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Design for X (DfX) is commonly known as Design for Excellence. It is a guideline for product development and improvement that can be applied in the four phases of a product life cycle - introduction, growth, maturity, and decline.
The 'X' in DfX refers to the different aspects of product development such as engineering, manufacturing, assembly, stowability, etc. The 'Design' refers to designing the products to meet the various characteristics and challenges of these aspects.
DfX is useful in controlling and improving the product's final characteristics, hence providing value propositions of product cost, quality, flexibility and reliability to customers.
I feel below journal articles great for learning DfX applications in Additive Manufacturing and Supply Chain:
Thanks for your good points.
Great response and very useful.
Thanks for sharing. Very interesting as I have a friend who is currently developing a new product and has had several issues with design houses in formulating certain aspects including methods of manufacture. Based on his experience with two such design houses, it seems there are many designers/ engineers out there who really don't know what they really should know based on their claims and hourly rates. He has already invested substantial funds only to be told by another firm he needs to start all over.
In may case, working inside an organization that is using DfX, I like to say that is nothing new below the sun. What @Pang stated above is the basement. The problem is along the years something critical is missing: the two layers involved wich are product desing and engineering desing.
The one challenge I perceive with D4X is that it focuses on optimizing one specific measure or criteria. The hard work in product design is figuring out how to balance multiple important dimensions and come up with the best set of features which addresses all dimensions.
What is the difference between Design for X [DfX] and Design of experiment [DOE]
DOE is design for a specific output variable which is dependent on multiple input variables, by trying to pick the specific inputs to get the desired output. It's really trying to fit an equation to a dataset.
In DOE, you will perform experiments where you measure your output against individual inputs to try and learn their relationship. Then the relationships between the output and the individual inputs are combined to try and pick the right inputs to get the right output, and then test your chosen combination of variables against the desired result. That can be a much faster way to try and pick multiple variables than an exhaustive set of experiments varying multiple things at a time.
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