Unilever Canada Collective Intelligence Platform
We propose to develop a demand forecasting pipeline that collects data and applies machine learning models to make accurate predictions of future demands under unstable scenarios. In particular, this project will focus on efficient forecast of Unilever’s base SKUs and involve model development based on time-series, machine learning and possibly combinations of the two, data engineering to identify critical features hinting future trends, and deployment of the models to the existing business process. Methodologies that will be used to support Collective Intelligence (CI) decision-making platform include time series methods like vector autoregression and vector error correcting model; machine learning methods like random forest and deep learning etc. The Machine learning-based demand forecasting for supply chain optimization method should be able to deliver on each component and seamlessly integrate applications to the main platform that runs in a Microsoft Azure environment.