Improved Output in Modular Function Deployment Using Heuristics

Abstract

In Modular Function Deployment, technical solutions are grouped into modules according to the product properties and the strategic intentions of the company. Statistical methods such as hierarchical clustering are useful in the formation of potential modules, but a significant amount of manual adjustment and application of engineering common sense is generally necessary. We propose a method for promoting better output from the clustering algorithm used in the conceptual module generation phase by adding Convergence Properties, a collective reference to data identified as option properties, geometrical information, flow heuristics, and module driver compatibility. The method was tested in a case study based on a cordless handheld vacuum cleaner.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.