In today’s highly competitive business environment, the rapidly changing customer demands and with the advent of enterprise wide information systems, the managers are bound to think beyond the conventional business processes and devise new ways to squeeze out costs and improve the performance without compromising on the quality at the same time. Supplier evaluation and selection is one such area which determines the success of any manufacturing firm. Supplier selection is the problem wherein the company decides which vendor to select to have that strategic and operational advantage of meeting the customers’ varying demands and fight the fierce competition. This paper presents a simple model based on Analytic Hierarchy Process (AHP) to help decision makers in supplier evaluation and selection, taking into account the firm’s requirements. The article is intended to help new scholars and researchers understand the AHP model and see different facets in first sight.

In the increasing competition it has become important in business world to understand the different aspects of production and purchasing to understand the need for desired material in the organization. The managers have an important responsibility of selecting a good supplier by evaluating them on different parameters which is directly or indirectly associated with their overall performance. For decision making based on multiple criteria evaluation many methods of Multi-Criteria Decision Making (MCDM) is used by firms. Data Envelopment Analysis (DEA) is prominently used by firms nowadays. In this paper, analysis of DEA is done by measuring supplier performance of two firms: multi-national telecommunication corporation and a manufacturing firm. The firm uses the methodology according to their requirement and criteria for evaluating their suppliers and find best among them.

Main reason for the attack being introduced to the system is because of popularity of the internet. Information security has now become a vital subject. Hence, there is an immediate need to recognize and detect the attacks. Intrusion Detection is defined as a method of diagnosing the attack and the sign of malicious activity in a computer network by evaluating the system continuously. The software that performs such task can be defined as Intrusion Detection Systems (IDS). System developed with the individual algorithms like classification, neural networks, clustering etc. gives good detection rate and less false alarm rate. Recent studies show that the cascading of multiple algorithm yields much better performance than the system developed with the single algorithm. Intrusion detection systems that uses single algorithm, the accuracy and detection rate were not up to mark. Rise in the false alarm rate was also encountered. Cascading of algorithm is performed to solve this problem. This paper represents two hybrid algorithms for developing the intrusion detection system. C4.5 decision tree and Support Vector Machine (SVM) are combined to maximize the accuracy, which is the advantage of C4.5 and diminish the wrong alarm rate which is the advantage of SVM. Results show the increase in the accuracy and detection rate and less false alarm rate.