How to Analyze and Quantify Similarities between Configured Engineer-To-Order Products by Comparing the Highlighted Features Utilizing the Configuration System Abilities Sara Shafiee1, Lars Hvam2, Katrin Kristjansdottir3 Abstract. 1 Engineering-To-Order (ETO) companies making reach all the advantages that can be gained from utilizing product complex and highly engineered products, face the challenge of configuration systems, changes in the organization and the delivering highly customized and engineered products with high supporting systems in the order acquisition and fulfillment process quality and short delivery time. In order to respond to those are needed [4]. These issues can be solved by double checking all challenges ETO companies strive to increase commonality between the outputs generated by the configurator through an automated IT different projects and to reuse product related information. For that purpose companies need to be able to retrieve previously designed solution. “All designs are redesigns” has long been a popular cliché products and identify which parts of the design can be reused and in design research [5]. More generally it has been observed that in which parts to redesign. This allows companies to reduce many firms the reuse and generalization of past experiences (often complexity in the product range, to decrease the engineering hours called "lessons learned") is becoming a key factor for the and to improve the accuracy of the product specifications. In this improvement, in time and in quality, of operational processes [6]. It article we suggest a framework where product features from the is rational to say that all the attributes of the products and all their company’s configuration system are listed up in order to compare relations are available in the configuration system; and for every with previously made products by retrieving information from received order from the customer, changes and specifications for internal ERP/PLM systems. The list of features consists of defining the product are entered into the configuration system. The idea is to features with potential sets of values e.g. capacity, dimensions, make a connection between ERP and the configuration system, quality of material, energy consumptions, etc. When identifying a specific previously designed product, it allows access to all of the when generating quotations in the product configuration systems specifications of the existing product along with the engineering and compare it with the previous done projects saved in the ERP hours used, materials used, and hours used in the workshop. The system from different perspectives. ETO companies producing aim of this paper is to make a framework for setting up a database complex highly engineered products have a significant problem before starting the comparison. when calculating the prices for the presale and sale processes. That is especially the case when domain experts cannot determine accurate price curves or when vendors are not providing sufficient 1 INTRODUCTION AND PROBLEM information to be modeled inside the configurator. Therefore STATEMENT estimates are used or markup factors are added. When A configurator supports the user in specifying different features of underestimating costs in projects the company will lose profit and a product by defining how predefined entities (physical or non- when overestimating the cost the customer might go elsewhere physical) and their properties (fixed or variable) can be combined where he can buy the product at a reasonable price. The accuracy [1]. Improving the quotation process with the help of configuration of calculations is affected, as previous projects are not easily systems is a great opportunity for enhancing the presale and accessible and it requires significant work to compare potential production process efficiency in the companies [2]. There are new projects with previous projects manually in order to find the several benefits that can be gained from utilizing product relevant information. configuration systems, such as a shorter lead-time for generating Hvam et al. [1] explains this problem by using an example from quotation and fewer errors, increased ability to meet customer F.L. Smidth, which is an ETO company selling cement plants. In requirements with regards to functionality and quality of the this example, the company strives to reuse information from products, increased customer satisfaction, etc. [3]. Theoretical previously made projects to calculate the most accurate price based elaboration of the empirical evidence suggests that, in order to on weight and capacity. According to Hvam et al. [1], the price and weight curves are made by inserting the capacity, price and weight 1 based on information from 3-5 previously produced machines. A Industrial PhD Student, Management Engineering department, Technical curve is then drawn through the points as is demonstrated in University of Denmark, 2800 Kgs.Lyngby, Denmark, sashaf@dtu.dk 2 Professor, Centre for Product Modelling (CPM), Department of Figure 4. This allows identification of prices and weights for Management Engineering, Technical University of Denmark, 2800 machines that have not previously been produced. Kgs.Lyngby, Denmark,lahv@dtu.dk 3 PhD Student, Management Engineering department, Technical University of Denmark, 2800 Kgs.Lyngby, Denmark, katkr@dtu.dk 139 Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria Modular architecture is a term that usually refers to the construction of a building from different instances of standardized