The United Nations (UN) developed sustainable development goals (SDGs) in 2015 to end poverty as a global agenda for the future to protect the planet, create peace and prosperity for its population. The UN emphasises the development should be balancing environmental, economic, and social sustainability. Also, nowadays governments, customers, and stakeholders’ pressure to remark environmental and social footprints have been increased. Decarbonising and sustainability of the supply chain is one of such novel ideas involving all the business value-adding operations. This includes purchasing, upstream, and downstream supply chains, distribution and collaboration with suppliers and patrons in a way that has the least negative environmental and social effects. To minimise energy consumption and carbon emissions in the supply chain operations we need to integrate carbon efficiency in suppliers, transportation, plants, distribution centers/warehouses all the way to the market. The main objective of this study is to investigate measuring eco-efficiency of suppliers in the supply chain with data envelopment analysis (DEA). It has the potential to minimise carbon footprints in the supply chain and to address the UN sustainability goals relating to creating a sustainable supply chain in measuring technical (operational), environmental and eco-efficiency of suppliers. In this paper, we model the necessity of simultaneous application of worst and best practice DEA in measuring eco-efficiency of suppliers to minimise carbon footprint in the supply chain. This model would help organisations to balance environmental, economic, and social sustainability in the supply chain in response to the UN sustainable development goals. It is found that this proposed model can provide a more reliable evaluation and selection of right suppliers considering their environmental and other traditional criteria. We also develop an integrated approach through DEA models for measuring technical (operational), environmental and eco-efficiency of suppliers. The proposed models are applied to evaluate the eco-efficiency of a manufacturing company in an automotive industry.