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Product Configuration Systems: State of the Art, Conceptualization and Extensions
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Product configurators are considered to be among the most successful applications of artificial intelligence technology. In this paper, we determine different conceptualizations of configurators and condense them in a comprehensive morphological box, which should support configurator designers as well as decision makers in selecting the right system. The analysis of the criteria according to which configurators that are designed thus far reveals a neglect of the front-end perspective. Therefore, it is relevant to extend configurators with a front-end component assisting customers during product configuration through advisory. We develop a framework describing the main requirements on an advisory system and propose the technical infrastructure for its implementation. Finally, the advisory system and the configurator are integrated into a comprehensive interaction system.
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... This me the complexity depends on the size of the input data and to solve the problems of inference and feasibility of co it is necessary to reduce the set of input data. To avoid such a problem, it is proposed to divide the knowledg which traditionally consists of TBox and ABox into two components, so that the subject area is described DL (19) Indeed, the condition ...
... It means the need to find all compatible compon find a list of components that are compatible with the selected. This problem more detail describe in (Trentin 2012), (Thorsten, et al., 2004), (Wang, et al., 2020). As specified in these works the quality of the result depend quality of metadata. ...
... It means the need to find all compatible compone find a list of components that are compatible with the selected. This problem more detail describe in (Trentin, 2012), (Thorsten, et al., 2004), (Wang, et al., 2020). As specified in these works the quality of the result depends quality of metadata. ...
Big data refers to large volumes, complex data sets with various autonomous sources, characterized by continuous growth. Data storage and data collection capabilities are now rapidly expanding in all fields of science and technology due to the rapid development of networks. Evaluating the quality of data is a difficult task in the context of big data, because the speed of semantic data reasoning directly depends on its quality. The appropriate strategies are necessary to evaluate and assess data quality according to the huge amount of data and its rapid generation. Managing a large volume of heterogeneous and distributed data requires defining and continuously updating metadata describing various aspects of data semantics and its quality, such as conformance to metadata schema, provenance, reliability, accuracy and other properties. The article examines the problem of evaluating the quality of big data in the semantic environment. The definition of big data and its semantics is given below and there is a short excursion on a theory of quality assessment. The model and its components which allow to form and specify metrics for quality have already been developed. This model includes such components as: quality characteristics; quality metric; quality system; quality policy. A quality model for big data that defines the main components and requirements for data evaluation has already been proposed. In particular, such evaluation components as: accessibility, relevance, popularity, compliance with the standard, consistency, etc. are highlighted. The problem of inference complexity is demonstrated in the article. Approaches to improving fast semantic inference through materialization and division of the knowledge base into two components, which are expressed by different dialects of descriptive logic, are also considered below. The materialization of big data makes it possible to significantly speed up the processing of requests for information extraction. It is demonstrated how the quality of metadata affects materialization. The proposed model of the knowledge base allows increasing the qualitative indicators of the reasoning speed.
... Early configuration systems were implemented on the basis of rule-based technologies that works by executing rules with the following form: "if condition then consequence" [Blec04]. ...
... The probably most well-known rule-based configuration system is XCON, which was used to configure computer systems [McDe82]. The main drawbacks of rule-based system are ascribed to the problems encountered during knowledge acquisition, consistency checking and knowledge maintenance [Günt99,Blec04]. ...
... Another type of configuration system is case-based that is developed based on the assumption that similar problems have similar solutions [Blec04]. Solving a problem is carried out by finding and adapting a previous solution to a similar problem [Petr12]. ...
Since only low detailed knowledge is available in the conceptual design stage, many requirements are subjected to uncertainties which lead to design changes throughout the development process. In this context, the development of robust solutions is investigated in this work in order to deal with the continuous design changes within minimal effort.
Design solution space (DSS) exploration is proposed as a new method to integrate robustness in a simple manner, since the uncertainties of the requirements can be represented through a set of solutions instead of a rigid one. Based on the analysis of modeling approaches for variable and multi-variant product models, the generative design approach (GDA) was elaborated as a practical approach for DSS exploration. Therefore, a GDA-based development process was proposed that enables designers to search for the most robust solution. The uncertainties of requirements are mapped via a set of design elements and their parametrizations. As a result, a large DSS is explored and represented via GDA model that delivers high robustness and flexibility for handling the requirements uncertainties.
The implementation of a vehicle door structure development demonstrated that the GDA-based development process is capable of developing robust solution against the uncertainties of requirements through DSS exploration.
... This work sees the concept of context-awareness as a tool to provide the required adaptability and a fit between task and information presentation. A context-aware chatbot holds the potential to ultimately lead to a higher task performance for users [7,8]. This work therefore investigates the design of a context-aware chatbot for product configuration. ...