components, and in manufacturing it is used for interchangeable units that are used to create the product variants [10]. Dahmus et al. [11] defines a Modularity Matrix to find the similarities between product platforms across columns for a single function in the Figure 1. Price and weight curve for main machines in F.L Smith matrix. Thereafter, architecting of the product portfolio is recommended to take advantage of possible commonalities through However, with regards to highly complex products, the price the reuse of modules across different product families. If an curves are not thought to be the most accurate method as there are existing product has standardized and decoupled interfaces, the several dependent features and great numbers of neighbors on the design of the next product can re-use heavily from the components curve. Another important drawback from the price curves is that of the previous product. Holmqvist [12] identifies existing the user is only provided access to some of the previously made modularization methods and analyses them with regards to their projects. Therefore the most similar previous projects might be ability to deal with different degrees of product complexity. Based missed. on that he proves that modularization methods are really useful for The first benefits of using an automated IT process, where an a simple product architecture but for higher degrees of product integration between the configuration system and the company’s complexity, when several functions are allocated to several internal ERP in order to get access to previously saved project physical modules, or large variation of variants, these methods information, is to avoid time consuming redesigning activities in seem inefficient [12]. Zamirowski et al. [13] presents three the production phase. This means that it will be possible to produce additional heuristics to find common modules across products in a the same component or product while spending the least possible product family. By knowing the previously ordered products, there time and resources. will be the opportunity of decoupling of design and production Salvador and Forza [7] offer much anecdotal evidence of the tasks. issues related to product configuration systems. These are listed in The potential benefits that can be gained from using the terms of: excessive errors, too long time between sales and comparison capabilities between configuration systems and other installation due to inadequate product information supply to the databases at the companies are summarized in Table 1. sales office, an excess of repetitive activities within the technical Table 1. Benefits from reusing the previous projects office, and a high rate of configuration errors in production. Even if Area Benefits there are often concerns regarding product configuration projects Management 1. Lean management by avoiding all the and the possible errors in the early phases of deploying the presales, production and sales activity systems, the confirmation of the configuration system is not the that have been performed before. only benefit from the mentioned solution. Configuration system 2. Reducing errors and increasing Salvador and Forza [7] describe product configuration systems development reliability of the configuration system. as aid systems for the end users or customers for creating 3. Facilitating the testing process for the configuration systems development. communication value. Comparing the new project with previous Standardization, Product 4. Recommending previously successful ones could also turn into a recommendation system in the projects to the end users. companies. Felfernig [8] discusses different recommendation planning, Configuration 5. Basis for product standardization. systems that are divided into Collaborative Filtering (CF), Content system Product planning, 6. Statistical approach to the information Based Filtering (CBF) and Knowledge Based Recommendations and market requirements of the (KBR). The available recommendation technologies in e- management product. commerce are potentially useful in helping customers to choose the Product planning, 7. Improve the quality of the optimal products configuration [9]. It seems that the mentioned Configuration system configuration system, lead time, idea is similar to the values that come from recommendation manufacturing, sales engineering. systems. This means that if a 95% similarity between the current project and a previous project is found, the previous project can be Inakoshi et al. [14] propose a framework for product configuration re-used and thereby cost related to making the product that integrates a constraint satisfaction problem with a Case-based specification significantly reduced. This includes costs in the sales Reasoning tool (CBR), where the framework is applied to an on- phase, engineering and production. Furthermore, this is likely to line sales system. This framework contains the following steps: improve the quality and the accuracy of the cost estimations. It also makes it easy to reach an agreement with the customer, and to 1. Case retrieval: similar cases are retrieved from the case recommend to them a consultancy to confirm the success of the base in accordance with the similarities between the project by small changes in the order. current query and the cases. Furthermore, this approach enables companies to analyze the 2. Requirement formalization: a well-defined requirement products statistically for future product development. Using the consists of the current query and the object function, and configuration systems and comparing different orders can provide it is supplied to a Constraint Satisfaction Problem (CSP) valuable information to managers, as it helps them to keep track of solver. product features and to get an overview of market demands. This helps companies to be more in control over the product assortment 3. Requirement modification: The well-defined and eliminates the complexity related to the diversity of product requirement is modified only if there is no configuration features offered in the production line. and the CSP solver returns no solution back to the CBR Wrapper. Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors 140 Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria 4. Parts database: a parts database that contains the configuration system to compare with all the previously generated definition of a product family. It defines the types of quotations, which are documented in a desired database. In Figure parts, the constraints on parts connectivity, and other 3 the process needed for the comparison accomplishment is kinds of restrictions on the products. illustrated. 5. CSP solver: The CSP solver receives a well-defined user requirement and solves the problem. The physical structure of the configuration system is illustrated in Figure 2. Figure 3. The process of comparing and find similar products 3.1 Set up of the database with previous projects, comparing the configured products with the previously designed Figure 2. Physical architecture of configuration system [14] products This research work has been used as an inspiration for creating a Inakoshi et al. [14] introduce a framework for comparing a product database development framework and then doing a comparison and configuration that integrates a constraint satisfaction problem with integrating it with the product configuration system. There is no a Case-Based Reasoning tool (CBR) for a specific case and with discussion in detail on how to make a database from the ERP specific tool. In this paper the aim is to make a framework in order system, where all the previous projects are stored. to create a database for the comparison, which allows the comparison to be done in a standardized way where the currently available tools and methods can be utilized. Based on literature a 2 RESEARCH METHOD seven step framework has been developed, the individual steps are illustrated in Figure 4. The process is not a complete waterfall In accordance with the overall objective, the first phase is focused process, as it is necessary to iterate some of the steps depending on on the development of the framework, devoted to selecting a the product. framework for product configuration, which integrates a constraint satisfaction problem with Case-Based Reasoning tool (CBR) from previous literature. The framework development is an ongoing research project to be developed further and tested by a group of researchers and practitioners with an applied research background in modelling products, product architecture, knowledge engineering and product configuration, software development, combining traditional domains of mechanical engineering with product configuration and software development. The framework will be tested in an ETO company specializing in production of catalysts. Figure 4. Database set up process in 7 phases 3 SUGGESTED METHOD FOR IDENTIFICATIO AND COMPARISON 3.1.1 Identify relevant features according to features BETWEEN PRODUCT FEATURES from configuration model Previous researchers define different tools and methods to measure Previous research that describes how to use modules across the similarities between product features. Using configuration different products [13] [15] will be used in order to compare systems and techniques for comparing products, it is possible to different products. Commonality is best obtained by minimizing compare different product features that have been ordered with the the non-value added variations across the products within the same new coming orders. One of the prerequirements for using the product family without limiting the choices for the customers [16]. automatic comparison is to have product configuration system in According to Ulrich [10], if an existing product has standardized the sales process. The scenario is to use product features in the and decoupled interfaces, the design of the next product can 141 Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria borrow heavily from the components of the previous product. This technique could be used for finding the projects or products Thevenot and Simpson [16] discuss a framework where with the same specification but for a high level of similarities. This commonality indices are used for redesigning the product families could help us to identify some of the product features and then to align with cost reductions in the product development process search for their range of values. aligned with the standardized and modularized product structure incorporated to the configuration system, makes it easier to pick the relevant features or add them to the configurator. 