Demand forecasting is a crucial part of any company or supply chain. It aims at predicting and estimating the future demand of products to help in better decision-making. This paper is a literature review on different demand forecasting methodologies which are used in different industries. The industries which are mainly focused in this literature review are restaurants, retail stores, drug stores, supermarkets and supply chains. It is observed that different organizations employ different forecasting techniques based on their requirement from these methodologies: traditional statistical models, machine learning, deep learning models and hybrid models. Latest research in demand forecasting using these models is briefly described and their advantages and limitations are discussed. Furthermore, models are classified on the basis of characteristics of data and forecasting time periods. It is observed that these individual models do not always work efficiently with all kinds of data and therefore an integrated approach should be employed where all these models are implemented, evaluated and then the model giving the best accuracy is considered for that particular industry dataset. © 2021 IEEE.