Knowledge Management

Agile Use of Knowledge for Innovations

  • Problem
  • Practices
  • Benefits

Successful knowledge management is a challenge for many companies:

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Employees often do not know where which knowledge can be found in the company and do not exchange information sufficiently.

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Companies struggle with documenting the knowledge of their employees in the long term (e.g. a great share of knowledge is lost when employees leave).

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Existing IT systems for storing knowledge are not user-oriented and, therefor used insufficiently.

Relevance of Knowledge Management - Quotes from the CRC 768 Colloquia.

Interdisciplinary Understanding

Don’t reinvent the wheel

Effective Search

Process Thinking

Availability

Build TRUST

Knowing WHO knows What

Knowledge Mangement in Innovation Environments Explained in one Minute
The Challenge of Knowledge Management: A Picture Says More Than 1000 words

The THREE levels for effective measures of successful knowledge management

In innovation processes, knowledge is a key resource for companies. However, knowledge resources are usually distributed among different entities, e.g. IT systems, experts and processes.

Successful knowledge management helps to identify, expand and use knowledge resources in an agile way. For this purpose, the CRC 768 Use Case “Knowledge Management” has developed a socio-technical approach which is based on findings from various scientific disciplines (see figure) and addresses the following central questions:

  • How can existing knowledge be made accessible to humans and machines?
  • How can individual knowledge be better interlinked?
  • Which organizational structures can support knowledge management?
Model-Based Knowledge Formalization

This perspective toolbox offers ideas for the formalization of knowledge. It …

  • … offers application-oriented modeling techniques to formalize knowledge.
  • … demonstrates three approaches to make existing knowledge available for humans and machines
  • … focuses on the important aspects of reusability and extendibility of knowledge.

Three technical tools for innovative knowledge management

Theoretical Background 

Question: How can information technology make existing knowledge available for humans and machines in an adaptive, user-centered, and practical way?

Computers and communication systems “are good at capturing, transforming, and distributing highly structured knowledge that changes rapidly” (Davenport, 1996). Technological systems for knowledge management should capture users’ existing knowledge and help users acquire additional knowledge.

Technological approaches range from paper-based and simple computer systems to document management and expert systems. While the number of approaches has increased rapidly due to technological progress, we argue that these approaches still cannot keep pace with technical and organizational developments and changing user needs, as they rely on static rather than flexible systems and changes are time- and cost-intensive.

We propose adopting model-based techniques that support the technological structuring, transformation, and distribution of knowledge, i.e. the exchange of knowledge carriers such as models and documents between humans and computers. When applied appropriately, modeling and models can make the following contributions to knowledge management: (a) dynamic and application-oriented formalization of knowledge, (b) reusability and expandability of knowledge over time and (c) availability and comprehensibility for both humans and machines.

Social Perspectives on Successful Knowledge Management

The success of knowledge management depends – besides technical and organizational aspects – on individuals’ willingness to share knowledge and social processes. The CRC 768 therefore developed an interdisciplinary, socio-technical framework for knowledge management [1] that incorporates the analysis and design of social processes related to the success of knowledge management. Thus, research of the CRC 768 also addressed individual and social processes explaining how teams and networks of teams successfully process and share knowledge for innovation work.

In the following you will find ideas, concepts, and research from psychology as well as a concrete instrument of the CRC 768 that help teams to improve social organization of their existing knowledge.

A social tool for innovative knowledge management

Theoretical Background

Question: How is knowledge which is distributed among individuals within and between teams, made accessible for innovation work through communication?

 Transactive Memory Systems

Practitioners often report that employees are not willing to share their knowledge or simply don`t know who knows what. Daniel Wegner’s social-psychological theory of transactive memory systems (TMS) is helpful to better understand these issues. The theory explains how individuals collectively link, share, and distribute knowledge: Team members develop and use an understanding of ‘who knows what’ through communication. This enables the efficient distribution of knowledge and specialization, supports trust in each other’s knowledge, and fosters coordinated collaboration. TMS positively relate to team performance and innovation and research of the CRC 768 illustrated that a well-established TMS within teams positively relates to the team’s ability to successfully handle recurring (cyclical) events [2]. Thus, TMS development should be fostered.

 

Approaches for fostering TMSs within and between teams

Based on TMS theory, the CRC 768 developed a visualization-based instrument for team TMS development, which helps team members to improve their shared knowledge about who knows what‘. Further, the tool systematically captures and reflects the communication processes of a TMS. Further information on the tool is provided below.