3.1.4 Classifying the products based on features E. Lopez-Herrejon et al. [17] introduce Software Product Line For identifying and classifying relevant features in order to make a Engineering (SPLE) to represent the combinations of features that database, classification techniques are required. Burbidge describes distinguish the system variants using feature models. how to classify the needs for the product components and coding them by introducing the Group Technology (GT) method [24]. Martinez et al. [25] then use the GT technique as a base for 3.1.2 Retrieve specifications on previous designed developing a new GT method [25] they provide an example where products from ERP / PLM system the GT technique is used in manufacturing plant where it help in The current generation of database systems is designed mainly to the processes of minimizing unnecessary variety by making support business applications and most of them offer discovery designers aware of existing components [24]. The aim of features using tree inducers, neural nets, and rule discovery classification and coding is to provide an efficient method of algorithms [18]. One of the fundamental problems of information information retrieval for decision making. To be efficient enough a extraction from ERP systems is that the formats of available data code must be designed for the particular purpose for which it will sources are often incompatible, requiring extensive conversion be used [24]. Leukel et al. [26] discuss the design and components efforts [19]. Knowledge discovery in databases represent the of product classification systems in B2B e-commerce and process for transformation of available data into strategic suggested a data model based on XML. Fairchild [27] discuss the information, which is characterized by issues related to the nature application of classification systems and the requirements on them. of data and desired features [20] [21]. Brachman et al. [22] define Simpson [28] uses GT for adding, removing, or substituting one or Knowledge Discovery (KD) process elements to be in three steps: more modules to the product platform for product platform design and customization. Sousa et al. [29] suggest an automated 1. Task discovery, data discovery, data cleansing, data classification system for specialization of life cycle assessment. segmentation First of all they manage to have a conceptual framework for 2. Model selection, parameter selection, model environmental performance of product concepts. Then, the specification, model fitting hierarchical clustering has been used in several applications to 3. Model evaluation, model refinement, output evaluation show useful ways of grouping objects according to their KD has a variety of meanings. It includes, at one end the derivation similarities and product descriptors data. Finally, it is used to of useful information from a database like “which products are develop an automated classification system based on decision trees needed for the specific amount of engineering hours for algorithms. Sousa et al. [29] also use Matlab and C4.5 decision tree installation?” [23]. algorithm, which seems to be applicable in all classification cases. C4.5 is an algorithm used to generate a classification in form of a decision tree that is either a leaf indicating a class or a decision 3.1.3 Retrieve features from product files and node that specifies some test to be carried out on a single attribute determining the values value. This algorithm has a few base cases as below [30]: Most companies use the old technique called “British classification” when naming different components according to the 1. All the samples in the list belong to the same class. When product variants. However as the products get more complicated this happens, it simply creates a leaf node for the decision this technique becomes impractical. In this technique, as shown in tree saying to choose that class. Figure 5, there is a “surname” of five digits it is the general class of 2. None of the features provide any information gain. In this an item and the “Christian name” of three digits for an exact case, C4.5 creates a decision node higher up the tree identity of for the particular item [24]. using the expected value of the class. 3. Instance of previously unseen class encountered. Again, C4.5 creates a decision node higher up the tree using the expected value. Ho [21] introduces OSHAM system generated in hierarchical graphical browser which is competing with C4.5. Magali and Geneste [6] propose object oriented modeling language, Unified Modeling Language (UML) as a standard modelling of domain knowledge for their research work to represent field data. The exploitation of the object modeling as an indexing base is suggested to allow a fast selection of potentially Figure 5. Expansion of a major class [24] interesting objects during the similar case search [6]. Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors 142 Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria Guillaume et al. [31] developed six heuristics for clustering and The case study is planned based on a group of researchers from the weighting the logical, syntactical and semantical relationships Technical University of Denmark in collaboration with Haldor between feature names. The other representation introduced as the Topsoe. The aim is to test and make further developments to the so-called Product Comparison Matrices (PCMs), can help to make proposed framework. The case study should aim to find the major a choice, where the aim is to visualize all the products and minor drawbacks in the current framework and refines it based characteristics through a metrical representation, [32]. on experiment. The main things that will be tested in the case study are listed below: 3.1.5 Set up database with previous products design 1. Can we retrieve the products’ features out of the ERP Ramakrishnan et al. [33] give an overview of database design in system? the following three steps: 2. Can we classify the products? 