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Product configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.

Given a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product components, as well as the correlation among product components. A configuration was developed which takes XML‐based requirement specification as input and bases on a constraint knowledge base to produce product configuration as output consisting of a list of selected components and the structure and topology of the product. Three modules are developed, namely product data modelling, configuration knowledge generation and product configuration generation module. The proposed approach is implemented in the configuration knowledge generation module. The configuration generation module realizes a resolution of constraint satisfaction problem to generate the output configuration.

The significance and effectiveness of the proposed approach is demonstrated by its incorporation in our configuration system prototype. A case study was conducted and experimental results show that the approach is promising in finding constraints with given sufficient data.

Novel knowledge generation approach is proposed to assist constraint generation for Constraint‐based product configuration system.

Product configurator is a sales and production‐planning tool that helps to transform customer requirements into bills‐of‐materials, lists of features and cost estimations. The purpose of this paper is to introduce a method of how to analyse sales configuration models by using a design structure matrix (DSM) tool. By applying the DSM techniques, the sales configuration managers may sequence the product configuration questions and organize the connection to production.

First, the paper explains a sales configuration system structure from published academic and non‐academic works. These sources employ both theoretical and practical views on the topic of computer‐based sales expert systems. Second, the paper demonstrates an application of using DSM for configuration modelling.

The current sales configuration approaches include constraint‐based, rules‐based, and object‐oriented approaches. Product description methods vary, but the general problem remains the same: the configuration process should be designed in such a way that customer selections do not affect the previous selections. From the user point of view, answering the questions should be smooth and fast. In turn this will lead to the growing importance of building more effective product configuration models. DSM offers a systematic way to organise customer interface in sales configuration systems.

This paper analyses how DSM could help in planning product configuration modelling. Comparison of different sequences is presented. The examples used are hypothetical, but illustrate the suitability of DSM analysis. Companies are trying to establish easily configured product models, which are fast, flexible and cost‐effective for adjustments and modifications. Use of DSM may help in the roll‐out of sales configuration projects. DSM may also be used as a quick view to represent the complexity of product configurability. The future needs for configuration tools will be focused towards product model management from the technical limitations of different data storage approaches.

Configurator software creates product variants, which are logical descriptions of physical products. Variants have parameters which describe the customer‐made selections. The parameter selections may have interconnections between the choices. Some selections may affect further selections and some combinations may not be allowed for incompatibility, cost or safety reasons. There are several commercial software packages available for creating product configurations. Product description methods vary, but the general problem remains the same: the configuration process should be designed in such a way that customer selections do not affect the previous selections. Answering the questions should be smooth and fast. Configuration of complex products, for instance, airplanes, may include several sub‐systems and have various loops within the quotation process. The use of DSM may help in the roll‐out of sales configuration projects. DSM may also be used as a quick view to represent the complexity of product configurability.

The paper helps both researchers and practitioners to obtain a clearer view on the development of sales configuration systems and the potential of systematic DSM‐based product model analysis.

This study aims to investigate how configurations of boundary objects (BOs) support innovation teams in developing innovative product concepts. Specifically, it explores the effectiveness of different artefact configurations in facilitating collaboration and bridging knowledge boundaries during the concept development process.

The research is based on data from ten undergraduate innovation teams working with an industry partner in a creative industry. Six categories of BOs are identified, which serve as tools for collaboration. The study applies fsQCA (fuzzy-set qualitative comparative analysis) to analyse the configurations employed by the teams to bridge knowledge boundaries and support the development of innovative product concepts.

The findings of the study reveal two distinct groups of configurations: product envisioning and product design. The configurations within the “product envisioning” group support the activities of visioning and pivoting, enabling teams to innovate the product concept by altering the product vision. On the other hand, the configurations within the “product design” group facilitate experimenting, modelling and prototyping, allowing teams to design the attributes of the innovative product concept while maintaining the product vision.

This research contributes to the field of innovation by providing insights into the role of BOs and their configurations in supporting innovation teams during concept development. The results suggest that configurations of “product envisioning” support bridging semantic knowledge boundaries, while configurations within “product design” bridge pragmatic knowledge boundaries. This understanding contributes to the broader field of knowledge integration and innovation in design contexts.

