Order clustering method during product planning

Abstract

The invention provides an order clustering method during product planning. The method comprises the steps of standardizing sample data, establishing a fuzzy similar matrix, calculating fuzzy clustering transitive closure of the fuzzy similar matrix, choosing confidence level values suitably to finish clustering, conducting membership calculation to a series of standard values or standard intervals, determined by manufacturers and corresponding to all attribute values of each class of orders, of an attribute, regarding the largest value or the interval of the membership as the attribute value in the class of the orders, and conducting satisfaction calculation of clients in the class of the orders. The order clustering method during the product planning can greatly reduce time of product clustering, improve efficiency of the product planning, process the order clustering accurately and fast, improve satisfaction of the clients, and lower production cost.

Description

technical field [0001] The invention relates to an order clustering method, in particular to a fuzzy clustering method that shortens the entire product planning time without affecting the overall product planning, and satisfies customer needs to the greatest extent to carry out the next product matching work. Background technique [0002] Cluster analysis is to input a set of unmarked record sets, that is, the input records have not been classified in any way, and then divide the record set reasonably according to certain rules and methods. In other words, the so-called clustering is the process of dividing a group of objects into several categories according to certain attributes of things, so that the object elements whose similarity is greater than the set value are in the same group, and the object elements smaller than the value are distributed in different groups. . Similarly, the clustering of customer needs refers to the customer needs obtained through various metho...

AI Technical Summary

Problems solved by technology

[0004] It can be seen from the above process that the existing product planning does not have the process of clustering, which increases the production cycle of the product virtually, reduces the production efficiency of the product, and at the same time reduces the benefit of the enterprise. Difficulty meeting customer needs on time

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