Modern enterprises of all sizes operate in global manufacturing networks and complex global supply chains. Because sustainability is now a major concern, global manufacturing enterprises must optimize their global supply chain over multiple objectives including sustainability. It is important for such enterprises to analyze their global supply chain across all the three pillars of sustainability (society, economy and environment) when making a distribution network decision. A cradle-to-gate approach is taken, which means this decision can depend on the manufacturing site, all its suppliers, raw material source and transportation right until the customer gate. In this article, a multi-objective optimization model is presented that provides a rigorous method to optimize over all the three pillars of sustainability using a cradle-to-gate approach.

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The application of machine learning techniques in the manufacturing sector provides opportunities for increased production efficiency and product quality. In this paper, we describe how audio and vibration data from a sensor unit can be combined with machine controller data to predict the condition of a milling tool. Emphasis is placed on the generalizability of the method to a range of prediction tasks in a manufacturing setting. Time series, audio, and acceleration signals are collected from a Computer Numeric Control (CNC) milling machine and discretized into blocks. Fourier transformation is employed to create generic power spectrum feature vectors. A Gaussian Process Regression model is then trained to predict the condition of the milling tool from the feature vectors. We highlight that this multi-step procedure could be useful for a range of manufacturing applications where the frequency content of a signal is related to a value of interest.