EIS for Consumers Classification and Support Decision Making in a Power Utility Database

The increasing of the storage system capacity and the reduction of the access time have allowed the development of new technologies which have afforded solutions for the automatic treatment of great databases. In this chapter a methodology to create Enterprise Information Systems which are capable of using all information available about customers is proposed. As example of utilization of this methodology, an Enterprise Information System for classification of customer problems is proposed. This EIS implements several technologies. Data Warehousing and Data Mining are two technologies which can analyze automatically corporative databases. Integration of these two technologies is proposed by the present work together with a rule based expert system to classify the utility consumption through the information stored in corporative databases.

Enhancing the Electronic Customer Relationship Management through Data Mining

According to the Moore’s law, the number of transistors per microprocessor will double in every two years. In no doubt, this exponential increase in the processing speeds would be flanked by the increasing amount of data that corporates contend on a daily basis. Hence all corporates are literally drowning in data. But definitely there exists a hiatus between the data storage and the information retrieval. One can ask an enigmatic question, how effectively a stored data can be utilised for the decision making in the long-term perspective. The answer is not yet arrived out. Hence the “Organizations are data rich, but information poor!”. If capturing and storing the relevant data is a hectic task, then analyzing and translating this data into the actionable information is the other corner stone in any information systems of a concern. This gap can be bridged or overruled by the concept of business intelligence. Business Intelligence (BI) can be simply defined in terms of data –driven approach rather than information driven which includes methods as decision support systems, online analytical processing (OLAP), statistical analysis, query and reporting, forecasting which can be primarily done by data mining. BI along with customer relationship management (CRM) software forms the second tier of a firm’s IT infrastructure. This chapter holds a bird’s eye view of the usage of datawarehousing approaches for a systematic business intelligence approach and its varied applications in view of electronic customer relationship management.

Stochastic Modeling of Supply Chain Management Systems

Logistics is that part of the supply chain process that plans, implements, and controls the efficient, effective flow and storage of goods, services, and related information from the point-of-origin to the point-of-consumption in order to meet customers’ requirements. Supply Chain Management (SCM) is the practice of coordinating the flow of goods, services, information and finances as they move from raw materials to parts supplier to manufacturer to wholesaler to retailer to consumer. This chapter introduces the concept of Supply Chain Management System (SCMS). Two stochastic modeling problems are discussed in this chapter. Poisson demand process with (s,S) installation policy at retailer nodes are assumed to simplify the study . The system performance measures are computed with reference to specific cost structure. The total average annual variable cost is taken as optimization criterion. Numerical examples are provided to illustrate the problem.

Mathematical Modeling of Supply Chain Management Systems

Supply Chain Management (SCM) is the practice of coordinating the flow of goods, services, information and finances as they move from raw materials to parts supplier to manufacturer to wholesaler to retailer to consumer. Different supply chains have been designed for a variety of firms and this chapter discusses some issues in this regard. This chapter attempts to find why we require different supply chain for different companies. In this chapter we discuss the role of stochastic models in supply chain management system, and also discuss other mathematical models for SCM.

Two-Way Substitutable Inventory System with N–Policy

This article presents a two commodity stochastic inventory system under continuous review. The maximum storage capacity for the i-th item is fixed as Si (i = 1, 2). It is assumed that demand for the i-the commodity is of unit size and demand time points form Poisson distribution with parameter i = 1, 2. The reorder level is fixed as si for the i-th commodity (i = 1, 2) and the ordering policy is to place order for items for the i-th commodity (i = 1, 2) when both the inventory levels are less than or equal to their respective reorder levels. The lead time is assumed to be exponential. The two commodities are assumed to be substitutable. That is, if the inventory level of one commodity reaches zero, then any demand for this commodity will be satisfied by the item of the other commodity. If no substitute is available, then this demand is backlogged up to a certain level Ni, (i = 1, 2) for the i-th commodity. Whenever the inventory level reaches Ni, (i = 1, 2), an order for Ni items is replenished instantaneously. For this model, the limiting probability distribution for the joint inventory levels is computed. Various operational characteristics and expression for long run total expected cost rate are derived.

Information Technology Enabled Vendor Managed Inventory in Modelling Supply Chain Issues

Supply chain is a network of firms interacting in a linear fashion to produce, sell and deliver a product or service to a predetermined market segment. It links all the chain partners within and across organization to work competitively by forming the partnerships together with the integration of business processes, technical and organizational aspects. The successful implementation of supply chain management depends on many soft issues (strategic/behavioural). The soft issues of supply chain models can be dealt through proper information sharing, communication and coordination between the stages of supply chain. Vendor managed inventory is a proven concept for successful collaborative and cooperative agreements in supply chain. This chapter reviews some of the soft issues in two-echelon supply chain models and proposes a classification schema. This chapter surveys the theoretical background and application of vendor managed inventory systems based on environment, operational issues and solution approaches. Hence it is concluded that the framework presented in this chapter would aid supply chain managers and researchers to further look into the soft issues while modeling supply chain with information technology enabled vendor managed inventory systems.

