Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Business intelligence as the support of decision-making processes in e-commerce systems environment

The present state of world economy urges managers to look for new methods, which can help to start the economic growth. To achieve this goal, managers use standard as well as new procedures. The fundamental prerequisite of the efficient decision-making processes are actual and right information. Managers need to monitor past information and current actual information to generate trends of future development based on it. Managers always should define strictly what do they want to know, how do they want to see it and for what purpose do they want to use it. Only in this case they can get right information applicable to efficient decision-making. Generally, managers´ decisions should lead to make the customers´ decision-making process easier. More frequently than ever, companies use e-commerce systems for the support of their business activities. In connection with the present state and future development, cross-border online shopping growth can be expected. To support this, companies will need much better systems providing the managers adequate and sufficient information. This type of information, which is usually multidimensional, can be provided by the Business Intelligence (BI) technologies. Besides special BI systems, some of BI technologies are obtained in quite a few of ERP (Enterprise Resource Planning) systems. One of the crucial questions is whether should companies and firms buy or develop special BI software, or whether they can use BI tools contained in some ERP systems. In respect of this, there is a question if the modern ERP systems can provide the managers sufficient possibilities relating to ad-hoc reporting, static and dynamic reports and OLAP analyses. A one of the main goals of this article is to show and verify Business Intelligence tools of Microsoft Dynamics NAV for the support of decision-making in terms of the cross-border online purchasing. Pursuant to above-mentioned, in this article authors deal with problems relating to managers´ decision-making, customers´ decision-making and a support of its using the BI tools contained in ERP system Microsoft Dynamics NAV. A great deal of this article is aimed at area of multidimensional data which are the source data of e-commerce systems.Business Intelligence, decision-making, e-commerce system, cross-border online purchasing, multi-dimensional data, reporting, data visualization

Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause

Competitive Positioning in International Logistics: Identifying a System of Attributes Through Neural Networks and Decision Trees

Firms involved in international logistics must develop a system of service attributes that give them a way to be profitable and to satisfy customers’ needs at the same time. How customers trade-off these various attributes in forming satisfaction with competing international logistics providers has not been explored well in the literature. This study explores the ocean freight shipping sector to identify the system of attributes that maximizes customers’ satisfaction. Data were collected from shipping managers in Singapore using personal interviews to identify the chief concerns in choosing and evaluating ocean freight services. The data were then examined using neural networks and decision trees, among other approaches to identify the system of attributes that is connected with customer satisfaction. The results illustrate the power of these methods in understanding how industrial customers with global operations process attributes to derive satisfaction. Implications are discussed