he rising concerns about carbon emissions due to drasticenvironmentalchanges globally hasincreased awareness of customers regarding the carbon footprint of the products they are consuming. Thus,compelledsupply chain managers to reformulate strategies forcontrolling thecarbonemissions. The various activities contributing to carbon emissionsin a supply chain are procurement, transportation, ordering and holding of inventory.Operational decisions like selection of the right supplier ofright lot-sizes can play a vital role in reducing the overall carbon footprint of a supply chain. Thispaper proposes a mixed-integer nonlinearprogram (MINLP) for supplier selectionalong with determining the right lot-sizesin a dynamic setting having multi-periods, multi-productsand multi-supplierswith a view of overall reduction in the supply chaincost as well as associated cost ofcarbon emissions. The model requires a range of real time parameters from both the buyer’sand supplier’sperspectives such as costs, capacities and carbon caps. These parameters have been mappedwith the different dimensions of Big Dataviz. volume, velocity and variety. The model provides an optimal supplier selection and lot-sizing policy along with the carbon emissions. For the purpose of evaluating the carbon emissions, three different carbon regulating policies viz.,carbon cap-and-trade, strict cap on carbon emission and carbon tax on emissions, have been considered and insights are drawn.The validationof the proposed MINLP has been done using a randomly generated dataset havingthe essential parameters of Big Data, i.e. volume, velocity, and variety.
