Learning orientation emphasizes the importance of learning from any experience. It is grounded on commitment to learn, shared vision, open‐mindedness, and knowledge sharing. Organizational knowledge management literature based on social complexity theory posits that learning orientation makes companies generate new knowledge through spontaneous multi-level iterations and self-organization. Challenges related to the current business environment requires companies to constantly adjust to remain competitive. Still, the mechanisms making learning-oriented companies more capable to develop innovative product have been scantly explored. Pertinent literature actually conjectures this relationship as spontaneous, directed, and unmediated. Moreover, Small and Medium Enterprises (SMEs)rarely represent the context of analysis of research on this topic. Frequently lacking resources to systematically pursue product innovation, SMEs rely on solutions deriving from the combination of internal knowledge and external sources; thus, these companies depend on learning orientation principles to remain innovative. In this vein, the research aims to understand how learning orientation allows product innovation in SMEs through the achievement of strategic flexibility. Structural equation modelling was used to analyse data from 300 British SMEs. The results demonstrate the mediating role of strategic flexibility in the relationships between learning orientation and product innovation. The importance of innovation culture also emerged.

This study explores the application of competency mapping models, incorporating in knowledge management for consulting firms. It evaluates 15 different models, focusing on their suitability for consulting contexts based on data collection, advantages, risks, and limitations. The findings indicate that AI and ML-enhanced competency mapping models are particularly more effective in consulting firms. Finally, the article proposes three key applications of these models for improving knowledge management in consulting firms via empowering communities and collaboration.

In the era of digitization and the growing flood of information, the automatic, role-specific identification of information is crucial. This research paper aims to investigate whether the adaptation of LLM is suitable for classifying information obtained from standards for corresponding role profiles. This research reveals that with systematic fine-tuning, prediction accuracy can be increased by almost 100%. The validation was carried out using a two-digit number of standards for three predefined roles and demonstrates the significant potential of LM for labelling content with regard to roles.

In today's competitive market, design firms are under pressure to enhance the speed of their decision-making processes to foster innovative products. Due to specialized nature of contemporary technology, enterprises are directed to consider Design for X factors during the product development process like environmental impact and production efficiency. This transformation leads to an increase in gaining knowledge from different fields. This paper presents a comprehensive framework for efficiently acquiring and applying knowledge, aimed at improving knowledge management and sharing practices.

The complexity of process plants and the growing demand for digitalization require efficient and accurate information retrieval throughout the lifecycle phases of a process plant. This paper discusses the concept of instantiation and introduces a method for identifying and multiplying required information in plant engineering using scalable so-called Instantiation Blocks linked to the Bill of Material. Core functionality, an ontology graph and a user interface based on Python and React are developed to demonstrate the implementation of the framework and validate its effectiveness in practice.

Quotation of engineer-to-order products provides substantial challenges in effectively managing engineering resources. This paper describes an approach that rationalizes this process by integrating multi-disciplinary design analysis and optimization with a new open-source library for managing engineering knowledge before and after optimization. The approach is applied and evaluated on mechanical rock excavation machines. Adapting the approach and considering the user feedback gathered can lead to an enhanced design space overview during quotation and thus more competitive product offerings.

To handle the increased complexity within the automotive industry, this paper introduces a guideline, which aims to support development service providers to examine the introduction and if applicable support the introduction of systems engineering. The initial verification was performed through applying the guideline at Porsche Engineering as an exemplary service provider. As a result, the success factors "knowledge basis" and "knowledge transfer" have been improved by two points on a 1-5 Likert-scale by introducing a SE process-specific knowledge platform and a defined knowledge transfer.

The manual execution of failure mode and effects analysis (FMEA) is time-consuming and error-prone. This article presents an approach in which large language models (LLMs) are integrated into FMEA. LLMs improve and accelerate FMEA with human in the loop. The discussion looks at software tools for FMEA and emphasizes that the tools must be tailored to the needs of the company. Our framework combines data collection, pre-processing and reliability assessment to automate FMEA. A case study validates this framework and demonstrates its efficiency and accuracy compared to manual FMEA.