... So far, only few technologies for assisting a configuration process have been tested. Most software based-configuration interfaces are available to customers as web forms [3,5,8]. However, they come with problems like limited flexibility and intuitiveness as well as complexity, which will be discussed in more detail in the subsequent section. ...
Product configurators provide an interface for customizing complex products. However, large form-based configurators overwhelm many end users and are often considered expert tools. This paper therefore addresses the problem of the complexity of current product configurators. Since chatbots can respond flexibly to queries and offer a natural language interface, they have the potential to simplify the configuration process. In this paper, we present a chatbot for product configuration that we developed using the design science research approach and in collaboration with an industrial partner. We derive design principles for configurator chatbots from user interviews that relate in particular to the flexibility of the chatbot compared to a static process. These design principles were implemented in our chatbot artifact which was evaluated in an online experiment (N = 12) and compared to a baseline chatbot with an inflexible configuration process. Our results indicate that the proposed design increased dependability and configuration performance, and overall had positive effects on participants’ engagement. Thus, this study contributes prescriptive knowledge on the design of context-aware chatbots for product configuration and a novel artifact in the form of a context-aware configurator chatbot prototype.
... This work sees the concept of context-awareness as a tool to provide the required adaptability and a fit between task and information presentation. A context-aware chatbot holds the potential to ultimately lead to a higher task performance for users [7,8]. This work therefore investigates the design of a context-aware chatbot for product configuration. ...
... So far, only few technologies for assisting a configuration process have been tested. Most software based-configuration interfaces are available to customers as web forms [3,5,8]. However, they come with problems like limited flexibility and intuitiveness as well as complexity, which will be discussed in more detail in the subsequent section. ...
Product configurators provide an interface for customizing complex products. However, large form-based configurators overwhelm many end users and are often considered expert tools. This paper therefore addresses the problem of the complexity of current product configurators. Since chatbots can respond flexibly to queries and offer a natural language interface, they have the potential to simplify the configuration process. In this paper, we present a chatbot for product configuration that we developed using the design science research approach and in collaboration with an industrial partner. We derive design principles for configurator chatbots from user interviews that relate in particular to the flexibility of the chatbot compared to a static process. These design principles were implemented in our chatbot artifact which was evaluated in an online experiment (N=12) and compared to a baseline chatbot with an inflexible configuration process. Our results indicate that the proposed design increased dependability and configuration performance, and overall had positive effects on participants' engagement. Thus, this study contributes prescriptive knowledge on the design of context-aware chatbots for product configuration and a novel artifact in the form of a context-aware configurator chatbot prototype.
... In this context, there is much discussion about the acquisition and representation of domain knowledge [40]. The systems contain or need direct access to knowledge bases with product knowledge and configuration rules [3]. Knowledge is not only seen as a fundamental resource in the MC discourse. ...
... One of the most frequent applications of analytics in this context is to determine the best possible configurations or to make recommendations to the user based on specified requirements. A frequently discussed approach to give recommendations is casebased reasoning, which retrieves the best fitting solutions from a case database [3] via case similarities. From a SDL perspective on a service ecosystem, the importance of decision support through so-called cognitive computing is growing, since decision making by actors is supported or replaced using modern analysis methods [36]. ...
In recent years, the discussion about how companies integrate new technologies into their value creation and how this affects their business has intensified. The trend towards digitalization is particularly challenging for smaller, value co-creating (VCC) companies in networks, yet little research has been done in this context. In response, this paper identifies four key technologies for promoting network VCC: (1) a service configuration system, (2) a centralized knowledge base, (3) an analytics system, and (4) a shared IT platform. We conducted a single embedded case study in a company network introducing these key technologies and thereby digitally transforming its VCC. Our results show how the companies in the network are approaching their transformation and what the impact and role of the technologies in their network VCC are.
... Articles were divided into two groups with reference to discussing the VCCFs related to the number of components and/or the number of end products. A number of articles discussed methods for quantifying complexity costs [26,27,35,43,[47][48][49][50][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. Table 1 lists the VCCFs relating to supplying and procuring materials found in the literature. ...
Many manufacturing companies experience increasing product variety, which not only leads to significantly increased operations’ costs but also to an uneven distribution of these costs across product variants. To deal with this challenge, managers need to accurately quantify the costs of product variety-induced complexity to understand its impact on company revenues. However, quantifying complexity costs is no trivial task, as both the variety-related factors that generate more complexity and the costs implied by these factors are specific to each company. Therefore, we extensively reviewed the relevant literature to build a list of possible complexity cost factors generated by product variety, which are here named variety-induced complexity cost factors (VCCFs). This list is intended to be used by managers as a reference set of VCCFs to help identify and subsequently quantify the costs of product variety-related complexity in manufacturing companies and finally attribute them to end product variants. To evaluate the usefulness of this list, the identified VCCFs were examined in six companies using both the judgment of managers and data from their enterprise resource planning systems. This empirical examination not only provides evidence of the usefulness of this list but also data, rarely available in academic literature, on the costs of product variety-induced complexity and its proportion of company revenue. It also suggests that it is important to research effective ways to get, elaborate, and present data to calculate the costs of product variety-related complexity and reports a viable approach used in six companies.