This paper aims to explore the use of the knowledge management (KM) perspective for configuration projects. Configuration projects implement configurators as information technology systems that help companies manage the specification process of customised products. An effective method of retrieving and formalising knowledge for configurators is essential, because it can reduce the risk of unsuccessful implementation and the time and effort required for development. Unfortunately, no standard KM frameworks are available specifically for configuration projects. This study identifies the knowledge necessary for different phases of a configuration project (which knowledge, for what purpose and from what sources), examines how it is transformed during a configuration project (what KM activities and tools are used) and establishes how the knowledge can be documented for future maintenance and updates.

This paper proposes a four-step framework for making the KM process more efficient in configuration projects. The framework is based on the literature, developed in collaboration with industrial partners and tested on four configuration projects in two engineering companies. The framework is a structured KM approach designed to save time for both domain experts and the configuration team. The authors have used a qualitative exploratory design based on multiple data sources: documentation, workshops and participant observation.

The proposed framework comprises four steps: determination of the system’s scope, to establish the project’s goal based on stakeholders’ requirements and prioritise the required products and processes; knowledge acquisition, to classify the knowledge according to the desired output and identify different knowledge sources; modelling and knowledge validation; and documentation and maintenance, to ensure that the KM system can be maintained and updated in the future.

Because the framework is tested on a limited number of cases, its generalisability may be limited. However, focusing on a few case applications allows us to assess the effectiveness of the framework in detail and in depth to identify the practical challenges of applying it. The results of the tests support the framework’s validity. Although the framework is designed mainly for engineering companies, other industries could benefit from using it as well.

The individual steps of the framework create a structured approach for the KM process. Thus, the approach can save both time and resources for companies, without the need for additional investment.

A standard framework is lacking in the literature on KM for configuration projects. This study fills that gap by developing a KM framework for configuration projects, based on KM frameworks developed for IT projects, and KM tools.

This paper explores how key insights from highly cited and well-used frameworks that describe the strategies and structures of MNCs are reflected in the international configurations of US MNCs. After reviewing existing frameworks that highlight different MNC choices regarding the integration, responsiveness, and dispersion of firm value chain activities, I perform a cluster analysis on a comprehensive and confidential database of US MNCs. The results reveal five configurations which both support the importance of key insights from existing frameworks while at the same time highlighting underexplored configuration characteristics, like the low levels of integration in US MNCs, the global sourcing arrangements for accessing foreign inputs and distribution, different approaches to regional expansion, and the limited geographic expansion of US MNCs pursuing product diversification. I argue that these underexplored characteristics suggest directions for future research to better reflect the international configuration choices of MNCs.

Product configurators are expert systems that support product customization by defining how predefined entities and their properties may be combined. Developers of configuration systems act as designers, although they do not often recognize that they are performing as such. Moreover, exploring solution spaces is typically not integral to configuration projects, as this task is typically perceived as mapping existing knowledge to the configurator. This article argues that developing configurators may be understood by distinguishing between the problem and solution spaces using design thinking (DT).

A multiple-case-study approach with four configuration projects is adopted to study two projects involving DT and compare them to two similar projects not involving DT. Data collection depended on multiple data sources via workshops and semi-structured interviews.

First, DT methods and concept–knowledge (C-K) theory are integrated into configuration projects. Second, the application of DT during configurator development is presented through workshops and interviews, which demonstrates the benefits of DT in overcoming existing challenges.

The case studies demonstrate the successful implementation of DT in developing configurators. However, a limited number of cases in only one company limits the generalizability of the results.

The framework's individual steps create a structured approach to supporting industrial companies with a toolbox of DT techniques and methods for configuration projects.

The results show that the application of DT to configuration projects can improve user motivation, stakeholder satisfaction and knowledge acquisition.

The purpose of this paper is to propose a three-staged approach to configuration change management that uses a combination of complexity analysis, data visualization, and algorithmic validation to assist in validating configuration changes.

