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Cost-benefit (C/B) analysis helps to determine the economic feasibility of business software investments. Research literature and published practices do not recognize substantial software maintenance costs in C/B analysis. Current analyses emphasize the benefits of an initial investment but do not consider the recurring benefits of each enhancement during the software lifecycle. Such analyses could lead to incorrect investment decisions and lost business opportunities. This article aims to review current research on software lifecycle costs and develop a theoretically sound C/B analysis.

This article reviews current C/B analyses and discusses their shortcomings in treating the significant recurring maintenance costs. It analyzes the findings of various studies on software maintenance and synthesizes these findings to identify the nature of various maintenance costs and their benefits. Based on the synthesis, it theorizes various cost and benefit elements for inclusion in a revised C/B analysis.

This article identifies each recurring maintenance cost relevant to C/B analysis. It also identifies recurring benefits from each enhancement that hitherto have been omitted. Finally, this article discusses how these costs and benefits should be treated in the revised C/Bs analysis.

This is a conceptual paper proposing a new C/B analysis and requires an empirical validation.

This article provides a revision of the C/B analysis that is long overdue. It will help to justify a software investment correctly, rank software projects that compete for limited funds and help create a sound software project portfolio. Since 20% of software products may incur 80% of software investment, this analysis will help to make correct software investments and avoid lost business opportunities. This article also describes a practical method to use the revised C/B analysis.

This article provides a revision of the C/B analysis that is long overdue. It will help to justify a software investment correctly, rank software projects that compete for limited funds and help create a sound software project portfolio. Since 20% of software products may incur 80% of software investment, this analysis will help to make correct software investments and avoid lost business opportunities. This article also describes a practical method to use the revised C/B analysis.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses.

The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results.

The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies.

Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

Preface The functions of business divide into several areas and the general focus of this book is on one of the most important although least understood of these—DISTRIBUTION. The particular focus is on reviewing current practice in distribution costing and on attempting to push the frontiers back a little by suggesting some new approaches to overcome previously defined shortcomings.

As real estate departments and workplace organisations devote more attention to strategic planning, most of the work has focused on improving performance metrics and developing dashboards to communicate this information clearly and concisely. Yet these steps will take these organisations only part of the way. Once they have this information, they need to devote more time to developing strategies and plans. This review examines one of these activities ‐ developing high‐level occupancy plans. Representatives of the strategy and planning groups at ten leading corporations and the occupancy planning experts at seven service providers and system developers were interviewed for this survey. It was found that most firms continue to complete high‐level occupancy plans with tedious and time‐consuming data‐collection processes and spreadsheet analyses. These organisations could improve efficiency and the success of their plans in two ways: better analysis approaches and better data collection and organisation. This review summarises the best practices identified in these areas.

The purpose of this paper is to examine the longitudinal evolution of quality costs measurement, depicted in 99 real data studies of the last 30 years. A meta-analysis of these articles is conducted, in order to highlight the evolution of the variables that have been used for the study of quality costing, in relation to the date of publication, business sector and geographical origin of each paper.

The analysis of the cost components has been conducted with the use of multiple correspondence analysis, which is a useful tool for the exploration of the interrelations among all elements, aiming at the identification of the dominant and most substantial tendencies in their structure.

The findings suggest that the level of analysis of quality costs is related to the date of publication, the business sector and the origin of each research. Furthermore, it is pointed out that the most prominent prevention costs are related to suppliers’ assurance, internal audit and new product’s design and development. Appraisal costs are mostly defined by quality audits and procurement costs, while failure costs by defect/failure analysis, low quality losses, complaint investigation and concessions and warranty claims.

The present paper is a longitudinal meta-analysis of 99 quality cost papers that have been published in the last 30 years. It explores the evolution of research in quality costing, not only in relation to the cost components in use, but also in terms of date of publication, business sector and geographical origin of the studies.

In recent years it has been often claimed that quality is one of most critical success factors for organizations. Managers introduced quality‐based programmes – such as total quality management – assuming that performances would improve. However, many quality‐based initiatives failed. There are several reasons that could explain the failure of quality‐based strategies in a number of firms; suggests two causes: the lack of effective decisional tools for evaluating the most effective investment(s) among a set of potential programmes; and the lack of specific goals to be assigned to each investment in order to monitor the actual results of the programmes on time. In small firms these problems are greater because of the limited availability of financial and managerial resources which make more difficult the identification of the most effective decisional solutions. Identifies a conceptual framework aimed at supporting the choice of most effective models for evaluating quality‐related investments in small firms, particularly an approach which balances different decisional needs such as completeness, urgency of evaluation, measurability of output and structural characteristics.