Tag: build combinations
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Stairway to (Product) Complexity (a.k.a. Why Do I have SO Much Stuff!!!)
In the last post I introduced two ideas about the sales history of any product. First, the number of unique configurations, or build combinations, depends on which features are included in its description. Second, the number of unique combinations drops whenever a feature is removed. A natural question to ask at this point is: Which feature, if it were removed, would lead to the greatest decrease in the number of unique configurations?
This is an easy question to answer, if we have a way of counting the number of unique configurations in any input set of configurations. This is really just a matter of sorting, so suppose we have such a counting algorithm. We try removing the features, one at a time. In each case we apply our counting algorithm to get a score. The score is the number of surviving unique configurations. The feature with the lowest score is the winner (like golf).
Now suppose that we permanently remove the winner, and repeat the contest again. This will determine a second winner, which can also be removed. We keep repeating until there are no features left. Now imagine a graph with the number of features removed on the x-axis and the number of surviving unique configurations on the y-axis. This is the stairway to complexity.
The stairway shown above is for a product (commercial stoves) with 23 features. The number of unique configurations starts at 1223 and drops to just 1 as the features are removed. The features are removed by looking for the biggest drop at each step.
Abstract:
Product complexity is driven by large number of options. Companies struggle to determine which feature choices are driving complexity. They typically “randomly cut choices” to streamline and rationalize SKUs. The cost of product complexity is tremendous on engineering. The current PLM systems do not have a method to measure this and provide intelligent feedback to engineers on how to standardize platforms to reduce engineering and maintenance costs. This article clearly details the metrics around product complexity and how to solve this issue.
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The Number of Choice Combinations Depend…
The number of choice combinations depend on which product features are included. The build combinations is the product mix or the marketing mix.
Let’s consider the sales history of our product. There are two very important numbers: the number of units sold and the number of unique configurations. The number of units is well defined, but the number of unique configurations is ambiguous. The ambiguity comes from the fact that there will be more unique configurations if we use more features, especially soft features, to describe our product.
One very special soft feature is a Serial Number, or VIN (Vehicle Identification Number). The whole purpose of the Serial Number is to make each instance of the product unique. So if we look at our sales history and include Serial Number, we will see that the number of unique configurations is exactly the same as the number of units of the product (instances).
If we want to begin to understand the demand for our product we have to see which instances are actually the same. That means we have to get rid of the Serial Numbers. When we do, the instances collapse into groups of now unique configurations; that is, unique without Serial Number.
If we are interested in the tangible features of the product, then we may want to take out other soft features as well. Geographic region is important for some purposes, but may be a distraction when we are interested in the physical product. Taking out the geographic region feature will cause another reduction in the number of unique configurations. The red, V8, convertible in Florida will get combined with the red, V8, convertible in New York.
Sometimes we are interested in the variants of our product ignoring color. We know that every real variant is going to come in several colors, but we want to look at the product without the distraction of color. This is sometimes called the “body in white”. So the red, V8, convertible and the green, V8, convertible collapse into the V8, convertible.
The point I am making is that the number of unique configurations depends on which features are included, and this number drops whenever a feature is taken away. Mathematically, this is called “projecting out the feature”.
The number of unique configurations is at most the number of units sold, and at a minimum it is just one. If we take away all of the features, then every unit looks the same, which means just one configuration. There is a path from one extreme to the other that we will introduce next time.
By the way – understanding this is important as product complexity is a key driver of process complexity.
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How many choice combinations does your product have? That depends.
Possible combinations
This is a question with several answers. The easiest answer is the least useful. The number of possible build combinations, or unique configurations, is easily computed by multiplying the number of options for each feature. For example, if your product has feature A with 3 options, feature B with 2 options and feature C with 4 options, then there are 24 (3 x 2 x 4) possible build combinations.
These numbers grow very rapidly. If you have 5 features, each with 4 options, there are about 1,000 build combinations (exactly 1,024). With 10 such features, the number of combinations is about 1 million (1,048,576), and with 15 features it is over 1 billion (1,073,741,824).
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