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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.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

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 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

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.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

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.

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.

The purpose of the paper is to develop a method to integrate the schedule-based analysis with a productivity-based analysis to prove and support the result of the damages calculation.

In this paper, a “cost and schedule impact integration” (CSI₂) model is proposed to objectively show and estimate lost productivity due to changes in construction projects.

A schedule-based analysis to include separate tracking of change order costs can be used to predict productivity due to the delay and disruption; changes in construction projects almost always result in delay and disruption. However, the schedule-based analysis needs to be integrated with a productivity-based analysis to prove and support the result of the damages calculation.

The results of this study expand upon construction practices for proving and quantifying lost productivity due to changes in construction projects.

The contribution of the paper is summarized as the introduction of a “schedule impact analysis” into a “cost impact analysis” technique to assess the damages, as well as to demonstrate the labor productivity impact due to delay and disruption in construction projects.