Data mining in design of products and production systems
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
References (31)
- et al.
Aggregation of orders in distribution centers using data mining
Expert Systems with Applications
(2005) - et al.
Modular product architecture
Design Studies
(2001) - et al.
Mass customization: Literature review and research directions
International Journal of Production Economics
(2001) - et al.
Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem
Nonlinear Analysis: Real World Applications
(2006) - et al.
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications
(2005) - et al.
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems
(2003) - et al.
Enabling customer relationship management in ISP services through mining usage patterns
Expert Systems with Applications
(2006) - et al.
A knowledge-enabled procedure for customer relationship management
Industrial Marketing Management
(2006) - et al.
A heuristic method for identifying modules for product architectures
Design Studies
(2000) - et al.
A data-mining based methodology for the design of product families
International Journal of Production Research
(2004)
Ingénierie de l’innovation: Organisation et méthodologies des entreprises innovantes
Innovation: The five disciplines for creating what customers want
Developing design templates for product platform focused design
Journal of Engineering Design
Data mining for improvement of product quality
International Journal of Production Research
From “future perfect”: Mass customizing
Planning Review
Cited by (85)
Data science for engineering design: State of the art and future directions
2021, Computers in IndustryCitation Excerpt :Based on their performance characteristics, new designs’ performance can be predicted through data mining and machine learning techniques (Bertoni et al., 2017). Another application is the reduction of existing product complexity, that can be achieved using a greedy modularity algorithm (Kusiak and Smith, 2007). Alongside the technical performance, also cost models contribute to design evaluation.
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions
2019, Journal of Cleaner ProductionCitation Excerpt :A comprehensive analysis of DM applications in manufacturing and product quality improvement was conducted by Choudhary et al. (2009) and Köksal et al. (2011). Recently, the applications of DM in different lifecycle stages, such as product design (Kusiak and Smith, 2007), production (Cheng et al., 2018a), maintenance (Bennane and Yacout, 2012), fault diagnosis (Sim et al., 2014), service (Karimi-Majd and Mahootchi, 2015), and recycling (Y. Wang et al., 2016) were implemented. The DM has also been attractive to many researchers on implementation of sustainable production and consumption strategies in manufacturing.
Unveiling the features of successful eBay smartphone sellers
2018, Journal of Retailing and Consumer ServicesCitation Excerpt :Within business applicability, increasing customer intelligence, improvement of operational efficiencies and customer customization are only some of the broad possibilities for data mining (Pal and Saini, 2014). Data mining has been used for modeling tourist hotel scores (Moro et al., 2017), designing of products and information systems (Kusiak and Smith, 2007), predicting bank telemarketing successful contacts (Moro et al., 2015a) or measuring social media performance (Moro et al., 2016). Other examples include the application to e-learning domain (Hanna, 2004), customer response to direct mailing (Coussement et al., 2015), credit risk assessment (Moro et al., 2015b), or for discovering the helpfulness of online reviews (Lee and Choeh, 2014).
UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices
2016, Computers and Industrial EngineeringDATA-DRIVEN DESIGN in CONCEPT DEVELOPMENT: SYSTEMATIC REVIEW and MISSED OPPORTUNITIES
2020, Proceedings of the Design Society: DESIGN ConferenceDesigning dual ontological products for human factors: a machine learning and harmonistic knowledge-based computational support tool
2023, Journal of Engineering Design
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.