Knowledge management for ERP success

The business value of Enterprise Resource Planning (ERP) systems and in general large software implementations has been extensively debated in both popular press and academic literature for over three decades. Despite the positive motives for adoption, various organizations have reported negative impacts from these large investments. This ‘disconnect’ between large IS investments and firms’ organizational performance may be attributable to the economic transition from an era of competitive advantage based on information to one that is based on Knowledge. This paper discusses the initial findings of a two-phased study that focuses on empirically assessing the impact of knowledge management on the success of Enterprise Resource Planning systems. The research study uses information gathered from twenty-seven public sector organizations in Queensland, Australia. Validation of the a priori model constructs through factor analysis identified two dimensions of knowledge management. Further analysis assessed the comparative differences in perceptions of knowledge management in ERP, across four employment cohorts.

A maintenance-data-model of enterprise resource planning

Two distinct maintenance-data-models are studied: a government Enterprise Resource Planning (ERP) maintenance-data-model, and the Software Engineering Industries (SEI) maintenance-data-model. The objective is to: (i) determine whether the SEI maintenance-data-model is sufficient in the context of ERP (by comparing with an ERP case), (ii) identify whether the ERP maintenance-data-model in this study has adequately captured the essential and common maintenance attributes (by comparing with the SEI), and (iii) proposed a new ERP maintenance-data-model as necessary. Our findings suggest that: (i) there are variations to the SEI model in an ERP-context, and (ii) there are rooms for improvements in our ERP case’s maintenance-data-model. Thus, a new ERP maintenance-data-model capturing the fundamental ERP maintenance attributes is proposed. This model is imperative for: (i) enhancing the reporting and visibility of maintenance activities, (ii) monitoring of the maintenance problems, resolutions and performance, and (iii) helping maintenance manager to better manage maintenance activities and make well-informed maintenance decisions.

Key performance indicators for measuring construction success

The construction industry is dynamic in nature. The concept of project success has remained ambiguously defined in the construction industry. Project success is almost the ultimate goal for every project. However, it means different things to different people. While some writers consider time, cost and quality as predominant criteria, others suggest that success is something more complex. The aim of this paper is to develop a framework for measuring success of construction projects. In this paper, a set of key performance indicators (KPIs), measured both objectively and subjectively are developed through a comprehensive literature review. The validity of the proposed KPIs is also tested by three case studies. Then, the limitations of the suggested KPIs are discussed. With the development of KPIs, a benchmark for measuring the performance of a construction project can be set. It also provides significant insights into developing a general and comprehensive base for further research.

An evolutionary framework for enhancing design

A computational framework for enhancing design in an evolutionary approach with a dynamic hierarchical structure is presented in this paper. This framework can be used as an evolutionary kernel for building computer-supported design systems. It provides computational components for generating, adapting and exploring alternative design solutions at multiple levels of abstraction with hierarchically structured design representations. In this paper, preliminary experimental results of using this framework in several design applications are presented.

Attitudes towards American brands and brand America

A study of 556 students at colleges and universities in Australia, Hong Kong and Singapore explored the relationship between attitude towards the United States and brand attitudes and preferences. Singaporean student attitudes towards both the US Government and US people were higher than were those of the Australian and Hong Kong students. Coke, Nike and McDonald's were among both the most-liked and disliked US brands among the international students, a finding suggesting that brands may possess both a 'lovemark' status, as described in the literature, and its opposite — 'loathemark' status — within the same demographic group. US brand preference scores did not offer support for the belief that international consumers 'vote with their pocketbooks' by refusing to purchase US brands if they have a negative attitude towards the United States. Among Hong Kong and Singaporean students, favourable attitudes towards the purchase of US brands was found to be positively related to favourability towards the US Government.

Supporting pre-tender construction planning with virtual prototyping

Purpose – Virtual prototyping technologies linked to building information models are commonplace within the aeronautical and automotive industries. Their use within the construction industry is now emerging. The purpose of this paper is to show how these technologies have been adopted on the pre-tender planning for a typical construction project. Design/methodology/approach – The research methodology taken was an “action research” approach where the researchers and developers were actively involved in the production of the virtual prototypes on behalf of the contractor thereby gaining consistent access to the decisions of the planning staff. The experiences from the case study were considered together with similar research on other construction projects. Findings – The findings from the case studies identify the role of virtual prototyping in components modelling, site modelling, construction equipment modelling, temporary works modelling, construction method visualization and method verification processes. Originality/value – The paper presents a state-of-the-art review and discusses the implications for the tendering process as these technologies are adopted. The adoption of the technologies will lead to new protocols and changes in the procurement of buildings and infrastructure.

Framework for measuring success of construction projects

The construction industry is dynamic in nature. The concept of project success has remained ambiguously defined in the construction industry. Project success means different things to different people. While some authors consider time, cost and quality as the predominant targets, others suggest that success is something more complex. The aim of this report is to develop a framework for measuring success of construction projects. A range of Key Performance Indicators (KPIs), measured both objectively and subjectively is developed. The identification of KPIs helps set a benchmark for measuring the performance of a construction project and provides significant insights into developing a general and comprehensive base for further research.

Case studies on vibration based damage identification : multi-criteria approach

Vibration based damage identification methods examine the changes in primary modal parameters or quantities derived from modal parameters. As one method may have advantages over the other under some circumstances, a multi-criteria approach is proposed. Case studies are conducted separately on beam, plate and plate-on-beam structures. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on flexibility and strain energy changes before and after damage are obtained and used as the indices for the assessment of the state of structural health. Results show that the proposed multi-criteria method is effective in damage identification in these structures.

Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring

As a part of vital infrastructure and transportation networks, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs always make the infrastructure owners difficult to undertake. Structural health monitoring (SHM) is set to assess condition and foresee probable failures of designated bridge(s), so as to monitor the structural health of the bridges. The SHM systems proposed recently are incorporated with Vibration-Based Damage Detection (VBDD) techniques, Statistical Methods and Signal processing techniques and have been regarded as efficient and economical ways to solve the problem. The recent development in damage detection and condition assessment techniques based on VBDD and statistical methods are reviewed. The VBDD methods based on changes in natural frequencies, curvature/strain modes, modal strain energy (MSE) dynamic flexibility, artificial neural networks (ANN) before and after damage and other signal processing methods like Wavelet techniques and empirical mode decomposition (EMD) / Hilbert spectrum methods are discussed here.