/benefit analysis will typically reveal that unit cost is much lower for a single large-capacity piece of equipment than for several smaller machines, rarely does that analysis incorporate the impact of uncertainty and variation on total cost; and, in reality, variation in either market demand or supply (such as machine downtime), or the relative inflexibility of a single large capacity machine can drive inefficiencies that greatly offset the lower unit cost that was calculated when a purchasing decision was made.

For example, a commercial bakery could purchase one large capacity mixer that could produce 100,000 loaves for far less cost per loaf than two smaller mixers. The large mixer produces large batch sizes; that’s how it gets its great efficiencies. But if the market is looking for variety, none of which is ordered in bulk, the large mixer results in the worst of both worlds: you either produce large batch sizes and have a lot of scrap if the demand does not materialize in time, or you waste the purchased capacity by preparing batch sizes more closely tied to current demand for the product variety. Either way, you can never really produce enough variety for the market, because the equipment produces only one variety at a time.

Capacity to produce must be as flexible as the market is variable and dynamic. Often this runs directly counter to economies of scale. Optimizing the machine-cost-per-unit can sub-optimize the profitability of the process as a whole.

So, ultimately, to effectively answer the “is bigger better” question, we must go well beyond conventional wisdom or what we assume to be true, and instead consider a wider range of variables. Possibly this well-known quote, sometimes attributed to Mark Twain but also to Josh Billings, sums-it-up best: “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”

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.

Read the full article…