These days, manufacturing companies are subject to a market environment that is characterized by constantly increasing customer requirements at shorter product life cycles [1]. In order to meet global competition with regard to these challenges, companies need to design and deliver products both providing significant value to their customers and being sustainable [2]. Herewith, the ability of customer-oriented product development at minimal time becomes a key success factor. Additionally, the consideration of sustainability aspects in the product development process is of increasing importance as customers’ demands for sustainable products are rising [3]. Product development particularly allows addressing these aspects upfront the product life cycle [4]. In this context, technical complaint management (TCM) has repeatedly proposed to be a source of knowledge for identifying and addressing critical aspects to product quality and sustainability [5]. TCM comprehends all technical related efforts to solve criticized product failures underlying internal or external complaints. It aims at fast failure identification, immediate root cause clarification and long-term prevention of reoccurrence whilst considering the entire product life cycle. Although research indicates great potential, long-term effects of failure-based learning lag behind [6,7]. In order to address this shortcoming, a conceptual process model for complaint knowledge transfer was developed in former research by LINDER ET AL. [8] The focus of this paper is to consider this model in detail by integrating a framework of knowledge management (KM) solutions and a corresponding selection approach using the analytical network process (ANP). The paper is organized as follows. First, the state of the art of knowledge management in TCM research is briefly reviewed. Afterwards, a literature-based framework of KM solutions is developed, which serves as methodological support for the above mentioned process model. Finally, an ANP model for the evaluation of the identified solutions is derived and parametrized based on the results of a study amongst 15 KM experts.