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The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This research examines the profound effects of integrating IoT-enabled predictive maintenance in sustainable transportation fleets. By using real-time sensor data, this implementation aims to enhance fleet dependability and operational efficiency. The fleet, including a variety of vehicles such as electric buses, hybrid cars, electric trucks, CNG-powered vans, and hybrid buses, is constantly monitored using IoT sensors that capture important characteristics like engine temperature, battery voltage, and brake wear percentages. The predictive maintenance algorithms adapt maintenance schedules in response to live sensor data, enabling a proactive strategy that tackles prospective problems before they result in major failures. The examination of the maintenance records reveals prompt actions, showcasing the system’s efficacy in reducing operational interruptions and improving the overall dependability of the fleet. Moreover, the examination of percentage change confirms the system’s flexibility, demonstrating its capacity to anticipate fluctuations in engine temperature, battery voltage, and brake wear. The findings highlight the system’s ability to adapt to various operating situations and its contribution to lowering maintenance expenses while enhancing operational effectiveness. The established approach incorporates ethical issues, such as data security and privacy, to ensure responsible adoption of IoT technology. This study has broader ramifications beyond the particular dataset, providing a detailed plan for incorporating IoTenabled predictive maintenance into contemporary transportation infrastructures. The study’s findings offer valuable insights into the potential of proactive maintenance strategies to transform the transportation industry towards sustainability. This contributes to a future where fleets operate with increased efficiency, reduced environmental impact, and improved reliability.