Knowledge-based configuration

Knowledge-based configuration, or also referred to as product configuration or product customization, is an activity of customising a product to meet the needs of a particular customer. The product in question may consist of mechanical parts, services, and software. Knowledge-based configuration is a major application area for artificial intelligence (AI), and it is based on modelling of the configurations in a manner that allows the utilisation of AI techniques for searching for a valid configuration to meet the needs of a particular customer.[A 1][A 2][A 3][A 4][A 5][B 1][B 2][B 3]

Background

Knowledge-based configuration (of complex products and services) has a long history as an artificial intelligence application area, see, e.g.[B 1][A 1][A 6][A 7][A 8][A 9][A 10][A 11] Informally, configuration can be defined as a "special case of design activity, where the artifact being configured is assembled from instances of a fixed set of well-defined component types which can be composed conforming to a set of constraints".[A 2] Such constraints[B 4] are representing technical restrictions, restrictions related to economic aspects, and conditions related to production processes. The result of a configuration process is a product configuration (concrete configuration), i.e., a list of instances and in some cases also connections between these instances. Examples of such configurations are computers to be delivered or financial service portfolio offers (e.g., a combination of loan and corresponding risk insurance).

Configuration systems

Configuration systems[B 1][A 1][A 2] or also referred to as configurators or mass customization toolkits,[A 12] are one of the most successfully applied Artificial Intelligence technologies. Examples are the automotive industry,[A 9] the telecommunication industry,[A 7] the computer industry,[A 6][A 13] and power electric transformers.[A 8] Starting with rule-based approaches such as R1/XCON,[A 6] model-based representations of knowledge (in contrast to rule-based representations) have been developed which strictly separate product domain knowledge from the problem solving one - examples thereof are the constraint satisfaction problem, the Boolean satisfiability problem, and different answer set programming (ASP) representations. There are two commonly cited conceptualizations of configuration knowledge.[A 3][A 4] The most important concepts in these are components, ports, resources and functions. This separation of product domain knowledge and problem solving knowledge increased the effectiveness of configuration application development and maintenance,[A 7][A 9][A 10][A 14] since changes in the product domain knowledge do not affect search strategies and vice versa.

Configurators are also often considered as "open innovation toolkits", i.e., tools which support customers in the product identification phase.[A 15] In this context customers are innovators who articulate their requirements leading to new innovative products.[A 15][A 16][A 17] "Mass Confusion" [A 18] – the overwhelming of customers by a large number of possible solution alternatives (choices) – is a phenomenon which often comes with the application of configuration technologies. This phenomenon motivated the creation of personalized configuration environments taking into account a customer’s knowledge and preferences.[A 19][A 20]

Configuration process

Core configuration, i.e., guiding the user and checking the consistency of user requirements with the knowledge base, solution presentation and translation of configuration results into bill of materials (BOM) are major tasks to be supported by a configurator.[A 21][B 5][A 5][A 12][A 22] Configuration knowledge bases are often built using proprietary languages.[A 10][A 19][A 23] In most cases knowledge bases are developed by knowledge engineers who elicit product, marketing and sales knowledge from domain experts. Configuration knowledge bases are composed of a formal description of the structure of the product and further constraints restricting the possible feature and component combinations.

Configurators known as characteristic based product configurators use sets of discrete variables that are either binary or have one of several values, and these variables define every possible product variation.

Software and service configuration

Recently, knowledge based configuration has been extended to service and software configuration. Modeling software configuration has been based on two main approaches: feature modeling,[A 24][B 6] and component-connectors.[A 25] Kumbang domain ontology combines the previous approaches building on the tradition of knowledge based configuration.[A 26]