... The reported PCS challenges are investigated and categorised in the literature (Kristjansdottir et al., 2018a;Shafiee, 2017). More specifically, these challenges are listed as resource management (Forza and Salvador, 2002b;Haug et al., 2012;Heiskala et al., 2005;Shafiee et al., 2014), product complexity (Heiskala et al., 2007;Forza and Salvador, 2002a, b;, IT challenges (Heiskala et al., 2007;Forza and Salvador, 2002a;Blecker et al., 2004;Felfernig et al., 2000a;Shafiee et al., 2017), knowledge acquisition challenges (Heiskala et al., 2007;Haug and Hvam, 2007;Shafiee et al., 2018), and organisational challenges (Heiskala et al., 2007;Felfernig et al., 2000a;Haug and Hvam, 2007). Computer-Aided Software Engineering (CASE) tools are used for developing high-quality, defect-free, and maintainable software and are often associated with methods for the development of information systems together with automated tools that can be used in a software development process (Kuhn, 1989;Zeng et al., 2013). ...
Computer-Aided Software Engineering (CASE) tools are popular software programs to support the members of the development team (including analysts, designers, coders, database administrators, and project managers) in building new software systems. Up-to-date and consistent knowledge representation and documentation is crucial for companies developing Product Configuration Systems (PCSs). The literature reports various challenges in PCS development, such as maintenance, documentation, knowledge management , resource and time management, system quality, and communication with domain experts as particularly problematic. A CASE tool tailored to the specific needs of PCS development can prove to be useful in tackling at least some of these challenges. Such a CASE tool has to support product models, which means it has to not only allow the representation of the product core architecture and the optional selectable features, but also ensure consistency between representations (views) and deliver forward or reverse engineering. This enables support and automates, at least partially, the development in general and the implementation stage. The focus and main contribution of this paper is twofold. First, we describe the view-based approach required to fully conceptualise the knowledge to generate PCS software from the CASE tool. To this end, the tool indeed includes four different views to build or edit all the required knowledge. Second, we validate this CASE tool within two case companies, wherein we evaluate its application on a project each time it is used. The results show that the use of the CASE tool increases the quality of PCS documentation and saves time and resources while also improving the PCS's overall quality.
... Visualization of the main results was performed using the method of the morphological box, which main advantage is a presentation of different alternatives at the same time (Blecker et al., 2004;Koch, 2015). ...
Purpose:New technical solutions in logistics change the ways of working within warehouses on differentlevels, from warehouse layouts and concepts of goods pick-ing to process planning and human resources. Thus, disrupting the previous practice in its core.Methodology: In order to evaluate the impact of the new technologies on the ware-house operations, the multiple case study approach was used. To gain a deeper un-derstanding of the changes within logistics processes, the results of the deep-dive analysis are summarized using morphologic box methodology.Findings:Presented solutions such as AutoStore, Kiva and CarryPick can lead to a substantial increase in the speed of order picking while staying very flexible and de-manding significantly less of expensive warehouse space. Still, the implementation of these technologies requires a systematic approach with clearly stated goals.Originality:In contrary to available papers which are concentrating on a single case study with application of one technology at one particular company, the presented paper analyses several solutions comparing them with each other. Additionally, the research evaluates the impact of the technologies on logistic processes and ware-house layouts. Thus, creating value for practitioners looking for solutions to optimize intralogistics.
... Product configurators are a subclass of expert systems, which represent one of the most successful applications of artificial intelligence principles in recent decades [1][2][3]. Product configurators include a knowledge base with information about product features, product structure, production processes, costs and prices [4], allowing them to simulate work normally carried out by product experts, such as sales staff and engineers. Automating and masscustomising knowledge work through the use of configurators can achieve a number of benefits, including: reduced time for generating quotes, fewer errors in product specifications, less resource use for product specification, more exact quotes, higher similarity of sold products, better supplier communication, and more [5][6][7]4,[8][9][10][11][12][13]. ...