Research of the CRC 768 further explored social aspects of successful knowledge sharing between teams [3] and shed light on the role of leadership for TMS development [4]. Interview-based studies showed that providing resources for knowledge sharing between teams, fostering an agile working environment, and highlighting interdependencies and communication within and between teams are essential for TMS and successful knowledge sharing in networks of teams.

Organizational Learning and Institutional Reflexivity

The innovation toolbox offers ideas for the revision and the improvement of knowledge management practices in organizations. It …

  • … offers methods for knowledge management in highly innovative environments,
  • … demonstrates five different use cases for developing organizational learning, and
  • … provides the scientific background of institutional reflexivity in a nutshell.

 

Five innovative tools for knowledge management

Theoretical Background

Question: How can organizations develop the ability to develop constantly new knowledge in a reflexive way and from within?

Change has become a permanent condition of organizational reality. In order to deal with this situation, organizations need the ability to manage change as a constant condition in all organizational areas. This comprises the ability to identify where and when what kind of new knowledge is required and how existing valuable knowledge can be maintained. It also comprises the ability to find a balance between stability and change to avoid an overestimation of change and new knowledge.

The organizational ability to constantly question standard procedures and develop productive and creative ways of exploration is called institutional reflexivity. It refers to an organization’s ability to systematically question its routines, create interventions, and use the resulting ruptures in a productive way. The goal of institutional reflexivity is to enable organizational change and innovation; consequently, successful institutional reflexivity results in systematic change.

Knowledge Management is not only crucial for enabling systemic change but must also be subject to constant revision and improvement. Therefore, organizations need to develop methods that help to regularly revise and update practices of knowledge management.

Sources

[1] Gammel, J. H., Koltun, G., (e. c.), Buchholz, J.,  Drewlani, T., Wissel J., Hollauer, C., Kugler, K. G., Zaggl, M., Vogel-Heuser, B. (accepted). A framework integrating technical, social, and managerial aspects of effective knowledge management. Proceedings of the 20th European Conference on Knowlede Management.

[2] Gammel, J. H., Reif, J. A. M. (e. c.), Kugler, K. G., & Brodbeck, F. C. (2018, June). Understanding the Relationship between Transactive Memory Systems and Team Performance: The Mediating Role of Cycle Management in Teams. Poster presented at the 29th International Congress of Applied Psychology Montréal, Québec Canada.

[3] Gammel, J. H., Kugler, K. G., & Brodbeck, F. C. (2016, Sept). Wissensaustausch und Innovationen in vernetzten Teams: Entwicklung und Validierung eines Modells effektiver transaktiver Wissenssysteme in Multiteam-Systemen. Poster präsentiert auf dem 50. Kongress der Deutschen Gesellschaft für Psychologie, Leipzig, Germany.

[4] Seeholzer, S., & Gammel, J. H. (2018, Sept). Wie beeinflusst Führung die Entwicklung transaktiver Wissenssysteme in Multiteam Systemen? - Eine explorative Interviewstudie. Poster präsentiert auf dem 51. Kongress der Deutschen Gesellschaft für Psychologie, Frankfurt, Germany.

[5] Großmann, D. J.; Kasperek, D.; Stahl, B.; Lohmann, B.; Maurer, M. (2015). Supporting PSS
Innovation Processes by an Integrating Model Grid. 7th Industrial Product-Service Systems Conference

[6] Hollauer, C,; Wilberg, J.; Omer, M. (2015). A Matrix-based Framework to Support Dynamic
Modeling of Sociotechnical Systems . Modeling and managing complex systems (DSM Conference)

[7] Koltun, G. D., Feldmann, S., Schütz, D., & Vogel-Heuser, B. (2017, March). Model-document coupling in aPS engineering: Challenges and requirements engineering use case. In Industrial Technology (ICIT), 2017 IEEE International Conference on (pp. 1177-1182). IEEE.

Gennadiy Koltun Foto (Medium)

Ing. Gennadiy Koltun

main research: Model-Based Systems Engineering, Model-Document Coupling of Engineering Models and Document, Knowledge Management in the context of cyclic innovations.

phone: +49 89 289 16451

E-Mail: gennadiy.koltun@tum.de

felix_ocker

Felix Ocker, M.Sc.

main research: knowledge formalization and inconsistency management in interdisciplinary engineering.

phone: +49 89 289 16440

E-Mail: felix.ocker@tum.de

ChristophHollauer

Ing. Christoph Hollauer

main research: Model-Based Systems Engineering, Model-Document Coupling of Engineering Models and Document, Knowledge Management in the context of cyclic innovations.

