Tag: demand driven
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What is sales history, exactly?
We often talk about the sales history of a product, so let’s explain exactly what it means. There is a raw sales history and a collapsed sales history. The sales history, raw or collapsed, is the starting point for all the analytics we will be introducing later.
Raw sales history
A product is a collection of features, where each feature has a set of mutually exclusive options (one of which may be “no,” “none” or “none of the above”). A sales history consists of a record for each unit of the product that has been sold, with a list of the options that were included. Since each record is for a specific unit, there may be a serial number feature. So imagine a table with a row for each unit sold and a column for each feature. The entries in a column are the different option choices for the corresponding feature. Blank cells indicate a “none” choice.
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The quality connection
Recently I wrote about quality rankings for automotive manufacturers and the perception of these rankings in the market. While the marketing teams at these companies must shoulder the burden to convince consumers about their products’ quality, there is a very real connection between product quality and configuration management.
In many industries where products have grown over time with constant additions of new features and flexibility to allow customers to build to order, the level of complexity is staggering. Often the number of configurations sold on an annual basis is surprisingly close to the total units sold for that same period. This “snowflake” situation is one of the worst possible scenarios in product complexity as each unit has its own signature. Obviously, the production of these products also requires flexibility in manufacturing. This may result in reduced use of automation, and often it leads to units being reconfigured where components installed during one step are either removed or modified in a later step due to a unique situation.
These one-off manufacturing processes open the door for product quality issues due to fewer controls during production. Put simply, if I can reduce the number of different things that must be done during production I should be able to do those things better.
So product management teams have direct input on product quality via product complexity. Managing the product option mix to reduce the overall number of configurations can promote the increased quality that all manufacturers are looking for.
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Help the sales team help the customer
This morning I was talking to the VP of business process improvement for a company that sells industrial machinery. Their products are highly configurable. She told me that every year they have 50% new configurations they have never seen before. The number of choices on their products has grown over time. ”A salesperson can’t know everything about the product,” she said. “Customers want a few choices, and before you know it, the quote has crept into a configuration that’s bad for the customer and bad for us. “
As the VP explained, the biggest opportunity for complexity management is at the point of taking an order. A customer wants to be guided to complete their order. This concept is called Demand Shaping. There are myriad ways a configurable product can be ordered. However, each customer cares only about a few features that are of high importance to him or her.
Understand product choices to manage complexity
A product is a collection of features, and each feature has alternative options. Understanding features helps determine strategies such as late staging. Some features are tangible, material things about the product: which engine, how much memory, Bluetooth. Other features are abstract or soft, like geographic region or sales channel. Among tangible features, distinctions can be made on the basis of the degree of postponement possible.
Pin-on features can be added to the product at the last minute, after a specific order is received. The classic example is the power cord for a printer. Hewlett-Packard avoided having different printers for different countries by attaching different power cords to a common printer.
Reconfigurable features can be changed after a real customer order is received. There are literally hundreds of different kinds of tractor tires, depending on the work a tractor will be used for. A tractor has to be built with some kind of tire just so it can be driven off the assembly line, and it’s easy to change the tires to suit the customer.
Line features, by contrast, are so basic to the product that they can’t be changed, such as the chassis or transmission for a vehicle or the motherboard for a computer.
Abstract or soft features are really attributes of the order rather than of the product itself. But they may be very valuable in understanding customer demand. The pattern of choices for tangible features may vary considerably by geographic region, which is a soft feature. For example, engine block heaters are popular in North Dakota and convertibles are popular in Florida, but convertibles with engine block heaters are almost non-existent.
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Extending the product configuration to gain insight
One of the most important components in choice complexity is the product configuration itself, the mixture of product options that give a product its unique signature. Obviously the typical product orderable options are needed to analyze the complexity of a product, but other more abstract options can offer surprising insights into product and customer behaviors.
A typical car configuration has options such as sedan, V6 engine, automatic, blue, cloth, AM/FM/CD, sunroof. But more abstract items can be recorded along with these to offer more insight. Sales type can be recorded to analyze what types of product configurations sell better in promotional sales events as opposed to normal sales transactions. An attribute to record an extended factory warranty option may provide new ideas for packaging options together with additional warranty services that customers are moving towards.
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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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When the tide goes out, it exposes products that were under water
The number of companies with complexity reduction initiatives has skyrocketed. Unlike five years ago, these are serious initiatives with management sponsorship and timelines.
A good friend of mine, who is a salesperson at a Caterpillar dealership, told me that when times are good he can sell any machine. When the times are bad, the bad stuff just sits around exposed.
Companies have proliferated their product offerings – there are almost infinite variations of everything that they offer. The rationale is that they will make one more sale because of that variation. But as product variations grow, the cost structure grows very fast as well, and the probability of finding that one customer who wants the new variation is quite slim. This results in excess inventory across the supply chain. And when the economic tide goes out, it exposes the cost of those product variations.
The companies with complexity reduction initiatives recognize that during good times and bad, managing product variants makes good business sense. Companies are now starting to implement metrics to measure product complexity because we all know that what gets measured gets managed! Product complexity metrics quickly expose underwater products.
The comment by my friend at Caterpillar reminded me of a trip I took to the Bay of Fundy. It is amazing how much is exposed when the tide really goes out, just like in this economy. The good news is that when the tide turns, the bad product lines it once covered will be significantly fewer, resulting in healthier and more competitive companies.
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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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Key Concepts To understanding Product Variety
1. Product
A product is something offered for sale to customers. This is deliberately vague, because we want to encompass services as well as tangible products. Most of our discussion and examples involve manufactured products, but our framework also applies to services with many variants like insurance policies and cell phone calling plans.
2. Instance
An instance of a product is a specific unit of the product: the car that Joe buys, which has a specific VIN (Vehicle Identification Number).
3. Configurable Product
A configurable product is a product where the instances are not all identical. No. 2 pencils are not configurable. Computers, cars, tractors, refrigerators and cell phones are configurable.
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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