Smart assistants in manufacturing can guide and aid on decision-making while also provide means to collect additional insights and information available to the users. A general approach for building a smart assistant that provides users with machine learning forecasts and a sequence of decision-making options is presented in this work. The system provides means for knowledge acquisition by gathering data from users. To minimize interactions and friction with users, we envision active learning can be used to get data labels for most data instances expected to be most informative. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing.