Process model variants are collections of similar process models evolved over time because of the adjustments that were made to a particular process in a given domain, e.g., order-to-cash or procure-to-pay process in reseller or procurement domain. These adjustments produce some variations between these process models that mainly should be identical but may differ slightly. These variations are due to new procedures, law regulations in different countries, variations due to different decision histories and organizational responsibilities and to different requirements for different branches of an enterprise.Existing approaches for capturing and analysing process variants diverge in two directions: in business process management and in business intelligence/data warehousing. Current approaches for managing process variants using BPMS suffer from three shortcomings that affect their usability in practice.Firstly, these process models result in data redundancy as often model variants are similar or identical for most parts if all these variants are kept separately.Secondly, to model and maintain these processes may result in a time-consuming and error-prone task for business designers. ... mehrThirdly, some optimization techniques might be applied to a specific variant without considering the other ones related to it.Whereas, approaches to analyse these variants using a data warehouse solution suffer from adequately abstracting and consolidating all variants into one generic process model, to provide the possibility to distinguish and compare among different parts of different variants. This shortcoming affects decision making of business analysts for a specific process context.As a consequence, analysing and comparing these multiple variants within a common IT system proved far from trivial.Following a design science research method, this thesis addresses the above shortcomings by proposing a framework to analyse process variants.The framework consists of three original contributions:(i) a novel meta-model of processes as a generic data model to capture and consolidate process variants into a reference process model;(ii) a process warehouse model to perform typical OLAP operations on different variation parts thus providing support to decision-making; (iii) a comparison between existing meta-modeling approaches by the research community based on different criteria from literature review.The framework concepts were formally defined and validated using two different scenarios. Moreover, a prototype is implemented to support the validation of the framework.