In order to operate complex supply chains, it is necessary to react to various changes. Product change management affects operations along the entire product lifecycle: from early development to manufacturing, sales and marketing operations until discontinuation of products. Various stakeholders need to make decisions when such changes occur. The main contribution is that we introduce a semantic web based knowledge graph in the realm of product changes, which is interpretable to humans and machines and allows a common understanding of the domain. The proposed Supply Chain Change Management knowledge graph is instantiated with real-world data from the semiconductor domain. In addition, we find that the definition of rule sets enhances the expressivity of the knowledge graph. We verify the quality of the knowledge graph in terms of visualization, deduction of implicit knowledge, and detection of inconsistent data through reasoning mechanisms. Standardized interfaces enable integration into related supply chain ontologies. The main implication for practice is that our proposed solution can help automating supply chain change processes that are currently time-consuming and prone to errors.