1,249 research outputs found
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Real-Time Value Chain Management
Value creation in the digital economy of the 21st century is characterized by the instantaneous processing and coordination of value chain activities across extended enterprises. Value chain management has evolved from automation and integration to the optimization of the entire value chain in real-time. This paper examines the real-time characteristics for each of the activities in the demand and supply value chains. A model is presented for real-time value chain management enabled by technologies in e-business, knowledge management and business intelligence
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Big Data Customer Knowledge Management
Knowledge management (KM) and customer relationship management (CRM) are dominant strategies for value creation for businesses in the new economy. Customer knowledge management (CKM) results in the merging of KM and CRM, where the knowledge management process is applied to customer knowledge, and customer knowledge is applied to customer relationship management operations. With the emergence of big data as the latest phase in the evolution of technology in business, CKM strategies need to be adjusted to meet the new challenges, changing from an internal organizational focus to new external channels such as social media and machine communications. This paper explores the concept of big data customer knowledge management. It presents an architecture that integrates CRM operations and KM processes with big data technologies that include NoSQL databases, Hadoop Distributed File System, MapReduce, and platforms for social media and machine-to-machine communications
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Optimizing Data Warehousing Startegies
Database technologies have evolved over the last two decades into different constructs to support the ever-growing information needs for organizations spanning the spectrum of operational and analytical processing. The paper examines the characteristics of transactional databases, operational data stores, data warehouses and virtual data warehouses. A framework is developed for an optimal data warehousing strategy based on organizational needs classified by the types of business processes defined by the requirements of supporting functional areas and the levels of decision structures. Enterprise architecture is described to provide an integrated and complimentary view of various data warehousing constructs
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The Anatomy of Real-Time CRM
In the digital economy of the 21st century, the focus of production efficiency and product differentiation is shifted to value creation and relationship management. Customer relationship management is a critical business strategy in gaining competitive advantages. The ubiquity of the Internet has changed the way businesses are conducted. Real-time CRM is becoming increasingly significant to enable the agility of businesses to provide quick, accurate and complete responses to customer needs. This paper examines the structural makeup of real-time CRM that consists of e-business enabled CRM (ECRM), knowledge enabled CRM (KCRM) and business intelligence enabled CRM (ICRM). An architecture is developed for real-time CRM utilizing the components of e-business, knowledge-based systems, virtual data warehousing and real-time analytics
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E-Business Enabled ERP II Architecture
The economy of the 21st century that suceeds the service and production economies has significant impact to business strategies. In this new economy, businesses compete as integral parts of global value network fueled by the Internet and Globalization. Critical to the competitive strategy are efficient real-time operations and the exploitation of relationship and knowledge assets across the value network
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Building Data Warehouses Using The Enterprise Modeling Framework
Tlds paper proposes an enterprise modeling framework for the deployment of data warehouses. The framework provides the information roadmap coordinating source data and different data warehouses across the business enterprise. The paper introduces a solution to address data warehousing issues at the enterprise level while avoiding the pitfalls of creating enterprise data warehouses and universal data marts. It further proposes a change of paradigm from point solutions focus to a methodology driven by enterprise requirements to meet the challenges of the new economy. The proposed framework emphasizes the separation of the conceptual construct from the physical and operational constructs of an enterprise. It points out the differences and dependencies of analytic and operational processes and how data warehouses and operational data stores respectively support their information requirements. This paper will demonstrate how the enterprise modeling framework for data warehousing can produce business benefits
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An Architecture for Big Data Analytics
Big Data is the new experience curve in the new economy driven by data with high volume, velocity, variety, and veracity. They come from various sources that include the Internet, mobile devices, social media, geospatial devices, sensors, and other machine-generated data. Unlocking the value of Big Data allows business to better sense and respond to the environment, and is becoming a key to creating competitive advantages in a complex and rapidly changing market. Government is also taking notice of the Big Data phenomenon and has created initiatives to exploit Big Data in many areas such as science and engineering, healthcare and national security. Traditional data processing and analysis of structured data using RDBMS and data warehousing no longer satisfy the challenges of Big Data. Technology trends for Big Data embrace open source software, commodity servers, and massively parallel-distributed processing platforms. Analytics is at the core of exploiting values from Big Data to create consumable insights for business and government. This paper presents architecture for Big Data Analytics and explores Big Data technologies that include NoSQL databases, Hadoop Distributed File System and MapReduce
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Integrating Knowledge Management and Relationship Management in an Enterprise Environment
Knowledge management and relationship management are essential ingredients for value creation to gain competitive advantages in the knowledge-based economy of the 21st century. The merging of the two disciplines has received increasing attention both in academia and in business. Past research focuses on the integration of knowledge management and customer relationship management at the business process level. This paper extends the concepts to enterprise relationship management to include customer relationship management, supplier relationship management and partner relationship management. The merging of knowledge management (KM) and enterprise relationship management (ERM) yields two perspectives: the ERM-oriented KM (EKM) and the KM-oriented ERM (KERM)
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A Predictive Analytic Model for Value Chain Management
Value chain management has gone through various stages of automation, integration and optimization in the past decades. While an optimization model for value chain deals with business scenarios under known circumstances, a predictive value chain model deals with probable circumstances in the future. Predictive analytics is succeeding optimization in the evolution of technologies supporting value chain management. This paper proposes a forward looking value creation model that combines the important concepts of value chain management and predictive analytics. An enterprise model for value chain predictive analytics that facilitates the convergence of information, operations and analytics is presented
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Customer Knowledge Management In the Cloud Ecosytem
The evolution of the economy has gone through the agricultural era, followed by the industrial era focusing on the production of goods and the postindustrial era accentuated by information and services. The new economy of the 21stcentury is characterized by knowledge and relationships. Galbreath (2002) described the transition to a new economic order driven by knowledge and based on the value of relationships. Customer knowledge management (CKM) synthesizes the customer knowledge and relationships assets in the extended enterprise to create advantages for companies in a very competitive market. Technologies have changed alongside the economy. Big Data and Cloud Computing are the latest stages of the evolution of technologies in business. This paper builds upon the work done in the areas of Customer Relationship Management, Knowledge Management, Big Data and Cloud Computing. It examines the CKM process enabled by a dynamic actionable demand driven service platform leveraging Big Data, knowledge analytics, and cloud computing
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