Development of demand forecasting models for sustainable supply chain management

Demand management plays a core role in supply chain management, but demand forecasting is insufficiently studied through the perspective of algorithm development and data analysis in the existing research. In practice, a small error in demand forecasting will make the information in the upstream and downstream of the supply chain seriously mismatched, resulting in a huge loss in finance and resources, which is the well-known bullwhip effect in inventory management. Besides, data collected for such analyses can be big, thin or incomplete (such as censored or missing). Mining valuable information from different data types has naturally become a focal point of research that needs further exploration. Existing algorithms for demand forecasting include classical statistical methods such as time series analysis including the exponential smoothing methods and the Box-Jenkins methods, and machine learning methods such as neural networks and decision trees. These methods are computationally complicated when modelling on dynamic big data. Additionally, there is little research exploring methods to forecast demands based on thin and incomplete data. This research therefore aims to develop data-driven algorithms to address the demand forecasting issue on big data and incomplete data, respectively, so as to eliminate the bullwhip effect and then facilitate more effective inventory control. In the development of demand forecasting models, both classical statistical methods and machine learning algorithms will be employed. The classical methods include the Poisson process, recurrent marked temporal point process and dynamic harmonic regression; and machine learning methods include recurrent neural networks such as the long short-term memory network. Further, online customer reviews, product attributes and other external impacting factors will all be considered in the development of forecasting models to investigate the impacts of possible covariates. Meanwhile, incomplete samples with missing or the censored data will be analyzed, which is closely related to my research during the master study. Finally, the proposed forecasting methods will be extended to the cases of multi-echelon supply chains and a longer forecast horizon.