Achieving Supply Chain Management (SCM)-Customer Relationship Management (CRM) Synergy Through Information and Communication Technology (ICT) Infrastructure in Knowledge Economy

Information and communication technology infrastructure has changed modern business practice. The ever-changing information and communication technology infrastructure of organizations’ is opening new vista, which has not only bundles of opportunities to encash but also tremendous obstacles as survival threats. The concern about organizational competitiveness and development is closely linked to notions of the information sensitive society and global knowledge based economies. The business organizations under global knowledge economy can emerge and grow rapidly by formulating and adopting the innovative business practices. Information’s impact is easily seen—it substitutes for inventory, speeds product design and delivery, drives process reengineering, and acts as a coordinating mechanism, helping different members of the supply chain work together effectively. While the potential of information sharing is widely promoted, relatively few companies have fully harnessed its capability to enhance competitive performance. The chapter tries to provide insight into how information and communication technology can be leveraged for supply chain value creation and make it possible to achieve synergy with customer relationship management.

A Fuzzy AHP Model for 3PL Selection in Lead Logistics Provider Scenarios

Selection of service providers in the global supply chains of today has been recognized as having a very important effect on the competitiveness of the entire supply chain. It results in achieving high quality end results (products and/or services), at reasonable cost coupled with high customer satisfaction. This article discusses the use of Fuzzy Analytic Hierarchy Process (FAHP) to effectively manage the qualitative and quantitative decision factors which are involved in the selection of providers of 3PL services under Lead Logistics Provider (LLP) environments of today. Lead logistics providers (LLP) are increasingly being banked upon to integrate the best of 3PL service providers and allow for synchronized and optimized operations. In the asset free environments of today, many a times, the LLP uses the services of the 3PL and hence the issue of reliably choosing them assumes increasingly greater significance. The fuzzy-AHP has been adequately demonstrated in literature to be an effective tool which can be used to factor-in the fuzziness of data. Triangular Fuzzy Numbers (TFN) have been deployed to make over the linguistic comparisons of criteria, sub-criteria and the alternatives. The FAHP based model formulated in this chapter is applied to a case study in the Indian context using data from three leading LSPs with significant operating leverages in the province of Uttrakhand (India). The proposed model can provide the guidelines and directions for the decision makers to effectively select their global service providers in the present day competitive logistics markets.

Benefits of Information Technology Implementations for Supply Chain Management

The usage of Information Technology (IT) in organizations across the supply chain has become a determinant of competitive advantage for many corporations. This chapter focuses on the usage of IT tools for Supply Chain Management (SCM). It also highlights the contribution of IT in helping restructure the entire distribution set-up to achieve higher service levels, lower inventory, and lower supply chain costs. An overview and tangible benefits of the existing IT tools, which are widely deployed, is provided with focus on existing configuration considerations, available applications, and deployments in India. The role of existing communication technologies in making IT an enabler of SCM, is highlighted by addressing a range of different point and enterprise solutions in a variety of supply chain settings. Critical IT demonstrations and implementations in SCM are discussed. Fundamental changes have occurred in today’s global economy. These changes alter the relationship that we have with our customers, our suppliers, our business partners, and our colleagues. Reflection on the evolving and emerging IT trends like software agents, RFID, web services, virtual supply chains, electronic commerce, and decision support systems, further highlights the importance of IT in the context of increasingly global competition. The rapid adoption of the Internet for communication with all stakeholders, seems to reflect the potential of the new-age communication media. It has also been observed that several progressive Indian companies are extensively using emerging tools like virtual supply chains, web services, RFID, and electronic commerce to shore up their supply chain operations. However, adoption of tools like software agents and decision support systems for supply chain integration by Indian companies, is limited.

Achieving Alignment in Production and Logistics Operations in Three Echelon Supply Chain Network Through New Heuristic Optimizer

During the past decade, great studies have been made in the development of standardized tools for supply chain modeling and network optimization Network optimization is the most basic type of modeling that can be performed with tools which helps to identity optimum paths or flow of goods in supply chain network. In this case, the network is defined by the flow of finished goods from origin to destination. Network modeling becomes more complex as the dimensions and scope of the supply chain expand Uncertainties in the supply chain usually increase the variance of profit or cost to the company, increasing the likely hood of decreased profit i.e. increase in total supply chain cost. Demand uncertainty and constraints posed by the every echelon are important factors to be considered in the supply chain design operations. This chapter specifically deals with the modeling and optimization of a three echelon supply chain network architecture using new Particle Swarm Optimization algorithm.