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

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

Not AvailableBug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine (SVM), probability based Naïve Bayes (NB), Decision Tree based J48 (A Java implementation of C4.5), rule based Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Random Forests (RF) learners in predicting the severity level (1 to 5) of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA’s PITS (Projects and Issue Tracking System) datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering (i) the value of accuracy and F-Measure for all severity level and (ii) number of best cases at different threshold level of accuracy and F-Measure.Not Availabl