Supply chain integration:challenges and solutions

Since its introduction by management consultants in the early 1980s, supply chain management (SCM) has been primarily concerned with the integration of processes and activities both within and between organisations. The concept of supply chain integration (SCI) is based on documented evidence that suggests that much of the waste throughout businesses is a consequence of fragmented supply chain configurations. However, there is also evidence to suggest that the achievement of higher levels of intra- and inter-firm integration presents an array of managerial challenges. The need for innovation in all aspects of SCM is widely recognised. Given the pivotal role of the integration paradigm within SCM, any meaningful innovation in this area must focus heavily on this issue. This chapter outlines some of the challenges by exploring the evolving SCM business context. It goes on to relate SCM theory to the widely cited Porter value chain concept. The core of the chapter provides a detailed description of SCI based on a wide variety of literature. It does so with particular reference to the challenges inherent in implementing an integrated business paradigm with a view to identifying a range of possible innovative solutions. The adoption of more integrated supply chain structures raises questions regarding the nature of both internal and external customer/supplier relationships. The effective management of such relationships is, therefore, given particular focus

Audio Classification and Retrieval Using Wavelets and Gaussian Mixture Models

This paper presents an audio classification and retrieval system using wavelets for extracting low-level acoustic features. The author performed multiple-level decomposition using discrete wavelet transform to extract acoustic features from audio recordings at different scales and times. The extracted features are then translated into a compact vector representation. Gaussian mixture models with expectation maximization algorithm are used to build models for audio classes and individual audio examples. The system is evaluated using three audio classification tasks: speech/music, male/female speech, and music genre. They also show how wavelets and Gaussian mixture models are used for class-based audio retrieval in two approaches: indexing using only wavelets versus indexing by Gaussian components. By evaluating the system through 10-fold cross-validation, the author shows the promising capability of wavelets and Gaussian mixture models for audio classification and retrieval. They also compare how parameters including frame size, wavelet level, Gaussian components, and sampling size affect performance in Gaussian models