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The construction sector has significantly evolved in recent decades, in parallel with a huge increase in the amount of data generated and exchanged in any construction project. These data need to be managed in order to complete a successful project in terms of quality, cost and schedule in the the context of a safe project environment while appropriately organising many construction documents.

However, the origin of these data is very diverse, mainly due to the sector’s characteristics. Moreover, these data are affected by uncertainty, complexity and diversity due to the imprecise nature of the many factors involved in construction projects. As a result, construction project data are associated with large, irregular and scattered datasets.

The objective of this chapter is to introduce an approach based on a fuzzy multi-dimensional model and on line analytical processing (OLAP) operations in order to manage construction data and support the decision-making process based on previous experiences. On one hand, the proposal allows for the integration of data in a common repository which is accessible to users along the whole project’s life cycle. On the other hand, it allows for the establishment of more flexible structures for representing the data of the main tasks in the construction project management domain. The incorporation of this fuzzy framework allows for the management of imprecision in construction data and provides easy and intuitive access to users so that they can make more reliable decisions.

Due to the recent increase in unexpected events that negatively affect projects, the concept of resilience has grasped the attention of researchers, as risk management alone fails to address unpredicted events. This study aims to identify resilience dimensions and their designated factors and then construct a comprehensive definition for resilience in construction projects.

This paper adopted a qualitative research design through content analysis of semi-structured interviews with project engineers on behalf of their construction project; 26 interviews were analyzed via ATLAS.ti.

The results demonstrate that resilience in construction projects consists of four main dimensions: preparation, absorption, recovery and adaptation; each dimension comprises separate factors. The evidence from this study demonstrates that the resilience of the organization, project team and project manager supports the construction project when facing unpredicted or abnormal conditions.

The results of this paper will help construction project managers to enhance the resilience of their projects by providing different resilience factors.

The resilience concept in projects is novel and has limited knowledge available, especially in construction projects therefore, it requires additional comprehensive discussion and validation. The findings of this paper will serve as a cornerstone for the development of the resilience field and enrich the indigent literature on resilience in the construction industry.

The purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.

This paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.

With a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.

This paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

The complexity of projects has become a serious issue and obstacle in their successful completion. In order to overcome these complexities, it has become imperative to identify the relevant management competencies of project managers. The purpose of this paper is to address the problem of cost, time and scope in engineering infrastructure projects due to their complexities through management competencies.

In the first phase of the study, 32 experts were interviewed through semi-structured pre-tested questionnaire. In this phase, essential elements of complexities were identified initially. This was followed by finding required dimensions of competencies to counter these complexities and to acquire improved performance. In the final stage, required levels of competencies for specific elements of complexity were identified. In the second phase, 85 “project managers” were also approached to get feedback about their recently completed public sector engineering infrastructure projects in Pakistan.

The study identified additional dimensions, i.e. honesty, enthusiasm and dedication, in the case of competencies and adverse law and order situation, political instability, land issues, energy crisis and weak authorization of project managers in the case of complexities. Leadership, management skill, communication skill, effectiveness and result orientation were identified as top quality traits required. The study concluded that there is a significant impact of management competencies and complexities on project performance.

The study contributes to a better understanding of how to improve performance in complex engineering infrastructure projects through adopting management competencies. It also empirically illustrates the relations among project management competencies, complexities and project performance. Although the research is grounded on public sector infrastructure projects, its findings may also be helpful for practices in project management of other sectors.

There are not many answers to the question of how the development projects launched to improve business performance in SMEs have succeeded. This study focuses on business development success in SMEs. The main objective is to structure and model the success dimensions that contribute to and can be used in evaluating the business development success in SMEs.

The study utilises multiple case study methodology, following the replication approach. The empirical evidence is based on data from four SMEs that have implemented a business development project. Two of the projects were perceived as successful and the other two as unsuccessful.

This study builds a framework for a business development project success in a SME context. In the SME context the business development project success seems to be dependent on several interrelated dimensions. Success in one area leads to success in other areas, and so creates an upward success spiral. Failure in one area seems to lead to failure in other areas, too, thus creating a downward failure spiral.

The results provide a basis for benchmarking one's business and evaluate how well one's own firm meets the success dimensions and its focus areas.

The framework for success dimensions has been developed providing a systematic way to analyse the business development project and its impact on the performing company. A setting for analysing the project success from different time perspectives in a SME context has been produced.

This study aimed to contribute to the field of project management (PM) by exploring the elements of project complexity and how individuals respond to such complexity. Multiple dimensions were examined, including technical, human and political dimensions, with a significant impact on project implementation.

An in-depth case study was conducted, focusing on the implementation of a Brazilian hospital. Data were collected through semi-structured interviews, participant observation and document analysis. Bridging the research gap involved unraveling the project complexity elements and how to manage them, more specifically in the hospital context.

The findings revealed that project complexity challenges managers to deal with uncertainties, emergencies and unexpected situations. It implies coping with multiple factors of technical, human and political dimensions. Divergent interests contributed to the formation of coalitions, triggering relevant individual and group learning. Strategic improvisation had a potentially significant response from managers regarding project complexity to make adjustments and changes, focusing on project effectiveness and performance.

The challenge of managing project complexity is to deal with the balance between structure and improvisation in response to complexity. Flexibility, adaptability, self-organisation and strategic improvisation are key elements in managerial practices that address complexity, especially in hospital projects.

The differentiated approach lies in the proposed model of project complexity, with elements that make up the technical, human and political dimensions, with significant results for complex projects.