Managing knowledge successfully is key for an organization to increase its innovative potential. The InKTI method supports the improvement of knowledge transfers in product and production engineering. To ensure acceptance, applicability, and contribution to success in practice, it is necessary to validate the InKTI method. This paper focuses on evaluating the contribution to success in a Live-Lab study with student engineering teams. Based on the results two consecutive field studies have been conducted to evaluate not only the success but also support, and applicability of the InKTI method.

Patents are an invaluable source of data that can be beneficial for Engineering Design (ED). Patenting is one of the main means for disclosing the inventive process. For this reason, the description of the problem solved should also be included in any patents.

The ED literature lacks a proper definition of a problem, resulting in a fragmented scenario. Prior studies have employed Text Mining (TM) to extract problems from patents. We argue that TM can assist ED researchers in understanding how problems are articulated in text. Based on the literature, we propose two hypotheses: (1) problem-related text exhibits a negative sentiment polarity compared to other sections of patents; (2) problem-related keywords identified in the literature are predominantly used to describe problems rather than other aspects.

We analyse Japanese patents to validate our hypotheses, since they explicit Problem and Solution in the abstract. Finally, we compare our results with a set of problem-related sentences extracted from USPTO patents.

Our study reveals a higher positive sentiment in problem-related sentences compared to solution-related ones and highlights the inadequacy of using problem-related keywords alone to differentiate between the two.

Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

In the development and production of new products, interdepartmental knowledge transfer is essential. Successful knowledge transfer faces several challenges, such as a lack of willingness to transfer knowledge or an inappropriate selection of tools. These can lead to the reduction of efficiency and effectiveness of knowledge transfers. Therefore, the InKTI – Interdepartmental Knowledge Transfer Improvement Method is developed to support the improvement (in terms of speed and quality) of knowledge transfers, particularly in product and production engineering.

This paper presents the first validation of the InKTI Method through a field study at the company Protektorwerk Florenz Maisch GmbH & Co. KG, which is a leading European company in the construction industry, to support the successful knowledge transfer into practice. Therefore, the research need is pointed out, and a concept for validation is developed and implemented. Afterward, the InKTI Method is evaluated based on its success, support as well as applicability.

The product engineering process as part of the product life cycle includes product and production system development as well as production. In integrated product and production engineering (PPE), knowledge transfer is an important success factor. Optimizing the efficiency and effectiveness of knowledge transfers can, for example, support the avoidance of costly, production-related changes to the product design. The current state of research describes different models of knowledge transfer as well as factors that influence it. Some results show how the speed and quality of knowledge transfer can be improved by implementing so-called interventions. However, those models either represent abstract contexts of knowledge transfer or focus only on product engineering. Therefore, a literature analysis is conducted to identify the system of objectives for a method, that supports the improvement of knowledge transfer in PPE. Subsequently, the system of objectives is operationalized to provide the basis for the InKTI – Interdepartmental Knowledge Transfer Improvement Method, which is applicable, supports the user in improving knowledge transfers in PPE, and aims to increase the quality and speed of knowledge transfers.

While volume-driven industries such as automotive are characterized by a high degree of data backflow across all production cycles, there is still a certain residue in the planning and construction of process plants. This is firstly due to the high proportion of customer-specific requirements and secondly to the significant amount of value added on site during construction. To handle recurring project-specific process plants as time- and cost-efficiently as possible, optimal information exchange among contractors of various disciplines and the plant developer is a prerequisite. For this purpose, a holistic digital representation of the plant is created, which consolidates all relevant information in one place serving as a foundation of multiple digital twins. An approach to identify and define relevant information depending on their subsequent use is developed. On this basis, a framework is proposed to enable a multipliable BOM-based automatic definition of information backflow to instantiate digital representations in parallel to the planning and construction process. Furthermore, project-specific contextual information will be captured and referenced in a structured form preventing their loss for subsequent similar projects.

