John William Carbone

Cloud Computing-Based Marketplace for Collaborative Design and Manufacturing

This paper introduces an open-source, interoperable platform for real-time collaboration in complex product lifecycle development across multiple companies. Each segment of the lifecycle, including product conception, design, analysis, prototyping, component sourcing, manufacturing and assembly, logistics and delivery, and services from installation to maintenance, repair and overhaul, can benefit from this collaboration through easy access, development, deployment, and integration of heterogeneous models and data. The platform is built on an elastic cloud-computing environment, which provides efficient scaling of computational performance needs to support the collaboration platform. We believe that this platform will enable organizations of all sizes to enter a new digital age of integrated product design, manufacturing and service systems.

A Hybrid Statistical Method for Accurate Prediction of Supplier Delivery Times of Aircraft Engine Parts

Aircraft engine assembly operations require thousands of parts provided by several geographically distributed suppliers. A majority of the operation steps are sequential, necessitating the availability of all the parts at appropriate times for these steps to be completed successfully. Thus, being able to accurately predict the availabilities of parts based on supplier deliveries is critical to minimizing the delays in meeting the customer demands. However, such accurate prediction is challenging due to the large lead times of these parts, limited knowledge of supplier capacities and capabilities, macroeconomic trends affecting material procurement and transportation times, and unreliable delivery date estimates provided by the suppliers themselves. We address these challenges by developing a statistical method that learns a hybrid stepwise regression — generalized multivariate gamma distribution model from historical transactional data on closed part purchase orders and is able to infer part delivery dates sufficiently before the supplier-promised delivery dates for open purchase orders. The hybrid form of the model makes it robust to data quality and short-term temporal effects as well as biased toward overestimating rather than underestimating the part delivery dates. Test results on real-world purchase orders demonstrate effective performance with low prediction errors and constantly high ratios of true positive to false positive predictions.Copyright