In current supply chain operations, original equipment manufacturers (OEMs) procure parts from hundreds of globally distributed suppliers, which are often small- and medium-scale enterprises (SMEs). The SMEs also obtain parts from many other dispersed suppliers, some of whom act as sole sources of critical parts, leading to the creation of complex supply chain networks. These characteristics necessitate having a high degree of visibility into the flow of parts through the networks to facilitate decision making for OEMs and SMEs, alike. However, such visibility is typically restricted in real-world operations due to limited information exchange among the buyers and suppliers. Therefore, we need an alternate mechanism to acquire this kind of visibility, particularly for critical prediction problems, such as purchase orders deliveries and sales orders fulfillments, together referred as work orders completion times. In this paper, we present one such surrogate mechanism in the form of supervised learning, where ensembles of decision trees are trained on historical transactional data. Furthermore, since many of the predictors are categorical variables, we apply a dimension reduction method to identify the most influential category levels. Results on real-world supply chain data show effective performance with substantially lower prediction errors than the original completion time estimates. In addition, we develop a web-based visibility tool to facilitate the real-time use of the prediction models. We also conduct a structured usability test to customize the tool interface. The testing results provide multiple helpful suggestions on enhancing the ease-of-use of the tool.