In order to have a sustainable disassembly process, a successful decision-making based on reliable and up-to-date information should be made while taking into consideration sustainability indicators. In this context, The aim of this paper is to introduce a decision support system based on knowledge based and digital twin in order to help stakeholders to choose the most sustainable disassembly scenario .In this research, firstly, we presented the state of art of disassembly process, digital twin, knowledge based system and the merging of knowledge based system and digital twin for disassembly. Secondly, we presented the knowledge based digital twin (KBDTw) system framework for a sustainable disassembly process. Thirdly, a case study is presented about the use of KBDTw in the end-of-life of internet boxes. Finally, a conclusion and future work are conducted.

Complex research problems are increasingly addressed by interdisciplinary, collaborate research projects generating large amounts of heterogeneous amounts of data. The overarching processing, analysis and availability of data are critical success factors for these research efforts. Data repositories enable long term availability of such data for the scientific community. The findability and therefore reusability strongly builds on comprehensive annotations of datasets stored in repositories. Often generic metadata schema are used to annotate data. In this publication we describe the implementation of discipline specific metadata into a data repository to provide more contextual information about data. To avoid extra workload for researchers to provide such metadata a workflow with standardised data templates for automated metadata extraction during the ingest process has been developed. The enriched metadata are in the following used in the development of two repository plugins for data comparison and data visualisation. The added values of discipline-specific annotations and derived search features to support matching and reusable data is then demonstrated by use cases of two Collaborative Research Centres (CRC 1368 and CRC 1153).

In times of ‘grand challenges’, design theorists dealing with complex systems are facing a dilemma: grand challenges require rule breaking, but they also require the preservation, as much as possible, of existing resources, systems, know-how and societal values. Design for transition calls not for ‘creative destruction’, but for ‘creative preservation’. How do we model a design process that involves ‘creative preservation’?

Today, it is recognized that category/topos theory provides a solid foundation for modelling complex systems and their evolution in design processes. Category theory can account for a design process inside a given ‘theory of the object’, while topos theory and design theory can account for the phenomena whereby a design process is innovative to preserve the knowledge structure. At the heart of this creative preservation is sheafification.

In this study, we analyse the sheafification process using design theory. First, we characterize sheafification from a design perspective. Next, we propose a very simple illustration involving the sheafification of an ordinal 2 category presheaf. Finally, we show how sheafification can be used to enable ‘creative preservation’ in specific complex systems.

This study aims at revising the history of knowledge management in service design organizations to discover what sources, technologies, tools, and users have been used and how knowledge management may thus help to improve consultants’ performance. Also, this study sheds light on the importance of decomposing knowledge (knowledge-leveling being said in this article) before tackling knowledge management. Moreover, this study provides a real case study investigation of knowledge management in a service design organization. Through this investigation, the authors propose their knowledge-leveling classification model and how knowledge management activities satisfy each class. Thus, the authors showcase the essentiality of knowledge leveling in knowledge management.

This paper presents a focused examination of critical performance and design issues for the introduction of highly automated tractors and their user interfaces in agriculture. An industry that as of today mainly uses direct-controlled machines that at least to some extent have partly automated functionalities. Issues include out-of-the-loop unfamiliarity, interface complexity, automation transparency, and changing information modalities in teleoperation scenarios for former cabin-based operated machines. Selected evidence and accompanying concepts and findings from literature are put in context to each issue, informing a systematic design process that utilizes the frameworks of knowledge engineering and ecological interface design. The resulting user interface prototype is built upon the identified requirements in analysis and collected design guidelines, stemming from various research areas. The documentation of the consideration of these in context with additional requirements, such as complexity reduction, information interactivity, and users' existing experiences is meant to provide insights into the often opaque and art-like design space.