Kontakt über Gennadiy Koltun

josef_gammel

Josef Gammel, M.Sc.

main research: Model-Based Systems Engineering, Model-Document Coupling of Engineering Models and Document, Knowledge Management in the context of cyclic innovations.

phone: +49 89 2189 5897

E-Mail: josef.gammel@psy.lmu.de

Tobias Drewlani, M.A.

main research: Model-Based Systems Engineering, Model-Document Coupling of Engineering Models and Document, Knowledge Management in the context of cyclic innovations.

Tel.: +49 289 29225

E-Mail: tobias.drewlani@tum.de

Johan Buchholz, M.A.

main research: Dynamics of digital change projects in organizations, Sociology of work and organizational sociology, Global learning, North-South-Relations and engineering development cooperation.

Tel.: +49 289 29226

E-Mail: johan.buchholz@tum.de

Juliane Wissel, M.Sc.

main research: User-manufacturer interactions as bidirectional information flows in PSS innovation processes, possibilities for optimal design of user-manufacturer interaction.

Tel.: +49 289 28404

E-Mail: juliane.wissel@tum.de

Gennadiy Koltun Foto (Medium)

Ing. Gennadiy Koltun

main research: Model-Based Systems Engineering, Model-Document Coupling of Engineering Models and Document, Knowledge Management in the context of cyclic innovations.

phone: +49 89 289 16451

E-Mail: gennadiy.koltun@tum.de

The CRC 768 Use Case Knowledge Management provides a socio-technical framework for knowledge management based on theories from the fields of information systems technology, psychology, and sociology.

The framework integrates technical, social, and organisational aspects of knowledge management (see figure):

  • We propose application-oriented modeling techniques for knowledge formalization to make knowledge available to both humans and machines.
  • We apply transactive knowledge systems to use knowledge appropriately to improve communication between people and teams.
  • New knowledge systems must always be successfully introduced into an organization. Institutional reflexivity is a model for reflecting and stabilizing changing requirements and practices for organizational Knowledge Management.

Diagnosis Instrument

A questionnaire which provides an assessment of the current knowledge management in your organization and suggestions for future improvements. (Only german version available yet)

15 Benefits for Take Away

Research Context

Find out more about the CRC 768 subprojects that resulted in this Use Case:

A Framework Integrating Technical, Social, and Managerial Aspects of Effective Knowledge Management

Expand Abstract Organizations must manage their knowledge resources effectively to perform well in competitive markets. However, with products, services, and processes becoming more complex, knowledge within organizations is highly diverse, dynamic, and distributed among different people and technical systems. Conventional knowledge management (KM) approaches are often not capable of addressing this complexity. To support organizations in handling the diversity, dynamics, and distribution of knowledge – and thus, enable them to better manage and exploit their knowledge resources – we present a socio-technical framework for organizational KM based on theories from information systems engineering, psychology, and sociology. The framework integrates the following technical, social, and managerial aspects of KM: (1) The technological formalization of knowledge (How does technology make existing knowledge available?); (2) the social organization of knowledge (How is knowledge socially distributed and linked across different people?); and (3) managerial practices concerning the exploration and exploitation of knowledge (How do organizational rules and structures support KM?). More specifically, we suggest application-oriented modeling techniques as a way of formalizing knowledge to make it available to both people and machines. Furthermore, we include transactive memory systems (i.e., knowledge about who knows what and communication between people to use that knowledge) in our framework to foster the identification and usage of relevant knowledge distributed among different individuals. Finally, because technological and transactive memory systems operate within an organizational context, the framework includes institutional reflexivity, a model for reflecting on and stabilizing changing requirements and practices in organizational KM. The framework was pre-evaluated by two independent expert groups consisting of managers, engineers, and researchers with backgrounds in innovation management and KM. We further suggest a tool for systematically diagnosing KM practices in organizations via questionnaire based on our theoretical framework. We conclude that technical, social, and managerial aspects must be addressed simultaneously to successfully organize and exploit existing knowledge.


Autor: Josef H., Gammel, Gennadiy, Koltun; Johan, Buchholz; Tobias, Drewlani; Juliane Wissel; Christoph, Hollauer; Katharina G., Kugler; Michael, Zaggl; Birgit, Vogel-Heuser

Supporting PSS Innovation Processes by an Integrating Model Grid

Expand Abstract To managecomplexity and to create an integrated understanding of PSS innovation processes a model grid containing 64 individual models of a collaborative research center was created. This enables the explicit representation of existing knowledge within and for the innovation process and supports data and information sharing through illustration of interactions and interfaces between the models. Whereconventional modelsfocus the internal situation, the model grid integrates models that cover the socio-technical, economic, and patent situation at the environment of the innovation process as well as customerbehavior.Thisprovides an enhanced view on PSS and can create significant benefits if applied to industrial PSS innovation processes.


Autor: Daniel J., Großmann; Daniel, Kasperek; Benjamin, Stahl; Boris, Lohmann; Maik Maurer

A Matrix-based Framework to Support Dynamic Modeling of Sociotechnical Systems

Expand Abstract In order to create viable sociotechnical systems, such as product-service systems, methods to design and analyze such systems are necessary. Dynamic modeling and simulation techniques such as Agent Based Modeling or System Dynamics are suitable techniques that extend the repertoire of existing model-based systems engineering for this purpose. However, due to the complexity involved in efficiently creating, managing and conducting experiments with a large number of such models, an approach is needed to support the modeling process and create transparency. The key result presented in this paper is a meta-model in the form of a MDM, which contains the domains and dependencies necessary to map the process of dynamic modeling of complex sociotechnical systems. The meta-model is the result of an academic case study, where static and dynamic models of a product- service system have been developed.


Autor: C., Hollauer; J., Wilberg; M., Omer

Wissensaustausch und Innovationen in vernetzten Teams - Transaktive Wissenssysteme in Multiteam-Systemen

Expand Abstract Goal: Wissensaustausch, definiert als die Bereitstellung von Information und Know-How, um anderen Personen bei der Lösung von Problemen, der Ideenentwicklung oder deren Implementierung zu helfen, ist bedeutend für Innovationen. Das Konstrukt der transaktiven Wissenssysteme beschreibt, wie Teams verteilte Informationen strukturieren sowie verarbeiten und wie Wissensaustausch zustande kommt. Multiteam-Systeme (MTS), welche gekennzeichnet sind durch mehrere vernetzte Teams mit einem gemeinsamen übergeordneten Ziel, wurden bei der Frage, wie transaktive Wissenssysteme effizient gestaltet sind und wie es zu erfolgreichem Wissensaustausch kommt, meist außer Acht gelassen. Wann und wie erfolgreicher Wissensaustausch in MTS entsteht und welche Komponenten eines transaktiven Wissenssystems von MTS den Wissensaustausch (proximal) und Innovationen (distal) erklären, ist daher Gegenstand dieser Studienarbeit.


Autor: Josef, Gammel

The social dynamics of heterogeneous innovation ecosystems: Effects of openness on community–firm relations

Expand Abstract In this article, we develop a programmatic notion of innovation ecosystems, which emphasizes the analysis of different forms of distributed innovation without reducing the perspective to the role of a focal organization. It highlights relationships between communities and corporate firms as nexus for distributed innovation and elaborates how different facets of openness shape the dynamic of the ecosystem. Thus, our model allows for the analysis and comparison of a broad scope of constellations, their particular coordinating mechanisms as well as related advantages and disadvantages. We apply this framework to two specific cases of distributed innovation, the RepRap 3D printer and the ARA modular smartphone, in order to delineate how differences in the forms of openness affect the prevalent relationships between communities and firms as well as the constituting functions of their particular innovation ecosystem.


Autor: Jan-Peter, Ferdinand; Uli, Meyer

Find out more in this video:

Contact

Gennadiy Koltun, Dipl.-Ing.

Technical University of Munich

Institute of Automation and Information Systems

Gennadiy.Koltun@tum.de

Tel. +49 89 289 16451

www.ais.mw.tum.de

www.sfb768.tum.de

Find out more about the CRC 768 subprojects that resulted in this Use Case:

Find out more in this video:

Contact

Gennadiy Koltun, Dipl.-Ing.

Technical University of Munich

Institute of Automation and Information Systems

Gennadiy.Koltun@tum.de

Tel. +49 89 289 16451

www.ais.mw.tum.de

www.sfb768.tum.de