See also

References

Conference and journal papers
  1. M. Stumptner, An Overview of Knowledge-Based Configuration. AI Commun. 10(2): 111–125, 1997.
  2. D. Sabin and R. Weigel, Product Configuration Frameworks – A Survey, IEEE Intelligent Systems, vol. 13, no. 4, pp. 42–49, 1998.
  3. T. Soininen, J. Tiihonen, T. Männistö, and R. Sulonen, Towards a General Ontology of Configuration. AI EDAM (Artificial Intelligence for Engineering Design, Analysis and Manufacturing), 12(4): 357–372, 1998
  4. A. Felfernig, G. Friedrich, and D. Jannach, Conceptual modeling for configuration of mass-customizable products, Artificial Intelligence in Engineering 15(2): 165–176, 2001
  5. Y. Wang, and M. Tseng, Adaptive Attribute Selection for Configurator Design via Shapley Value. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 25 (1): 189–199, 2011.
  6. V. Barker, D. O’Connor, J. Bachant, and E. Soloway, Expert systems for configuration at Digital: XCON and beyond, Communications of the ACM, vol. 32, no. 3, pp. 298–318, 1989.
  7. G. Fleischanderl, G. Friedrich, A. Haselboeck, H. Schreiner, and M. Stumptner, Configuring Large Systems Using Generative Constraint Satisfaction, IEEE Intelligent Systems, vol. 13, no. 4, pp. 59–68, 1998.
  8. C. Forza and F. Salvador, Managing for variety in the order acquisition and fulfillment process: The contribution of product configuration systems, International Journal of Production Economics, no. 76, pp. 87–98, 2002.
  9. E. Juengst and M. Heinrich, Using Resource Balancing to Configure Modular Systems, IEEE Intelligent Systems, vol. 13, no. 4, pp. 50–58, 1998.
  10. D. Mailharro, A classification and constraint-based framework for configuration, Artificial Intelligence for Engineering, Design, Analysis and Manufacturing Journal, Special Issue: Configuration Design, vol. 12, no. 4, pp. 383–397, 1998.
  11. S. Mittal and F. Frayman, Towards a Generic Model of Configuration Tasks, in 11th International Joint Conference on Artificial Intelligence, Detroit, MI, 1989, pp. 1395–1401.
  12. N. Franke and F. Piller, Configuration Toolkits for Mass Customization: Setting a Research Agenda, Working Paper No. 33 of the Dept. of General and Industrial Management, Technische Universitaet Muenchen, no. ISSN 0942-5098, 2002.
  13. D. McGuiness and J. Wright, An Industrial Strength Description Logics-Based Configurator Platform, IEEE Intelligent Systems, vol. 13, no. 4, pp. 69–77, 1998.
  14. S. Mittal and B. Falkenhainer, Dynamic Constraint Satisfaction Problems, in National Conference on Artificial Intelligence (AAAI 90), Boston, MA, 1990, pp. 25–32.
  15. E. von Hippel, User Toolkits for Innovation, Journal of Product Innovation Management, vol. 18, no. 4, pp. 247-257, 2001.
  16. F. Piller and M. Tseng, The Customer Centric Enterprise, Advances in Mass Customization and Personalization. Springer Verlag, 2003, pp. 3–16.
  17. Y. Wang, and M. Tseng, An Approach to Improve the Efficiency of Configurators. In Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management, 2007.
  18. C. Huffman and B. Kahn, Variety for Sale: Mass Customization or Mass Confusion, Journal of Retailing, no. 74, pp. 491–513, 1998.
  19. U. Junker, Preference programming for configuration, in IJCAI’01 Workshop on Configuration, Seattle, WA, 2001.
  20. L. Ardissono, A. Felfernig, G. Friedrich, D. Jannach, G. Petrone, R. Schaefer, and M. Zanker, A Framework for the development of personalized, distributed web-based configuration systems, AI Magazine, vol. 24, no. 3, pp. 93–108, 2003.
  21. A. Haag, Product Configuration in SAP: A Retrospective, in Book: Knowledge-based Configuration - From Research to Business Cases, Elsevier/Morgan Kaufmann, pp. 319-337, 2014.
  22. A. Felfernig, Standardized Configuration Knowledge Representations as Technological Foundation for Mass Customization, IEEE Transactions on Engineering Management, 54(1), pp. 41–56, 2007.
  23. A. Haag, Sales Configuration in Business Processes, IEEE Intelligent Systems, vol. 13, no. 4, pp. 78–85, 1998.
  24. K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. S. Peterson, Feature-oriented domain analysis (FODA) feasibility study, Technical Report CMU/SEI-90-TR-21 ESD-90-TR-222, Software Engineering Institute, Carnegie Mellon University, 1990
  25. R. van Ommering, F. van der Linden, J. Kramer, and J. Magee, The Koala component model for consumer electronics software, IEEE Computer, 33(3): 72–85, 2000.
  26. T. Asikainen, T. Männistö, and T. Soininen, Kumbang: A domain ontology for modelling variability in software product families, Advanced Engineering Informatics, 21(1): 23–40, 2007.

Books
  1. A. Felfernig, L. Hotz, C. Bagley, and J. Tiihonen, Knowledge-based Configuration: From Research to Business Cases, Elsevier/Morgan Kaufmann, 2014.
  2. L. Hvam, N. Mortensen, J. Riis, Product Customization, Springer Verlag, 2008.
  3. C. Forza, F. Salvador, Product Information Management for Mass Customization, Palgrave Macmillan, 2006.
  4. F. Rossi, P. Van Beek, T. Walsh, Handbook of Constraint Programming, Elsevier, 2006.
  5. U. Blumöhr, M. Münch, M. Ukalovic, Variant Configuration with SAP, Galileo Press, 2012.
  6. K. Czarnecki, U. W. Eisenecker, Generative Programming – Methods, Tools, and Applications, Addison Wesley, 2000.

Research prototypes

Journal special issues on configuration
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