214:, also known as choice boards, design systems, toolkits, or co-design platforms, are responsible for guiding the user through the configuration process. Different variations are represented, visualized, assessed and priced which starts a learning-by-doing process for the user. While the term “configurator” or “configuration system” is quoted rather often in literature, it is used for the most part in a technical sense, addressing a software tool. The success of such an interaction system is, however, not only defined by its technological capabilities, but also by its integration in the whole sale environment, its ability to allow for learning by doing, to provide experience and process satisfaction, and its integration into the brand concept. (
292:). Configurators serve as an important tool for choice navigation. Configurators have been widely used in e-Commerce. Examples can be found in different industries like accessories, apparel, automobile, food, industrial goods etc. The main challenge of choice navigation lies in the ability to support customers in identifying their own solutions while minimizing complexity and the burden of choice, i.e. improving the experience of customer needs, elicitation and interaction in a configuration process. Many efforts have been put along this direction to enhance the efficiency of configurator design, such as adaptive configurators(
230:). They are employed in B2B (business to business), as well as B2C (business to consumer) markets and are operated either by trained staff or customers themselves. Whereas B2B configurators are primarily used to support sales and lift production efficiency, B2C configurators are often employed as design tools that allow customers to "co-design" their own products. This is reflected in different advantages according to usage:
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Configurators enable mass customization, which depends on a deep and efficient integration of customers into value creation. Salvador et al. identified three fundamental capabilities determining the ability of a company to mass-customize its offering, i.e. solution space development, robust process
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Case based: in case based configurators, the knowledge necessary for reasoning is stored mainly in cases that record a set of configurations sold to earlier customers. With the case based approach, one tries to solve the current configuration problem by finding a similar, previously solved problem
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manner. At each step, the system examines the entire set of rules and considers only the rules it can execute next. Each rule carries its own complete triggering context, which identifies its scope of applicability. The system then selects and executes one of the rules under consideration by
300:). The prediction is integrated into the configurator to improve the quality and speed of configuration process. Configurators may also be used to limit or eliminate mass customization if intended to do so. This is accomplished through limiting of allowable options in data models.
344:), the most important advantages of model based systems are a better separation between what is known and how the knowledge is used, enhanced robustness, enhanced compositionality and enhanced re-usability.
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Model Based: the main assumption behind model based configurators is the existence of a system's model which consists of decomposable entities and interactions between their elements. As presented by (
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and adapting it to the new requirements. The basic processing cycle in a case based configurator is: input customer requirements, retrieve a configuration and adapt the case to the new situation.
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Franke, Nikolaus; Piller, Frank (2003). "Key
Research Issues in User Interaction with User Toolkits in a Mass Customisation System".
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Wang, Yue; Tseng, Mitchell (2011). "Adaptive
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333:). This kind of systems often suffers from the maintenance issues because of the lack of separation between
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International Florida Artificial Intelligence Research Society Conference
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performing its action part. Most of early configuration systems fall in this category, like R1/XCON (
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Frayman, F; Mittal, S (1987). "Cossack: A Constraint based expert system for configuration task".
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National Conference on Artificial Intelligence
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Sabin, D; Weigel, R (1998). "Product configuration frameworks—a survey".
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488:"Customizing Question Selection in Conversational Case-Based Reasoning"
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Hvam, Lars; Haug, Anders; Mortensen, Niels Henrik; Thuesen, Christian.
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Configurators can be found in various forms and different industries (
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McDermott, J (1980). "R1: An Expert in the
Computer Systems Domain".
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Systems in Engineering: Planning and Design
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Error elimination throughout the ordering and production process
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393:"OBSERVED BENEFITS FROM PRODUCT CONFIGURATION SYSTEMS"
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Hamscher, W (1994). "Explaining
Financial Results".
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