A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Spotting The Elusive Prospect Customer: Exploratory Study Of A Web-Powered Customer Relationship Management Framework

Research on how Web-Mining (WM) optimizes marketing, is sparse. Especially absent, is research on WM usefulness for Customer Relationship Management (CRM). The purpose of this research, is to propose a Web Mining-enabled knowledge acquisition framework for analytical CRM. An exploratory study consisting of eleven in-depth interviews with marketing scholars and practitioners revealed that, WM methods and techniques - currently available to practitioners - are well-suited for identifying the profile of web prospects according to their browsing behaviour and to classify them into homogeneous groups. Besides, the nascent technologies regarding opinion mining, sentiment analysis or natural language parsing, and which underlie WM, seem sufficient to acquire knowledge pertaining to attitudinal and other more psychometrically-based characteristics about web prospects. Such tools enable to better understand the so-often termed elusive prospects, by crafting fine-grained online marketing strategies to acquire those would-be customers. The authors discuss the managerial implications that derive from these findings

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

The use of web analytics on a small data set in an online media company : shifter´s case study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe primary struggle in data analysis is the lack of talent in performing relevant and fit-to-business analyzes that retrieve knowledge and provides concise and clear action plans to today’s startups and small enterprises that exist online. Tracking, knowing and understanding the navigational patterns of user behavior for a 3 month period collection and using an Excel spreadsheet tool obtained a context for each piece of content produced and published by Shifter, an online media company. Investigations made after acquiring Shifter’s data resulted in recommendations for rethink and redesign the editorial content of the business to answer different community’s needs