Pervasive limitations : innovating with ambient intelligence (AmI) technologies and restricted absorptive capacity in Australian SME manufacturers

The last 25 years have seen rapid increases in the number and sophistication of technological and process innovations in large manufacturers, producing dramatic improvements in productivity and efficiency. However, smaller manufacturers’ adoption of such innovations has been uneven. Ambient Intelligence (AmI) technologies are being positioned as the next performance and productivity enhancing purchase for manufacturers. This paper defines and gives examples of AmI technologies in current use, summarises AmI technologies of potential interest to small and medium enterprise (SME) manufacturers, and identifies potential impacts of restricted absorptive capacity in SMEs on the adoption of AmI technologies. Comparing two SME manufacturers, one from Germany and one from Australia illustrates a potential application of generic AmI technology based business solutions to a range of SME manufacturers.

A decision-making model to choose business intelligence platforms for organizations

In recent time, technology applications in different fields, especially Business Intelligence (BI) have been developed rapidly and considered to be one of the most significant uses of information technology with special position reserved. The application of BI systems provides organizations with a sense of superiority in the competitive environment. Despite many advantages, the companies applying such systems may also encounter problems in decision-making process because of the highly diversified interactions within the systems. Hence, the choice of a suitable BI platform is important to take the great advantage of using information technology in all organizational fields. The current research aims at addressing the problems existed in the organizational decision-making process, proposing and implementing a suitable BI platform using Iranian companies as case study. The paper attempts to present a solitary model based on studying different methods in BI platform choice and applying the chosen BI platform for different decisionmaking processes. The results from evaluating the effectiveness of subsequently implementing the model for Iranian Industrial companies are discussed.

Critical success factors for business intelligence systems

The implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of critical success factors (CSFs) for management reference because the BI market has been driven mainly by the IT industry and vendors. This research seeks to bridge the gap that exists between academia and practitioners by investigating the CSFs influencing BI systems success. The study followed a two-stage qualitative approach. Firstly, the authors utilised the Delphi method to conduct three rounds of studies. The study develops a CSFs framework crucial for BI systems implementation. Next, the framework and the associated CSFs are delineated through a series of case studies. The empirical findings substantiate the construct and applicability of the framework. More significantly, the research further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results.

An artificial intelligence examination of the influence of geological conditions and changes in climate on damage to light structures in Victoria

Damage to light structures in the state of Victoria can be caused by movements of expansive soils. The presentation will present the results of an examination of reports of increasing complaints of house damage in Victoria and particularly in the Melbourne area. The examination analyses the influence of geology and change in climate using Neural Network and Genetic Algorithm approaches and assesses their relative importance in contributing to the cause of the damage.

Developing emotional intelligence in high potential middle managers

This paper reports on a mixed quantitative and qualitative longitudinal study, performed in Australia, in which experiential training was used to foster increased emotional intelligence in a group of middle managers previously identified as high achievers. Evaluation of pre and post-training measures of emotional intelligence confirmed a significant difference in scores on an instrument measuring the construct. These results were confirmed by content analysis of journals kept by participants during the management development program. Findings are discussed in terms of their implications for management development as well as for further research.

A brief review of recent research trends on applications of computational and statistical techniques in financial and business intelligence

Artificial neural networks and statistical techniques like decision trees, discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.

Empirical investigation of critical success factors for implementing business intelligence systems in multiple engineering asset management organisations

Engineering asset management organisations (EAMOs) are increasingly motivated to implement business intelligence (BI) systems in response to dispersed information environments and compliance requirements. However, the implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet, so far, there are few defined critical success factors (CSFs) to which management can refer. Drawing on the CSFs framework derived from a previous Delphi study, a multiple-case design was used to examine how these CSFs could be implemented by five EAMOs. The case studies substantiate the construct and applicability of the CSFs framework. These CSFs are: committed management support and sponsorship, a clear vision and well-established business case, business-centric championship and balanced team composition, a business-driven and iterative develop ment approach, user-oriented change management, a business-driven, scalable and flexible technical framework, and sustainable data quality and integrity. More significantly, the study further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results.

