A design for business intelligence service in demand driven supply chain management

This paper discusses the problems inherent within traditional supply chain management's forecast and inventory management processes arising when tackling demand driven supply chain. A demand driven supply chain management architecture developed by Orchestr8 Ltd., U.K. is described to demonstrate its advantages over traditional supply chain management. Within this architecture, a metrics reporting system is designed by adopting business intelligence technology that supports users for decision making and planning supply activities over supply chain health.

Enterprise intelligence: searching for a single version of the truth

Multiple versions of information and associated problems are well documented in both academic research and industry best practices. Many solutions have proposed a single version of the truth, with Business intelligence being adopted by many organizations. Business Intelligence (BI), however, is largely based on the collection of data, processing and presentation of information to meet different stakeholders’ requirement. This paper reviews the promise of Enterprise Intelligence, which promises to support decision-making based on a defined strategic understanding of the organizations goals and a unified version of the truth.

Social networks: evolving graphs with memory dependent edges

The plethora, and mass take up, of digital communication tech- nologies has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the ex- istence or otherwise of certain infinite products and series involving age dependent model parameters. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

Towards the design of a unified semantics model for OLTP and OLAP systems

The concepts of on-line transactional processing (OLTP) and on-line analytical processing (OLAP) are often confused with the technologies or models that are used to design transactional and analytics based information systems. This in some way has contributed to existence of gaps between the semantics in information captured during transactional processing and information stored for analytical use. In this paper, we propose the use of a unified semantics design model, as a solution to help bridge the semantic gaps between data captured by OLTP systems and the information provided by OLAP systems. The central focus of this design approach is on enabling business intelligence using not just data, but data with context.

Did farming arise from a misapplication of social intelligence?

The origins of farming is the defining event of human history-the one turning point that has resulted in modern humans having a quite different type of lifestyle and cognition to all other animals and past types of humans. With the economic basis provided by farming, human individuals and societies have developed types of material culture that greatly augment powers of memory and computation, extending the human mental capacity far beyond that which the brain alone can provide. Archaeologists have long debated and discussed why people began living in settled communities and became dependent on cultivated plants and animals, which soon evolved into domesticated forms. One of the most intriguing explanations was proposed more than 20 years ago not by an archaeologist but by a psychologist: Nicholas Humphrey suggested that farming arose from the 'misapplication of social intelligence'. I explore this idea in relation to recent discoveries and archaeological interpretations in the Near East, arguing that social intelligence has indeed played a key role in the origin of farming and hence the emergence of the modern world.

The social relations of business representation and devolved governance in the United Kingdom

The reform of regional governance in the United Kingdom has been, in part, premised on the notion that regions provide new territories of action in which cooperative networks between business communities and state-agencies can be established. Promoting business interests is seen as one mechanism for enhancing the economic competitiveness and performance of 'laggard' regions. Yet, within this context of change, business agendas and capacities are often assumed to exist 'out there, as a resource waiting to be tapped by state institutions. There is little recognition that business organisations' involvement in networks of governance owes much to historical patterns and practices of business representation, to the types of activities that exist within the business sector, and to interpretations of their own role and position within wider policymaking and implementation networks. This paper, drawing on a study of business agendas in post-devolution Scotland, demonstrates that in practice business agendas are highly complex. Their formation in any particular place depends on the actions of reflexive agents, whose perspectives and capacities are shaped by the social, economic, and political contexts within which they are operating. As such, any understanding of business agendas needs to identify the social relations of business as a whole, rather than assuming away such complexities.

Untangling rhetoric and reality in the CSR debate – the role of corporate social responsibility in effective business strategy

While there is a strong moral case for corporate social responsibility (CSR), the business case for CSR is certainly not irrefutable. A better understanding of how to integrate CSR into business strategy is needed but with ever increasing momentum towards sustainability as a business driver, it is often difficult to untangle the rhetoric from reality in the CSR debate. Through an analysis of eight case studies of leading firms from throughout the construction supply chain who claim to engage in CSR, we explore how consulting and contracting firms in the construction and engineering industries integrate CSR into their business strategy. Findings point to an inherent caution of moving beyond compliance and to a risk-averse culture which adopts very narrow definitions of success. We conclude that until this culture changes or the industry is forced by clients or regulation to change, the idea of CSR will continue to mean achieving economic measures of success, with ecological goals a second regulated priority and social goals a distant third.