Performing business processes are a critical asset for manufacturing companies operating on highly competitive markets. Conventional approaches to business process improvement, however, are vulnerable to subjectivity and high manual efforts in their execution. These challenges can be overcome with recent databased approaches that semi-automate process analysis and design. Those approaches formalize methodical knowledge on weakness detection, measure derivation and performance evaluation for business processes into a performance-related decision support. By enabling the databased automation of these tasks this formalization helps to reduce efforts and subjectivity in process analysis and design. However, practice lacks a procedure for applying this decision support in operative business process improvement. Moreover, this decision support only formalises methodological knowledge. Operative business process improvement in practice additionally requires the consideration of experts' contextual knowledge about the company and the business process itself. This paper presents a hybrid approach for the analysis and design of business processes using a databased decision support. First, existing phase models for business process improvement are consolidated into a reference model. Second, an expert-based assessment is conducted on how decision support extends, modifies or eliminates the conventional tasks of process analysis and design. In the third step, a hybrid phase model for process analysis and design is developed that integrates the formalised methodological knowledge of the decision support and contextual knowledge of experts.