Lean Six Sigma (LSS) has rapidly established itself as the key business process improvement strategy of choice for many companies. The LSS approach provides significant benefits to companies through its dual focus on reducing waste and increasing value whilst resolving Critical to Quality (CTQ) issues that affect consistency and repeatability in a product and process. The implementation of LSS is finding wider application in many different environments. Through a case study approach, this paper describes the novel implementation of an integrated LSS framework and outlines how it was used to identify the factors that affect supply chain performance in an aerospace Maintenance Repair and Overhaul (MRO) facility. The study outlines the application and measures the effectiveness of the integrated LSS framework through its ability to achieve new and enhanced performance through simultaneously reducing late material calls and reducing and stabilizing Order To Receipt (OTR) times.

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This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.

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