Business intelligence systems implementation : testing a critical success factors framework in multiple cases

This paper presents and discusses the critical success factors (CSFs) influencing the implementation of business intelligence (BI) systems. Based on a preliminary critical success factors (CSFs) framework, multiple case studies approach was applied to investigate the CSFs influencing BI systems implementation in seven large engineering enterprises. The empirical findings demonstrate a clear trend towards multidimensional challenges involved in such resourceful and complex undertaking. The CSFs exist in various dimensions composed of organisation, process, and technology perspectives. More significantly, the study reveals that a more fundamental issue concerning the business needs of BI systems may, in the end, impede BI systems success. That is, BI stakeholders are urged to apply a business-orientation approach in tackling implementation challenges and ensuring buy-in from business stakeholders.

A SOA-driven business intelligence architecture

A business intelligence architecture comprises of different unique components to collect, transform, analyze and present the structured and unstructured raw data in simple formats to assist decision makers in making timely decisions. The introduction of service-oriented architecture (SOA) enables reusable services which are accessible over a network on demand. However, there is still a lack of academic literatures on the business intelligence architecture with service-oriented concept. Based upon various references on BI architectures from major vendors, a novel BI architecture that is built on service-oriented concept is presented and described in this paper. The proposed service-oriented architecture enables enterprises to deploy a more agile, flexible, cheaper, reusable, reliable and responsive BI applications in supporting decision making process.

A maturity model of enterprise business intelligence

The implementation of an enterprise-level business intelligence initiative is a large-scale and complex undertaking, involving significant expenditure and multiple stakeholders over a lengthy period. It is therefore imperative to have systematic guidelines for business intelligence stakeholders in referring business intelligence maturity levels. Draw upon the prudent concepts of the Capability Maturity Model, this research proposes a multi-dimensional maturity model with distinct maturity levels for managing enterprise business intelligence initiatives. The maturity model, named Enterprise Business Intelligence Maturiy (EBIM), consists of five core maturity levels and four key dimensions, namely information quality, master data management, warehousing architecture, and analytics. It can be used to assist enterprises in benchmarking their business intelligence maturity level and identifying the critical areas to attain higher level of maturity.

Enhancing business intelligence traceability through an integrated metadata framework

The Business Intelligence (BI) system provides users with multi-dimensional information (so-called BI product) to support their decision-making. However, very often business users still could not fully understand the BI product, nor have a clear picture of the entire information manufacturing chain of the BI product. In response to this situation, this paper presents an integrated metadata framework (“BIP-Map”) to facilitate the traceability and accountability of a BI product following the design science research approach. Specifically, the salient modelling and management techniques from the business process modelling notation (BPMN), the information product map (IP-Map), and the metadata management are adapted to construct a three-layered integrated metadata framework enabling the business users to make timely and informed decisions. A BIP-Map informed prototype system has been developed in collaboration with online job recruitment firms. The authors conducted in-depth interviews with seven key BI stakeholders of the recruitment firms to evaluate the usefulness of the BIP-Map. It is envisaged that the metadata framework allows the technical personnel to understand the business processes that relate to certain information provided in the BI reports. Business users will also be able to gain insights into the logic behind any BI report.

Data quality management in a business intelligence environment : from the lens of metadata

Business Intelligence is becoming more pervasive in many large and medium-sized organisations. Being a long term undertaking Business Intelligence raises many issues that an organisation has to deal with in order to improve its decision making processes. Data quality is one of the main issues exposed by Business Intelligence. Within the organisation data quality can affect attitudes to Business Intelligence itself, especially from the business users group. Comprehensive management of data quality is a crucial part of any Business Intelligence endeavour. It is important to address all types of data quality issues and come up with an all-in-one solution. We believe that extensive metadata infrastructure is the primary technical solution for management of data quality in Business Intelligence. Moreover, metadata has a more broad application for improving the Business Intelligence environment. Upon identifying the sources of data quality issues in Business Intelligence we propose a concept of data quality management by means of metadata framework and discuss the recommended solution.