Configurator

"Design Automation" redirects here. For the automation of electronic circuit design, see Electronic design automation. For automation of other designs, see Computer-automated design.

Configurators, 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. (Franke & Piller (2003))

Advantages

Configurators can be found in various forms and different industries (Felfernig et al. (2014)). 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:

For B2B:

• Lower distribution costs
• Quicker reaction to customer inquiries
• Reduced capital commitment and less overproduction
• Error elimination throughout the ordering and production process
• Quality improvements in customer-service
• Worldwide access to up-to-date product information
• Reduction of item numbers

For B2C:

• Differentiation through individuality
• Reduced capital commitment and less overproduction
• Better knowledge of customers' needs
• Higher customer loyalty
• Shopping as experience

Enabler of mass customization

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 design and choice navigation (Salvador, Martin & Piller (2009)). 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(Wang & Tseng (2011);Jalali & Leake (2012)). 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.

Existing configuration paradigms

According to (Sabin & Weigel (1998)), configurators can be classified as rule based, model based and case based, depending on the reasoning techniques used.

• Rule based: these systems derive solutions in a forward-chaining 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 performing its action part. Most of early configuration systems fall in this category, like R1/XCON (McDermott (1980)), Cossack (Frayman & Mittal (1987)) and MICON (Birmingham & Siewiorek (1988)). This kind of systems often suffers from the maintenance issues because of the lack of separation between domain knowledge and control strategy, especially when the configurator system is complex.
• 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 (Hamscher (1994)), 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.
• 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 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.

References

• Franke, Nikolaus; Piller, Frank (2003). "Key Research Issues in User Interaction with User Toolkits in a Mass Customisation System". International Journal of Technology Management. 26 (5): 578–599. doi:10.1504/ijtm.2003.003424.
• Salvador, F; Martin, P; Piller, Frank (2009). "Cracking the code of mass customization" (PDF). Sloan Management Review. 50 (3): 71–78.
• Wang, Yue; Tseng, Mitchell (2011). "Adaptive Attribute Selection for Configurator Design via Shapley Value". Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 25 (1): 189–199. doi:10.1017/s0890060410000624. S2CID 14003617.
• Jalali, V; Leake, D (2012). "Customizing Question Selection in Conversational Case-Based Reasoning". Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference.
• Sabin, D; Weigel, R (1998). "Product configuration frameworks—a survey". IEEE Intelligent Systems. 14 (4): 42–49. doi:10.1109/5254.708432.
• McDermott, J (1980). "R1: An Expert in the Computer Systems Domain". Proceedings of the 1st Annual National Conference on Artificial Intelligence: 269–271.
• Frayman, F; Mittal, S (1987). "Cossack: A Constraint based expert system for configuration task". Knowledge-based Expert Systems in Engineering: Planning and Design: 143–166.
• Birmingham, W; Siewiorek, D (1988). "MICON: A single board computer synthesis tool". IEEE Circuits and Devices Magazine. 4 (1): 37–46. doi:10.1109/101.929. S2CID 21093059.
• Hamscher, W (1994). "Explaining Financial Results". Intelligent Systems in Accounting, Finance and Management. 3 (1): 1–19. doi:10.1002/j.1099-1174.1994.tb00051.x.
• Felfernig, A; Hotz, L; Bagley, C; Tiihonen, J (2014). Knowledge-based Configuration - From Research to Business Cases. Elsevier/Morgan Kaufmann. pp. 1–376. ISBN 9780124158696.

See also

• Mass customization
• Personalization
• Build to order
• Custom-Fit
• Dell
• Personalized marketing
• Product differentiation
• Product management
• Rapid manufacturing
• Knowledge-based Configuration