In order to accomplish the above purpose, the authors conducted a review of existing configuration management practices. This was followed by an in-depth case study of the configuration management practices of a major automotive OEM. The primary means of data collection for the case study were interviews, ethnographic study, and document analysis. Based on the results of the case study, a set of support tools is proposed to assist in the configuration management process.

Through the case study, the authors identified that the OEM used a configuration management method that largely represented the rule-based reasoning methods identified in the literature review. In addition, many of the associated challenges are present, primarily, the difficulty in making changes to the rule system and evaluating the changes.

The primary limitation is that the case study was based on a single OEM. However, the results are in line with other practices identified in the literature review. Therefore, it is expected that the findings and recommendations should hold true in other applications.

A set of configuration management tools and associated requirements are identified and defined that could be used to assist companies in the automotive industry, and perhaps others, in managing their option changes as they continue to move towards full mass customization of products.

The proposed approach for configuration management has not been seen in any other organization. The value of this paper is in the effectiveness of the proposed approach in assisting in the configuration change management process.

Configuration systems are used as a means for efficient design of customer tailored product service systems (PSS). In PSS configuration, mapping customer needs with optimal configuration of PSS components have become much more challenging, because more knowledge with personalization aspects has to be considered. However, the extant techniques are hard to be applied to acquire personalized configuration rules. The purpose of this paper is to extract the configuration rule knowledge in symbolism formulation from historical data.

Customer characteristics (CCs) are defined and introduced into the construction of configuration rules. Personalized PSS configuration rules (PCRs) are thereby proposed to collect and represent more knowledge. An approach combining Local Cluster Neural Network and Rulex algorithm is proposed to extract rule knowledge from historical data.

The personalized configuration rules with CCs are able to alleviate the burden of customers in expressing functional requirements. Furthermore, in the long-term relationship with a customer in PSS realization, PSS offerings can be reconfigured according to the changing CCs with the guide of PCRs.

The contribution of this paper lies in introducing the attribute of CCs into the antecedents of PCRs and proposing the neural networks-based approach to extracting the rule knowledge from historical data.

The purpose of this paper is to explore the impact of configuration on supply network capability. It was believed that a configuration perspective might provide new insights on the capability and performance of supply networks, a gap in the literature, and provide a basis for the development of tools to aid their analysis and design.

The methodology involved the development of a configuration definition and mapping approach extending established strategic and firm level constructs to the network operational level. The resulting tools were tested and refined in a series of case studies across a range of sectors and value chain models. Supply network capability assessments, from the perspective of the focal firm, were then compared with their configuration profiles.

The configuration mapping tools were found to give new insights into the structure of supply networks and allow comparisons to be made across sectors and business models through the use of consistent and quantitative methods and common presentation. They provide the foundations for linking configuration to capability and performance, and contribute to supply network design and development by highlighting the intrinsic capabilities associated with different configurations.

Although multiple case networks have been investigated, the configuration exemplars remain suggestive models. The research suggests that a re‐evaluation of operational process excellence models is needed, where the link between process maturity and performance may require a configuration context.

Advantages of particular configurations have been identified with implications for supply network development and industrial policy.

The paper seeks to develop established strategic management configuration concepts to the analysis and design of supply networks by providing a robust operational definition of supply network configuration and novel tools for their mapping and assessment.

The paper aims to address the question of how and how often companies change their manufacturing strategy in the medium and long run, thus addressing a lack of evidence in the literature.

This paper explores the movements made by companies among four manufacturing strategy configurations drawn from the literature (market‐based, product‐based, capability‐based and price‐based configuration). Analyses are based on three longitudinal samples from the International Manufacturing Strategy Survey (IMSS) database.

Results show that while strategic configurations are rather stable, many companies do indeed change strategy and identifies which patterns of change prevail. Product‐based strategy is the most‐widely spread and most stable strategy. Capability‐based competition is the rising star. The market‐based strategy is struggling and price‐based competition is on its way out.

The main limitation is the small size of longitudinal samples, leading to tentative propositions for further testing.

No strategic configuration appears to be the final “maturity” target for manufacturers. Companies select their configurations according to life cycle of the organization and market competition.

The paper contributes to fill a lack of longitudinal evidence of strategic change and flexibility of manufacturing companies.