The chapter presents an analysis of the decision-making process of leaders on issues of nuclear armament and nuclear disarmament, through four case studies. The first, pertaining armament, is South Africa, with a focus on Pieter Willem Botha, former prime minister and president of South Africa. The second deals with former Libyan President Muammar Gaddafi’s nuclear armament decision. The third case study investigates the decision of former president of South Africa, F. W. De Klerk, on nuclear disarmament. The fourth and final case study uncovers Colonel Muammar Gaddafi’s nuclear disarmament decision.

Using the Applied Decision Analysis (ADA) method historical decisions have been analyzed in this chapter for the purpose of identifying each leader’s ‘decision code’. Specifically, were these decisions based on rational calculations or were they influenced more by cognitive decision processes?

By revealing a ‘decision code’ using a reverse engineering of the decision processes, I conclude that the three leaders placed high importance on security and geopolitics. By analyzing different dimensions and processes that impacted their decision processes, it is evident that while armament decision utilizes the poliheuristic decision rule, disarmament decisions abide by rational calculations.

Project-based industries face major challenges in controlling project cost and completing within the budget. This is a critical issue as it often connects to the main objectives of any project. However, accurate estimation at the beginning of the project is difficult. Scholars argue that project complexity is a major contributor to cost estimation inaccuracies. Therefore, recognising the priorities of acknowledging complexity dimensions in cost estimation across similar industries is beneficial in identifying effective practices to reduce cost implications. Hence, the purpose of this paper is to identify the level of importance given to different complexity dimensions in cost estimation and to recognise best practices to improve cost estimation accuracy.

An online questionnaire survey was conducted among professionals including estimators, project managers, and quantity surveyors to rank the identified complexity dimensions based on their impacts in cost estimation accuracy. Besides, in-depth interviews were conducted among experts and practitioners from different industries, in order to extract effective practices to improve the cost estimation process of complex projects.

Study results show that risk, project and product size, and time frame are the high-impact complexity dimensions on cost estimation, which need more attention in reducing unforeseen cost implications. Moreover, study suggests that implementing a knowledge sharing system will be beneficial to acquire reliable and adequate information for cost estimation. Further, appropriate staffing, network enhancement, risk management, and circumspect estimation are some of the suggestions to improve cost estimation of complex projects.

The study finally provides suggestions to improve cost estimation in complex projects. Further, the results are expected to be beneficial to learn lessons from different industries and to exchange best practices.

The objective of this research is to provide practitioners with inter-industry applicable rules and guidelines for the Lean Six Sigma (LSS) project definition phase. This research resulted in 13 inter-industry generic project definitions that are divided by four performance dimensions: quality, dependability, speed and cost efficiency.

A total of 312 previously executed LSS improvement projects in a broad variety of industries at black belt or master black belt level are analyzed. All these projects have followed the LSS methodology and are characterized by the use of critical to quality (CTQ) measurements and the structured improvement method of define, measure, analyse, improve and control for operations improvement.

This research resulted in 13 inter-industry generic project definitions that are divided by four performance dimensions: quality, dependability, speed and cost efficiency. Three factors that have stood out in this research are; the difficulty to capture the performance dimension flexibility in LSS project definitions, the strong focus on internal organizational benefits in defining CTQs for LSS project definitions and the unclear alignment of LSS project definitions to existing strategic objectives of the company.

This research established useable generic LSS project definitions including generic CTQ’s measures, applicable to multiple industries. These generic LSS project definitions provide useful guidance in the initial LSS project phase, helping to decompose strategic focal points into clear and measurable project objectives.

Using 12 case studies, the purpose of this paper is to investigate the use of business analysis techniques in BPR. Some techniques are used more than others depending on the fit between the technique and the problem. Other techniques are preferred due to their versatility, easy to use, and flexibility. Some are difficult to use requiring skills that analysts do not possess. Problem analysis, and business process analysis and activity elimination techniques are preferred for process improvement projects, and technology analysis for technology problems. Root cause analysis (RCA) and activity-based costing (ABC) are seldom used. RCA requires specific skills and ABC is only applicable for discrete business activities.

This is an exploratory case study analysis. The author analyzed 12 existing business reengineering (BR) case studies from the MIS literature. Cases include, but not limited to IBM Credit Union, Chase Manhattan Bank, Honeywell Corporation, and Cigna.

The author identified eight business analysis techniques used in business process reengineering. The author found that some techniques are preferred over others. Some possible reasons are related to the fit between the analysis technique and the problem situation, the ease of use-of-use of the chosen technique, and the versatility of the technique. Some BR projects require the use of several techniques, while others require just one. It appears that the problem complexity is correlated with the number of techniques required or used.

Small sample sizes are often subject to criticism about replication and generalizability of results. However, this research is a good starting point for expanding the sample to allow more generalizable results. Future research may investigate the deeper connections between reengineering and analysis techniques and the risks of using various techniques to diagnose problems in multiple dimensions. An investigation of fit between problems and techniques could be explored.

The author have a better idea which techniques are used more, which are more versatile, and which are difficult to use and why. Practitioners and academicians have a better understanding of the fit between technique and problem and how best to align them. It guides the selection of choosing a technique, and exposes potential problems. For example RCA requires knowledge of fishbone diagram construction and interpreting results. Unfamiliarity with the technique results in disaster and increases project risk. Understanding the issues helps to reduce project risk and increase project success, benefiting project teams, practitioners, and organizations.

Many aspects of BR have been studied but the contribution of this research is to investigate relationships between business analysis techniques and business areas, referred to as BR dimensions. The author try to find answers to the following questions: first, are business analysis techniques used for BR project, and is there evidence that BR affects one or more areas of the business? Second, are BR projects limited to a single dimension? Third, are some techniques better suited for diagnosing problems in specific dimensions and are some techniques more difficult to use than others, if so why?; are some techniques used more than others, if so why?