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8 more definitions you need to know for product complexity analysis
1. Kit
A kit is a collection of parts that are used together for some purpose — for example, all the parts needed to implement air conditioning on a particular model of a car. A kit is assigned its own part number.
2. BOM
BOM stands for bill of materials. When a customer makes a selection of choices chooses a configuration (i.e., makes a complete set of option choices), the manufacturer translates the order into a collection of parts that are needed to assemble it. The BOM is expressed in terms of part numbers. These part numbers may refer to whole kits, composite parts or specific atomic parts. A complete vehicle, or washing machine, will contain many parts that the customer has not chosen. But these parts appear in every instance, or else they are implied by the combination of choices that the customer made.
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Product management to fit?
I often hear clients say, “We’ve been told by management that we should only have a standard and premium model.” Then product teams are tasked to fit the line to that limitation, while no product analysis was done to support the decision.
While reduction in configurations is important, how it is achieved is just as important. Using a quantitative approach earlier in this decision-making cycle can help the management team make an informed decision. Maybe a reduction to two models is just too aggressive; adding one additional model might be the difference in having proper market coverage. Examination of customer buying trends and patterns should be the first indicator for product direction. Determining product strategy based on market needs as opposed to retrofitting the product to an idea could lead to more effective product lines for both the manufacturer and consumer.
Optimization is the big win – but getting started is key
When I started studying complexity and realized the huge adverse impact it was having on companies, I was determined to “find it and get rid of it.” There are many places where that formula will lead to big improvements in everything – profits, service, quality and more. More and more companies are discovering how to do this. In some cases it is pretty simple. Just having the courage of their convictions that it will make things better is all that stands in the way of eliminating complexity.
Well, I found that is not completely true – at least not all the time. There are some situations where what seems to be a simple complexity elimination process turns out to be quite a bit more… complex! The real issue is not just complexity reduction. It is “optimization” of complexity. Get rid of the wasteful part and structure processes to use the right level of complexity.
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The Root Cause of Product Complexity!
Emcien defines product complexity as simply the ability to predict what the next order coming into the company will be.
Think about it: If you only made product configuration A, you have 100% confidence in knowing that the next order in the door will be configuration A (assuming you get an order in the door at all, not a total given in this economy). But if you have configurations A and B, it’s harder to know and with A, B and C, it’s even harder, and so on. When you have thousands of configurations, predicting the next one is very difficult.
It’s not just the number of configurations that’s important but also how they’re distributed. If I have 10 configurations but 90% of my orders are for config A, then it’s still safe to predict that the next order is config A. But having 10 configs that have each been ordered 10% of the time is extremely complex!
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Why product complexity matters
I was telling some friends at a brunch about what I do, and how variety drives cost in manufacturing. “But all the manufacturing has moved to China,” commented one person. I’ve heard this comment over and over.
A picture is worth a thousand words — and here’s one that fits the bill.
- Commoditization of labor in manufacturing
- Higher output per worker
- The percentage of cost in goods is much higher
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