Data Mining and Fuzzy Clustering to Support Product Family Design

2006 ◽  
Author(s):  
Seung Ki Moon ◽  
Soundar R. T. Kumara ◽  
Timothy W. Simpson
Keyword(s):  
Data Mining ◽  
Mass Customization ◽  
Product Family ◽  
Knowledge Based ◽  
Product Function ◽  
Fuzzy C Means Clustering ◽  
Large Product ◽  
Functional Features ◽  
Using Data

In mass customization, data mining can be used to extract valid, previously unknown, and easily interpretable information from large product databases in order to improve and optimize engineering design and manufacturing process decisions. A product family is a group of related products based on a product platform, facilitating mass customization by providing a variety of products for different market segments cost-effectively. In this paper, we propose a method for identifying a platform along with variant and unique modules in a product family using data mining techniques. Association rule mining is applied to develop rules related to design knowledge based on product function, which can be clustered by their similarity based on functional features. Fuzzy c-means clustering is used to determine initial clusters that represent modules. The clustering result identifies the platform and its modules by a platform level membership function and classification. We apply the proposed method to determine a new platform using a case study involving a power tool family.

A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

A Market-Based Design Strategy for a Universal Product Family

2012 ◽  
Vol 134 (11) ◽  
Author(s):  
Seung Ki Moon ◽  
Daniel A. McAdams
Keyword(s):  
Mass Customization ◽  
Universal Design ◽  
Product Family ◽  
Cost Effective ◽  
Differentiated Products ◽  
Product Family Design ◽  
Design Strategies ◽  
Market Segments ◽  
Light Duty

Companies that generate a variety of products and services are creating, and increasing research on, mass-customized products in order to satisfy customers’ specific needs. Currently, the majority of effort is focused on consumers who are without disabilities. The research presented here is motivated by the need to provide a basis of product design methods for users with some disability—often called universal design (UD). Product family design is a way to achieve cost-effective mass customization by allowing highly differentiated products serving distinct market segments to be developed from a common platform. By extending concepts from product family design and mass customization to universal design, we propose a method for developing and evaluating a universal product family within uncertain market environments. We will model design strategies for a universal product family as a market economy where product family platform configurations are generated through market segments based on a product platform and customers’ preferences. A coalitional game is employed to evaluate which design strategies provide more benefit when included in the platform based on the marginal profit contribution of each strategy. To demonstrate an implementation of the proposed method, we use a case study involving a family of light-duty trucks.

scholarly journals Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

2020 ◽  
Keyword(s):  
Data Mining ◽  
Customer Segmentation ◽  
Food Distribution ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Marketing Activities ◽  
Using Data ◽  
Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

Resolving the Paradox of Overconfident Students With Intelligent Methods

2018 ◽  
pp. 1971-1986
Author(s):  
Denis Smolin ◽  
Sergey Butakov
Keyword(s):  
Data Mining ◽  
Classification Tree ◽  
Classical Test Theory ◽  
Test Theory ◽  
Course Content ◽  
Data Mining Algorithms ◽  
Closing The Loop ◽  
Using Data ◽  
Intelligent Methods

The chapter presents a case study of using data mining tools to solve the puzzle of inconsistency between students' in-class performance and the results of the final tests. Classical test theory cannot explain such inconsistency, while the classification tree generated by one of the well-known data mining algorithms has provided reasonable explanation, which was confirmed by course exit interviews. The experimental results could be used as a case study of implementing Artificial Intelligence-based methods to analyze course results. Such analyses equip educators with an additional tool that allows closing the loop between assessment results and course content and arrangements.

Detection of Desertion Patterns in University Students Using Data Mining Techniques: A Case Study

2018 ◽  
pp. 420-429
Author(s):  
Dayana Vila ◽  
Saúl Cisneros ◽  
Pedro Granda ◽  
Cosme Ortega ◽  
Miguel Posso-Yépez ◽  
...  
Keyword(s):  
Data Mining ◽  
University Students ◽  
Data Mining Techniques ◽  
Using Data
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