The complexity of manual assembly is continuously increasing due to a large variety of products, multi-product assembly or a batch size of one. To stay ahead in competency and competition, and to ensure adaptability and flexibility in today’s dynamic production environment, awareness of knowledge as the 4th factor of production, as well as the effective management of knowledge, are crucial. The present research therefore aimed at further advancing knowledge management in manual assembly by (1) assessing cognitive assistance systems and organisational incentive systems by use of an online survey distributed to German production companies, and by (2) applying the Analytical Hierarchy Process (AHP) as a transparent decision-making tool for knowledge-based improvements in the manual assembly process and workplace design. By employing an exemplary case of two feasible assembly alternatives, the AHP was applied as a method of knowledge measurement in a specific use case revealing priorities for knowledge-based ideas. To properly compute a final priority ranking of workers’ knowledge ideas, an algorithm written in Python programming language in accordance with the problem-solving framework previously published by Thomas L. Saaty (Decision Sciences, 18: 157-177, 1987). The performance of the algorithm shows that the rating process can be standardised and automated to a high level, and that the AHP may thus provide supportive evidence for assembly optimisation. The AHP-derived results can be used as a suitable basis for a bonus-point incentive system, which should contain both material and immaterial incentives. To operationalise this, it is therefore recommended to integrate the AHP rating process into a knowledge management application of hand-held devices, such as tablets, which are widely used in the production environment.