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Value Engineering / Analysis

Value Analysis or Value Engineering is a function-oriented, systematic approach which can be used to analyze and improve value in any product, software or service. 


The classical philosophy of value was developed from 1947 on wards by Larry Delos Miles at General Electric. His book Techniques for Value Analysis and Engineering was published in 1961.

In the Value Model we have used this classical philosophy as our basis for moving forward. Our aspiration has been to produce a practical working model with customer value as the steel thread binding everything together. A requirement has been that the method can be easily applied in all types of development projects.


We have also integrated different tools such as Triz to get a more holistic and powerful framework.

Functional Model

Functional model of a technological product

The key is how to create a schematic drawing or a model describing the major functional elements in a product, process or service and the functions carried out by these elements along with other relevant factors affecting the value. This task is more difficult than you may realise and practice is required before the art can be mastered. Two of the most common problems are:

  • finding the right level for formulating the functions

  • trying to include the logical sequence in which functions are to be carried out.


Some also argue that then making your functional model you should start with a white paper and not look on the existing solution. Example of such methodologies are for example FAST-diagrams. We on the other hand think this is principally wrong in most cases. Only when you are moving to a new S-curve is this the correct strategy. In all other situations, the key to success is to increase customer value without making any major technical changes in your existing solution. Making large changes in any technological system always increases the complexity, cost and risk of the development project and is a restart on the learning curve. All those factors combined, could by the end of the day jeopardize the value increase created on paper. We have seen numerous examples of this.


A powerful strategy in most product development projects is to maximize the number of components and sub-systems that can be carried over into the next product. If your start playing around with everything is like playing with fire.


Goal – maximum value increase with minimal changes. First if you tried this strategy and can prove it is not a viable strategy should it be abandoned.

Start instead by creating a benchmark which is a combination of your product, the best elements of your competitor’s products and other the state of the art solutions. The benchmark is the best the world can do today with existing and proven technology. The benchmark is your fall back alternative. If everything goes wrong this is the product you are going to make.


The benchmark constitutes the reference to be used for quantifying customer value and is also the starting point for the functional tactics required to attain unrivalled customer value. Main, additional and unwanted functions as well as costs therefore need to be formulated. A functional model of the benchmark displays the most important subsystems that:

  • contributes to creating and destroying customer value

  • make up the majority of cost for production, distribution, operation, maintenance and service as well as decommissioning

  • consumes resources from the organization for example due to low quality or high warranty.


Functional models can also be made at different abstract levels and deeper studies must sometimes be carried out to achieve a more physical perspective when working to improve customer value within a product. It may be helpful to limit such an in-depth model to just a few elements. Compare it to observing something under a microscope: a different picture is presented by an in-depth definition. Sometimes you may even have to enlarge the picture to the subatomic level to gain the in-depth insight required for finding innovative solutions to the problem. 


Finally strive to produce simple models that focus on the most important functional elements involved in the problem you are working with.

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