David Lechevalier

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Data analytics is proving to be very useful for achieving productivity gains in manufacturing. Predictive analytics (using advanced machine learning) is particularly valuable in manufacturing, as it leads to production improvement with respect to the cost, quantity, quality and sustainability of manufactured products by anticipating changes to the(More)
Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ).(More)
A virtual factory should represent most of the features and operations of the corresponding real factory. Some of the key features of the virtual factory include the ability to assess performance at multiple resolutions and generate analytics data similar to that possible in a real factory. One should be able to look at the overall factory performance and(More)
Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied(More)
Developing manufacturing simulation models usually requires experts with knowledge of multiple areas including manufacturing, modeling, and simulation software. The expertise requirements increase for virtual factory models that include representations of manufacturing at multiple resolution levels. This paper reports on an initial effort to automatically(More)
Sustainable manufacturing aims to increase efficiency and offset negative impacts throughout the life cycle of products. A myriad of standards pertaining to various aspects of a product's life cycle exist; however, it is often difficult for non-experts to navigate and comprehend these standards. The objective of this paper is to describe a classification(More)
Evaluation of key performance indicators (KPIs) such as energy consumption is essential for decisionmaking during the design and operation of smart manufacturing systems. The measurements of KPIs are strongly affected by several uncertainty sources such as input material uncertainty, the inherent variability in the manufacturing process, model uncertainty(More)