Several studies reporting benefits from the use of product configurators in engineering-oriented companies can be found in the literature. On the other hand, some literature also claims that companies abandon configurator projects in many cases. Such claims are, however, supported only by references to the experience of the authors and do not involve much information about the projects. This represents a significant gap in the literature, as insights from failed configurator projects could help prevent other companies from making similar mistakes and promote the development of more appropriate methods and tools. To address this gap in the literature, this paper outlines an overall framework for understanding failure in configuration projects. Using this framework, eight failed configurator projects are investigated. These cases demonstrate that poor decision-making in one phase can have escalating negative consequences in the subsequent phases until the configurator project eventually fails. Furthermore, based on the insights obtained from the eight projects studied, this paper defines a set of guidelines for avoiding failure in configurator projects.
Mass customization is an alternative approach in which companies abandon costly market research tools and instead involve their customers into the product development process. Although self-designing a product via toolkits requires additional effort for customers, they are willing to pay a price premium. However, users indicate that they would pay an even higher price premium for a product designed in an ideal toolkit, which indicates that toolkits currently do not support their users in the best possible way. The user and the toolkit constitute a joint cognitive system to perform the task of “self-designing a product”, therefore the configuration of an ideal toolkit is based on the user's mental capabilities. The task is conceptualized as a self-design process subdivided into three episodes: (i) exploration, (ii) generation, and (iii) evaluation. The task of self-designing a product is a mental activity that is executed in the brain of the designer, where information regarding the design is processed. Based on the information processing theory we developed a model that indicates areas, where the cognitive skills of users are limited. These action points show where the toolkit can (even better) support the user during the self-design process. Based on this, we want to formulate design principles for a toolkit that supports the user as best as possible during the self-design process.KeywordsToolkits for mass customizationCognitive ergonomicsSelf-design process
This article presents a generalized ontology of product configuration as a step towards a general ontology of config-uration, which is needed to reuse and share configuration knowledge. The ontology presented consists of a set of concepts for representing the knowledge on a configuration and the restrictions on possible configurations. The ontol-ogy is based on a synthesis of the main approaches to configuration. Earlier approaches are extended with new concepts arising from our practical experience on configurable products. The concepts include components, attributes, re-sources, ports, contexts, functions, constraints, and relations between these. The main contributions of this work are in the detailed conceptualization of knowledge on product structures and in extending the resource concept with contexts for limiting the availability and use of resources. In addition, constraint sets representing different views on the product are introduced. The ontology is compared with the previous work on configuration. It covers all the principal ap-proaches, that is, connection-based, structure-based, resource-based, and function-based approaches to configuration. The dependencies between the concepts arising from different conceptualizations are briefly analyzed. Several ways in which the ontology could be extended are pointed out.
Product configurators have become an important information technology for the sales, order and delivery processes of many companies. In this article we discuss the types of configurable products and configuration related processes that can gain the most from such technology. Configurable products transfer much of the design work from the sales-delivery process to the R&D process. This requires systemizing the product and the related product knowledge. We present a general model of the R&D and sales-delivery processes for configurable products. The major benefits and problems of configurable products are then discussed.
This product and process-oriented view serves as a basis for our treatment of product configurators. Configurators with up-to-date product information allow non-product-experts to make error-free sales specifications and production orders. Configurators also reduce lead-times in the sales-delivery process. The major problem with product configurators are the long-term management and maintenance of the product knowledge as product models and product instances evolve.
The underlying thesis in this article is that product configurators on their own are not enough to make the sales and order fulfilment processes more efficient. The success of a configuration system in any company is based on adequate systemisation of the product, in some cases even re-designing the product for configurability, and the systemisation and reengineering of the configuration related processes.
Kansei Engineering was developed as a consumer-oriented technology for new product development. It is defined as “translating technology of a consumer's feeling and image for a product into design elements”. Kansei Engineering (KE) technology is classified into three types, KE Type I, II, and III. KE Type I is a category classification on the new product toward the design elements. Type II utilizes the current computer technologies such as Expert System, Neural Network Model and Genetic Algorithm. Type III is a model using a mathematical structure.Kansei Engineering has permeated Japanese industries, including automotive, electrical appliance, construction, clothing and so forth. The successful companies using Kansei Engineering benefited from good sales regarding the new consumer-oriented products.Relevance to industryKansei Engineering is utilized in the automotive, electrical appliance, construction, clothing and other industries. This paper provides help to potential product designers in these industries.
The configuration of technical systems is one of the most successful application areas of knowledge-based systems. This article overviews and evaluates the developed representation technologies and problem-solving methods, successful application fields and software-tools for configuration of the last few years. Current research themes and perspectives for the application of knowledge-based systems are presented as an outlook for the future.
http://www.bracil.net/edward/fcs.html
Copyright with Thom Fruehwirth, Books on Demand
Resource balancing avoids the knowledge-base maintenance
bottlenecks inherent in many rule-based configuration approaches. Domain
knowledge is based directly on technical relationships. Control
knowledge is needed only to fine-tune the configuration process