Application of data mining to manage new product development and innovation Prof. Huang Tai-Shen, Chang Chia-Fang Graduate Institute of Design, Chaoyang University of Technology, Taiwan Abstract Enterprises are realizing how important it is to "know what they know" and be able to make use of the vast amounts of knowledge in the recent years. Technologies of knowledge management such as data warehousing, data mining apply to product innovation that can gain a competitive advantage. Particularly through data mining that the extraction of hidden predictive information from large database can identify valuable customers, predict future market, enhance product innovation efficiency and enable firms to make knowledge-driven decisions. The research provides the structure of data mining to discovery the inflow resources of product innovation that includes various hidden knowledge. When the product of life-cycle is shortening, manufacturers and designers should reduce cost to keep competitive advantages. It is very important to develop effective methods and tools for product design. The purpose of data mining is to search useful data and to support making right decisions. The research focuses on building the structure of data mining to fit new product innovation and development, and adopts the decision tree model to predict trends easily. Keywords: Data mining, product design process, Industrial Design Inflow of resources: This paper 1. Introduction focuses on discovering data about This research integrates an inflow of inflow of new product resources, data about new product development including customer, product, and and innovation by using data mining market (see figure 2.). The product technology. At first, this paper describes resources come from competitive the structure of data mining to manage activities, comparing with competitors new product innovation (see figure 1). and searching competitive sources What kind of innovation could result in (customers need) to find the core new product? It is very important to competitive opportunity. determine the resources of data mining. Strategic planning: The step focus on Product innovation process includes special opportunity-analyzing. The six steps: process of creatively recognizing 1
Defining problems Building databases Inflow of resources Analyzing and searching rules Strategic planning Building models Concept generation Applying data mining to product innovation Strategic evaluation Evaluating models Technical development Data mining process Commercialization Product innovation process Figure1. Data mining process vs. product innovation process opportunities is called opportunity identification. Concept generation: The most fruitful ideation involves identifying problems and suggesting solutions to the strategic planning. Strategic evaluation: Strategic evaluation is the stage that the ideas coming from the concept generation activity are evaluated. Strategic evaluation uses a scoring model of some type and results in a decision to either undertake development or quit. Technical development: An inventory is taken of the firm s operations skills. Commercialization: Traditionally, the term commercialization has described that time or that decision where the firm decides to market a product [4]. 2. Data mining Mining means to find something that already exists. Data mining is defined as a process of identifying hidden patterns and relationships within data. [1]. The objective of this process is to extract through large quantities of data and discover new information. The benefit of data mining is to turn data into actionable results [16]. 2
Market: Customer: Competitive advantage C2 C1 C Product: Competitor C3 Core competitive C1: product-oriented database C2: customer-oriented database C3: market-oriented database C: core competitive opportunity Figure2. The core competitive opportunity resources 2.1. Data mining process Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data in order to make valid predictions. The research builds the structure of data mining process including six steps: (see figure 3.): Define the problem: This accurately decides the form of input and output, to decide cost effectiveness. In product innovation, there are three defined categories: customer, product, and market (see figure 2). Building database: Building database means discovery of data dependencies. In the relational data model, the definition of the relations is about the relationship among their attributes. The attributes come through four steps: defining problem, selecting data, building model, and selecting models (See figure 4). Analyzing and searching rules: The rules mean how to classify the attributes. Building models: This includes model developing (or knowledge patterns extracted). Applying data mining to product innovation: Searching product data from the database which consists of inflow of resources (the first step of product innovation process). Evaluating model: estimating how well a particular pattern meets the criteria of the data mining process. Therefore, evaluating model reflects whether the strategic planning is in place. The cycle of evaluating model builds feedback to support product innovation. 2.2. Building database The selected data is the base of building integrated database. After building database, the selected data must be analyzed to build models. In the product design, the database needs many formal models to apply data mining to new product innovation. Why is building database so important? Data mining is to reason hidden data, to predict, and to support decision-making. We describe the relation of database to data mining. Building database: Database is an emerging approach for effective decision support. 3
