It has been recognized by manufacturing companies that working collaboratively is the way to advance their competiveness. Order fulfillment estimation addresses the issue of uncertainty from vendors. It is significant for collaborative manufacturing, which enhances companies’ responsiveness to market dynamics. In a data-rich scenario, order fulfillment estimation can be performed based on information extracted from data acquisition devices, such as smart sensors. The analysis result should serve the decisions-making of the production planning, and an indicator should be passed along the production chain even to its end customer for collaborative purpose. In the meanwhile, the manufacturer’s sensitive or confidential information is excluded to avoid risks. This article studies a method to effectively evaluate the order fulfillment process in an Industrial Internet of Things (IIoT) facilitated make-to-order production system. An order fulfilment progress (OFP) indicator is proposed to dynamically represent the fulfillment progress, and its estimation mathematical models are proposed. To improve the practicability of the OFP indicator in production, the influence of abnormal event scenarios are discussed to modify the OFP. A case study presented in this research demonstrates the proposed indicator with consideration of job in process (JIP) is promising comparing to conventional indicators that are represented by the proportion of finished over total products.