3. Can we make a data base according to the product 1. Requirement analysis: Understanding of what data is to features? be stored in the database, what applications must be built 4. How to do the comparison between the new product and on top of it, what operations are most frequent and subject the previous designed products? to performance requirements. 5. How to integrate the data base and configuration 2. Conceptual database design: The information gathered in systems? How to make the user interface in the the requirements analysis step is used to develop a high- configuration system? level description of the data along with the constraints to be stored in the database. 3. Logical database design: Database Management System 5. CONCLUSION (DBMS) has to be chosen to implement the database design, and convert the conceptual database design into a database schema in the data model of the chosen DBMS. In this paper we suggest an approach for comparing a new order that is being configured with previous made configurations, which are usually stored in various internal systems at the companies. 3.1.6 Comparing the new order products with the This will lead to some advantages such as increased commonality previous designed products in the ERP/ PLM across different products and reuse of modules across the family of system products. To achieve the goal of comparing different products a database for the necessary features is needed. The proposed There are extensive research works in the field of IT illustrating approach includes 7 separate phases. Finally after the database different methods to do the comparison in an automated way. setup, the comparison method based on literature will be Classical Case-based Reasoning tool (CBR) methodologies [34] accomplished and the integration between the configuration system [35] are based on four tasks, which are: Retrieve, Reuse, Revise and database will be performed. The paper is just mentioning a and Retain are highly used for this purpose. problem realized as one of the configuration system drawbacks and Navinchandra’s [36] developed CYCLOPS, which was the first suggests a framework for using comparison method to solve this system to explore CBR in interactive design. Vareilles et al. [37] problem. To have a generic framework to retrieve data from ERP/ proposed an approach to use ‘contextual knowledge corresponding PLM systems and compare them in configuration projects further to past cases’ and ‘general knowledge corresponding to relations, research work is required as listed below: rules or constraints that link design variables’. In this research, 1. Framework testing for a case study and test the available Constraint Satisfaction Problem (CSP) is used regarding general tools for retrieving and comparing the features. knowledge and CBR operates with conceptual knowledge. 2. Development of the possible ways to integrate database with product configuration system. Magali and Geneste [6] propose a method to define the neighborhood of the retrieved case to propagate domain constraints. In this method they use Fuzzy Search is divided in two 6. REFERENCES steps that are: rough filtering process and similarity measuring. Coudert et al. [38] suggest an integrated case-based approach by [1] L. Hvam, N.H. Mortensen and J. Riis, Product Customization, using ontology of concepts for guiding project planning and system Springer, Berlin, 2008. design processes. [2] A. Felfernig, L. Hotz, C. Bagley and J. Tiihonen, Knowledge-based Configuration: From Research to Business Cases, Morgan Kaufman, 2014. 3.1.7 Integration of the database with the product configuration system [3] L. Hvam, S. Pape and M.K. Nielsen, "Improving the quotation process with product configuration," Computers in Induustry, 607-621, (2006). According to Inakoshi [14], there is the possibility to integrate a [4] D.L. McGuinness and J.R. Wright, "Conceptual modelling for constraint satisfaction problem with CBR for a product configuration: A description logic-based approach," AI EDAM, 12, 04, configuration system. 333-344, (1998). [5] A.K. Goel and S. Craw, "Design, innovation and case-based reasoning," The Knowledge Engineering Review, 271-276, (2005). 4. PLAN FOR THE CASE STUDY [6] R. Magali and L. Geneste, "Search and adaptation in a fuzzy object 143 Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria oriented case base," Advances in Case-Based Reasoning, Springer, Manufacturing Plan Generation and Classification," Concurrent Berlin, 350-364, 2002. Engineering: Research & Applications, 8,. 1, 12–23, (2000). [7] F. Salvador and C. Forza, Product Information Management for Mass [26] J. Leukel, V. Schmitz and F. D. Dorloff, "A modeling approach for Customization: Connecting Customer, Front-office and Back-office for product classification systems," in 13th International Workshop on Fast and Efficient Customization, New York: Palgrave Macmillan, Database and Expert Systems Applications, (2002). 2007. [27] A. M. Fairchild and B. de Vuyst, "Coding standards benefiting product [8] A. Felfernig, L. Hotz, J. Tiihonen and C. Bagley, "Configuration- and service information in e-commerce," in 35th Annual Hawaii Related Topics.”, in Knowledge-based configuration: From research International Conference on System Sciences , (2002). to business cases, Morgan Kaufmann",. 21-28, 2014. [28] T. W. Simpson, "Product platform design and customization: Status [9] L.L. Zhang, "Product configuration: a review of the state-of-the-art and promise," AI EDAM: Artificial Intelligence for Engineering and future research," International Journal of Production Research, Design, Analysis and Manufacturing, 18, no. 01, pp. 3-20, 2004. 52, 21, 6381-6398, (2014). [29] I. Sousa and D. Wallace, "Product classification to support [10] H. Ulrich, "Fundamentals of product modularity", in “Management of approximate life-cycle assessment of design concepts," Technological Design: Engineering and Management Perspectives, Atlanta, GA, Forecasting and Social Change, 73, 3, 186-189, (1980).. Springer, 219-231, (1994). [30] J. R. Quinlan, C4. 5: programs for machine learning, Morgan [11] J.B. Dahmus, J. P. Gonzalez-Zugasti and K. N. & Otto, "Modular Kaufmann Publishers, 1993. product architecture," Design studies, 22, 5, 409-424, (2001). [31] G. Bécan, M. Acher, B. Baudry and S.B. Nasr, "Breathing ontological [12] T.K. Holmqvist and M. L. Persson, "Analysis and improvement of knowledge into feature model synthesis: an empirical study," product modularization methods: Their ability to deal with complex Empirical Software Engineering, 1-48, (2015). products.," Systems Engineering, 6,. 3, 195-209, (2003) [32] G. Bécan, N. Sannier, M. Acher, O. Barais, A. Blouin and B. Baudry, [13] E.J. Zamirowski and K. N. Otto, "Identifying product family "Automating the formalization of product comparison matrices," in architecture modularity using function and variety heuristics," in 11th 29th ACM/IEEE international conference on Automated software International Conference on Design Theory and Methodology, ASME, engineering, 433-444, 2014. Las Vegas, (1999). [33] R. Ramakrishnan and J. Gehrke, Database management systems, [14] H. Inakoshi, S. Okamoto, Y. Ohta and N. Yugami, "Effective decision Osborne: McGraw-Hill, 2000. support for product configuration by using CBR," in Fourth [34] Y. Avramenko and A. Kraslawski, "Similarity concept for case-based International Conference on Case-Based Reasoning (ICCBR), design in process engineering," Computers & chemical engineering, Workshop Casebased Reasoning in Electronic Commerce, Vancouver, 30, 548-557, (2006). Canada, 2001. [35] F. Grupe, R. Urwiler, N. Ramarapu and M. Owrang, "The application [15] A. Ericsson and G. Erixon, Controlling design variants: modular of case-based reasoning to the software development process," product platform, Society of Manufacturing Engineers, 1999. Information and Software Technology,. 40, 493–499, (1998). [16] H. J. Thevenot and T. W. Simpson, "Commonality indices for product [36] D. Navinchandra, "Exploration and innovation in design: towards a family design: a detailed comparison.," Journal of Engineering computational model", Springer Science & Business Media, New Design, 17, 2, 99-119, (2006). York, 2012. [17] R.E. Lopez-Herrejon, L. Linsbauer, J.A. Galindo, J.A. Parejo, D. [37] E. Vareilles, M. Aldanondo, A. C. De Boisse, T. Coudert, P. Gaborit Benavides, S. Segura and A. Egyed, "An assessment of search-based and L. Geneste, "How to take into account general and contextual techniques for reverse engineering feature models," Journal of Systems knowledge for interactive aiding design: Towards the coupling of CSP and Software, 103, 353-369, (2015). and CBR approaches," Engineering Applications of Artificial [18] T. Imielinski and H. Mannila, "A database perspective on knowledge Intelligence, 25, 31-47, (2012). discovery," Communications of the ACM, 39, 11, 58-64, (1996). [38] T. Coudert, E. Vareilles, L. Geneste, M. Aldanondo and J. Abeille, [19] E. Bendoly, "Theory and support for process frameworks of "Proposal for an integrated case based project planning and system knowledge discovery and data mining from ERP systems," design process," in 2nd International Conference en Complex Systems Information & Management,. 40, 7, 639-647, (2003). Design and Management, CSDM, 2011. [20] R. Davies, "The creation of new knowledge by information retrieval [39] H. Fargier, J. Lang and T. Schiex, "Mixed constraint satisfaction: A and classification," Journal of Documentation, 45, 273–301, (1989). framework for decision problems under incomplete knowledge," [21] T. B. Ho, "Discovering and using knowledge from unsupervised data" AAAI, 1, (1996). Decision Support Systems, 21, 29–42, (1997). [40] A. K. Goel and S. Craw, "Design, innovation and case-based [22] R. J. Brachman and T. Anand, "The process of knowledge discovery reasoning," The Knowledge Engineering Review, 20, 271-276, (2006). in databases," in In Advances in knowledge discovery and data mining, [41] V. R. Basili and D. M. Weiss, "A Methodology for Collecting Valid CA, (1996). Software Engineering Data," IEE transactions of software [23] F. H. Grupe, "Using domain knowledge to guide database knowledge engineering, 10, 728-738, (1984). discovery," Expert Systems With Applications, 10, 2, 173-180, (1996). [42] A. Haug, "Representation of Industrial Knowledge – As a Basis for [24] J. L. Burbidge, The introduction of group technology, London: Developing and Maintaining Product Configurators," Technical Heinemann, 1975. University of Denmark, Lyngby, 2008. [25] M. Martinez, J. Favrel and P. Ghodous, "Product Family [43] S. Shafiee and L. Hvam, "An agile documentation system for highly Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors 144 Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria engineered, complex product configuration systems," 22nd EurOMA Conference, NEUCHÂTEL, SWITZERLAND, 2015. [44] P. Kruchten, The Rational Unified Process: An Introduction, New York: Addison-Wesley, 1998. 145 Juha Tiihonen, Andreas Falkner and Tomas Axling, Editors Proceedings of the 17th International Configuration Workshop September 10-11, 2015, Vienna, Austria