Inflow of data Inflow of data Feedback 1. Defining problem 2. Selecting data 3. Building models 4. Selecting models Visualization Pattern mined Users Purpose: 1. Decision-making 2. Prediction Building database Figure3. The relation of database to data mining Visualization: Data visualization graphically represents the structure that exists among data sets. Users: data mining should benefit human users. Prediction: Prediction involves using some variables or fields in the database to predict unknown or future values of other variables of interest. 3. Decision tree Decision-tree is a common knowledge representation used for classification. Decision-tree approaches are good for handling classification problems. Classification is the process of using historical data to build a model for the purpose of understanding and prediction [17]. Many algorithms and techniques of data-mining have been developed. These algorithms include neural network, fuzzy theory, and decision tree, etc [16-18]. 3.1. The two types of decision tree The two types of decision tree include top-down decision tree and bottom-up decision tree. Top-down : this begins with the design strategy. Top-down decision tree starts at the abstract and general levels of the ontology and works. Bottom-up :It is the tool of building strategic criteria into project selection. 4. Product innovation decision-tree In this paper, we propose the structure about the combination of top-down and bottom-up decision tree. In the first step of data mining process, the resource of defining problems involves in three directions: customer, product, and market (see figure2). The direction of product means the core competitive activity: it divides into four departments: function department, material department, technique department, and form department. The direction of customer means the sources of competitive advantages. It includes three departments: customer s needs wants and customer s cycle. 4
The direction of market means competitors. It includes five departments: market size, market In the second step of data mining process, includes three categories: customer-oriented, product-oriented, and market-oriented databases. Customer-oriented database: The core problem is whether customers are satisfied with product. Product-oriented database: The core problem is whether product fit the capability of market. Market-oriented database: The core problem is whether market could discovery and achieves customers needs (see figure2). The third step of data mining process, analyzing and searching rules, adopts decision tree. The fourth step of data mining process, building models, take use of top-down and bottom-up decision tree models. Then applying data mining to product innovation has design target: top-down (see figure4) and detailed design: bottom-up structures. No C1 Needed X1 Fit F1 Need Design purpose C2 In place T1 T2 Customer Level No F2 Practice Benefit M11 M22 Product Level Market Level Extracting data Major Function Stop Go Stop Sub-function1 Sub-function2 Sub-function3 Go Stop Go Stop Go Function Function Function Function Function Figure5. Detailed function The product target decision-tree (see figure4) needs tangible target and determining design purpose is the starting of design project. Achieving design purpose begins from customer level. If customer level could fit design purpose, the project will go through product level. The detailed function decision tree creates the platform to collect various data. If choosing different models, data mining through visualization, will show different suggestions (see figure5). When developing the product innovation, the research used integrated decision tree systems which include bottom-up and top-down decision tree. Making integrated decision adopts top-down decision tree because it can hold the target to support new product innovative development. Making detailed design adopts bottom-up decision tree because it can have potential activities to explore new product design. Figure4. Product target decision-tree 5
5. Conclusion Supporting design innovation processes with technology and methods from the field of knowledge management can have a beneficial effect both on product and on financial development. The new knowledge made available by data mining can lead to products that are more competitive. New product innovative development adopts top-down decision tree because it can hold the target. Making detailed design adopts bottom-up decision tree because it can analyze new product design. Knowledge management in design will support decision-making with broader, accessible knowledge bases, and organize data in generally recognized and widely used the Integrated Decision Tree model. and Operations Research, Vol. 31, pp.1933-1945 [9] Horváth, 2001, A contemporary survey of scientific research into engineering design, 13 th international conference on engineering design, Glasgow, UK, pp.13-20 [10] V Hubka and W E Eder, 2001, functions revisited, 13 th international conference on engineering design, Glasgow, UK, pp.69-76 [11] C T Hansen, 2001, verification of a new model of decision-making in design Decision-making in design, 13 th international conference on engineering design, Glasgow, UK, pp.101-108 [12]Motokazu Orihata and Chihiro Watanabe, 1999, the interaction between product concept and institutional inducement: a new driver of product innovation, Technovation Vol. 20, pp.11-23 [13]Raghavan Parthasarthy and Jan Hammond, 2001, Product innovation input and outcome: moderating effects of the innovation process, Journal of Engineering and Technology Management, Vol.19, pp.75-91 6. References [1] Robert Groth, 2000, Data Mining, Hall PTR, New Jersey [2] Zhengxin Chen, 2001, Data Mining and Uncertain Reasoning, Wiley Inter-science, Canada [3] Robert G. Cooper, 2001, Winning at New Product, Perseus publishing, New York [4] C. Merle Crawford, 1996, New Products Management, Mc Graw Hill, America [5] Paul Belliveau, Abble Griffin, and Stephen Somermeyer, 2002, the PDMA Toolbook for New Product Development, Wiley Inter-science, Canada [6] Vijay Atluri and John Hale, 2000, Research Advances in Database and Information Systems Security, Kluwer Academic, America [7] Ranjit K. Roy, 2001, Design of Experiments Using the TAGUCHI Approach,Wiley Inter-science, Canada [8] Kweku-Muata and Osei-Bryson, 2004, Evaluation of decision trees: a multi-criteria approach, Computers [14]J Cristina Olaru and Louis Wehenkel, 2003, A complete fuzzy decision tree technique, Fuzzy Sets and Systems, Vol. 138, pp.221-254 [15]Udo-Ernst Haner, 2002, Innovation quality-a conceptual framework, International journal of product economics, Vol.80, pp.31-37 [16]Chris Rygielski, Jyun-Cheng Wang, David C. Yen, 2002, Data mining techniques for customer relationship managemet, Technology in Society, Vol. 24, pp.483-502 [17]Chris Clifton, Bhavani Thuraisingham, 2001, Emerging standards for data mining, Computer standards and interfaces, Vol. 23, pp.187-193 [18]Jules J. Berman, 2002, Confidentiality issues for medical data miners, Artficial Intelligence in Medicine, Vol. 26, pp.25-36 [19]Helen M. Moshkovich, Alexander I. Mechitove, and David L. Olson, 2002, Rle induction in data mining: 45 6