Machine learning has achieved remarkable performance in many domains, now it promising to solve manufacturing problems — a new ongoing trend of using machine learning in industrial applications. Dealing with the material order demand in manufacturing as time-series sequences, making unsupervised time-series clustering possible to apply. This study aims to evaluate different time-series clustering approaches, algorithms, and distance measures in material flow data. Three different approaches are evaluated; statistical clustering approaches; raw based and shape-based approaches and at last feature-based approach. The objectives are to categorize the materials in the supermarket (intermediate storage area to store materials before assembling the products) into three different flows according to their time-series properties. The experimental shows that feature-based approach is performed best for the data. A features filter is applied to keep the relevant features, that catch the unique characteristics from the data the predicted output. As a conclusion data type, structure, the goal of the clustering task and the application domains are reasons that have to consider when choosing the suitable clustering approach.