ANP-based knowledge management solutions framework for the long-term complaint knowledge transfer

Abstract

This paper presents a methodological framework based on the analytical network process (ANP) approach for selecting knowledge management (KM) solutions for transferring complaint knowledge to new product developments. Based on an extensive literature review and prior research projects, competing objectives, diverse criteria as well as various organisation-specific factors have been identified and integrated into the framework. An expert study amongst 15 KM experts was conducted to evaluate KM solutions with respect to the identified objectives and selection criteria. Additionally, the practical applicability was tested in a case study in the German machinery and equipment industry. The framework exceeds existing approaches to technical complaint management (TCM) in enabling a more elaborated design of the long-term knowledge transfer phase within the TCM process. In this regard, the framework provides a systematic approach to assist practitioners in selecting KM solutions for a specific organisational setting. Several universal implications for selecting KM solutions in the context of TCM were derived from the results of the expert study and the case study (e.g. most favourable KM solutions for single criteria). These findings enable an effective and efficient transfer of complaint knowledge to future product developments, and thus facilitate the design of a more sustainable and improved TCM process.

Keywords:

• knowledge management
• learning organisation
• technical complaint management
• decision making
• analytical network process

Acknowledgements

The research presented in this paper has been carried out within the research project ‘Entwicklung eines empirisch fundierten Gestaltungsmodells fuer die effektive und effiziente Analyse, Bearbeitung und Nutzung von Kundenreklamationsinformationen’ (SCHM 1856/31-3) at the Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors gratefully acknowledge the support of the funding organisation German National Science Foundation (Deutsche Forschungsgemeinschaft DFG).