Data mining in design of products and production systems

https://doi.org/10.1016/j.arcontrol.2007.03.003Get rights and content

Abstract

Data mining is acquiring its own identity by refining concepts from other disciplines, developing generic algorithms, and entering new application areas. Engineering design and manufacturing have been affected by the data mining pursuit. This paper outlines areas of product and manufacturing system design that are particularly suitable for data-mining applications. One of the emerging areas is innovation. The key challenges of data mining in the domains discussed in the paper are outlined.

Introduction

Corporations are interested in innovative ways of conducting their business. Some innovation can be attributed to the growing use of data in design and manufacturing.

Traditionally, the flow of data and information in design and manufacturing systems has been essentially unidirectional as illustrated in Fig. 1.

Any local bidirectional flow (loops) of information has often been attributed to imperfections of the process, e.g., design negotiation, manufacturing errors. The developments in networking, data warehousing, and data mining have contributed to the emergence of the closed loop system illustrated in Fig. 2.

Products and components generate a data trail across life-cycle phases such as market analysis, design engineering, manufacturing, and service. Data-mining algorithms extract knowledge from this large volume of data leading to significant improvements in the next generation of products and services. In fact, the knowledge discovery activity could become the key factor to innovation and business success.

The basic capabilities of data analysis tools are outlined next.

Important patterns might be hidden in the industrial data. For example, data mining applied to the customer domain may reveal answers to questions such as

  • What characterizes frequent buyers?

  • What characterizes customers who react to promotions?

  • What characterizes customers making quick purchase decisions?

  • What characterizes customers who do not purchase?

Most database systems, such as MS-Access and Oracle, provide some query capabilities providing answers to some of these higher-level questions. However, for in-depth analysis, data-mining algorithms are needed (Witten & Frank, 2005).

Industrial companies are increasingly developing data warehouses to collect business data. Data-mining algorithms cannot only extract the static patterns in data, but can also discover dynamic trends. Mining time series is an active research area (Kusiak & Song, 2006). The trends reflect customer interest shifts, technology development, and the response to marketing strategies.

Modern databases may contain large number of rows (transactions) and columns (features). An important research area is the concept of dimensionality reduction. Unrelated data items and features can be eliminated from the dataset to reduce the data-mining effort.

Visualization tools enhance human understanding of data. For example, graphs, charts, and tables make information easier to understand than the original data. The relationships between different data items become obvious when they are displayed. To make full use of the data visualization tools, data and knowledge are needed.

Section snippets

Knowledge discovery

There are two general classes of data mining, descriptive and predictive. The goal of descriptive data mining is to discover patterns, e.g., product configurations formed in mass customization applications. The predictive data mining aims at building models to determine (predict) an outcome, e.g., a stock level. Since the width of data analyzed by the data-mining algorithms is essentially unlimited, the patterns discovered are usually not anticipated and are of interest to different users. The

Data-driven design

Engineering design has been lagging in the development of data mining; however, the potential for benefits is significant. Product complexity reduction and modularity are two of many potential examples discussed next.

Increasing the modularity among products is a common goal for many companies. Some of the benefits of modularity include the potential for (Chandrasekaran, Stone, & McAdams, 2004; Kusiak & Huang, 1996):

  • economy of scale,

  • increased feasibility of product-component change,

  • increased

Mass customization

Mass customization is defined as permitting “customized manufacture on a mass basis” (Davis, 1989). According to Da Silveira, Borenstein, and Fogliatto (2001), there have been three drivers of mass customization. The first was due to the advent of flexible manufacturing and information technologies that enabled production systems to deliver a higher variety of products at lower costs. The second was due to the fact that consumers are constantly increasing their expectations for product variety

Supply chain management

A supply chain is a contractual linkage among various parties ideally to achieve a “just-in-time” flow of goods. The purpose of the supply chain designer is to quickly generate the electronic trade scenarios. Supply chain management involves the adoption of electronic linkages between two businesses that are related as supplier/customer within a single industry channel or supply chain (Westland & Clark, 1999).

Data mining is a powerful tool for supply chain management, especially in the

Concept introduction

Recent years have brought about a renewed interest in innovation, especially after the Innovate America Report (NIIR, 2004) was published. Though innovation has been a subject of intensive studies by diverse research communities, many will agree that the results produced have not translated into meaningful innovation gains in the industry (Boly, 2004, Carlson and Wilmot, 2006). Rather, industry is awaiting methodologies, processes, and tools leading to innovation breakthroughs.

It appears that

Conclusion

Although numerous successful applications of data mining in design and manufacturing have been reported, many challenges are ahead. Some challenges come from the data mining itself and others come from the application domains. The main challenges are as follows:

  • Make greater use of unstructured information. Most data-mining algorithms have been designed to process numeric or textual data. Design and manufacturing systems could provide other forms of data, e.g., geometry, audio, and video. The

Dr. Andrew Kusiak is a Professor in the Department of Mechanical and Industrial Engineering at the University of Iowa in Iowa City, Iowa. He is interested in applications of computational intelligence in automation, energy, manufacturing, product development, and healthcare. Dr. Kusiak has published numerous books and technical papers in journals sponsored by professional societies, such as AAAI, ASME, IEEE, IIE, ESOR, IFIP, IFAC, INFORMS, ISPE, and SME. He speaks frequently at international

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    Dr. Andrew Kusiak is a Professor in the Department of Mechanical and Industrial Engineering at the University of Iowa in Iowa City, Iowa. He is interested in applications of computational intelligence in automation, energy, manufacturing, product development, and healthcare. Dr. Kusiak has published numerous books and technical papers in journals sponsored by professional societies, such as AAAI, ASME, IEEE, IIE, ESOR, IFIP, IFAC, INFORMS, ISPE, and SME. He speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations. Dr. Kusiak has served on editorial boards of over 35 journals. He is the IIE Fellow and the Editor-in-Chief of the Journal of Intelligent Manufacturing.

    Mathew R. Smith is a graduate student in Industrial Engineering in the Department of Mechanical and Industrial Engineering at the University of Iowa, Iowa City, IA. He has obtained a BS degree in Industrial Engineering from the same department and is interested in applications of operations research in engineering design and manufacturing. He is a member of the Intelligent Systems Laboratory.

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