Since manufacturers are the main drivers in the selection of the materials used in their products, they have a special responsibility for investigating the accompanying sustainability aspects. The recently increased attention they pay to these issues is motivated not only by a sense of social responsibility, but also by pressure from customers and competitors. In particular, the three dimensions of sustainability – based on the triple bottom line of economic, environmental and social criteria – are of growing importance for sustainable supply chain management. Raw materials and their supply chains are often at the focus of attention, since manufacturers may need to diversify them for their products. Appropriate assessment models then become essential. Although raw material assessments and raw material-focused decision support schemes have recently been applied more often in the corporate context, several aspects such as indicator selection, weighting or social assessment are rarely considered. As a result, a comprehensive, sustainability-oriented raw material assessment and decision support scheme in the corporate context is not available in the scientific literature. In the design of such a scheme, four questions arise: First, how should a corporate-oriented raw material assessment model be structured? Second, how can all three sustainability dimensions be taken into account? Third, how can the application of raw material assessments in a corporate-oriented decision-making process increase the sustainability level of a company? And fourth, does a methodically structured raw material assessment model have advantages over existing models? To answer these questions, we have developed a model which takes into account state-of-the-art sustainability assessments as well as recent developments in the field of criticality analysis, life cycle impact assessment (LCIA) and social life cycle assessment (SLCA). With partners from both academia and industry we identify relevant quantitative indicators for structuring the assessment model and for calculating corresponding indicator weightings, using the analytic hierarchy process (AHP). To demonstrate the applicability of the assessment model for decision support and its benefits for companies in identifying potential hotspots in raw material supply chains, we present a case study that includes decision support for selecting capacitor technologies. A sensitivity analysis demonstrates the robustness of the assessments. In short, this article presents a sustainability-oriented raw material assessment and decision support model and proposes how it should be applied in a corporate context.