Strategic organization in projects and firms through software integration

As integrated software solutions reshape project delivery, they alter the bases for collaboration and competition across firms in complex industries. This paper synthesises and extends literatures on strategy in project-based industries and digitally-integrated work to understand how project-based firms interact with digital infrastructures for project delivery. Four identified strategies are to: 1) develop and use capabilities to shape the integrated software solutions that are used in projects; 2) co-specialize, developing complementary assets to work repeatedly with a particular integrator firm; 3) retain flexibility by developing and maintaining capabilities in multiple digital technologies and processes; and 4) manage interfaces, translating work into project formats for coordination while hiding proprietary data and capabilities in internal systems. The paper articulates the strategic importance of digital infrastructures for delivery as well as product architectures. It concludes by discussing managerial implications of the identified strategies and areas for further research.

Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

SPICE: a business process diagnostic tool for construction projects

The construction sector is under growing pressure to increase productivity and improve quality, most notably in reports by Latham (1994, Constructing the Team, HMSO, London) and Egan (1998, Rethinking Construction, HMSO, London). A major problem for construction companies is the lack of project predictability. One method of increasing predictability and delivering increased customer value is through the systematic management of construction processes. However, the industry has no methodological mechanism to assess process capability and prioritise process improvements. Standardized Process Improvement for Construction Enterprises (SPICE) is a research project that is attempting to develop a stepwise process improvement framework for the construction industry, utilizing experience from the software industry, and in particular the Capability Maturity Model (CMM), which has resulted in significant productivity improvements in the software industry. This paper introduces SPICE concepts and presents the results from two case studies conducted on design and build projects. These studies have provided further in-sight into the relevance and accuracy of the framework, as well as its value for the construction sector.

Selection of funding schemes by a borrowing decision model: a Hong Kong case study

In financial decision-making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi-structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision-maker to analyse the financial options.

Application of the entropy technique and genetic algorithms to construction site layout planning of medium-size projects

Genetic algorithms (GAs) have been introduced into site layout planning as reported in a number of studies. In these studies, the objective functions were defined so as to employ the GAs in searching for the optimal site layout. However, few studies have been carried out to investigate the actual closeness of relationships between site facilities; it is these relationships that ultimately govern the site layout. This study has determined that the underlying factors of site layout planning for medium-size projects include work flow, personnel flow, safety and environment, and personal preferences. By finding the weightings on these factors and the corresponding closeness indices between each facility, a closeness relationship has been deduced. Two contemporary mathematical approaches - fuzzy logic theory and an entropy measure - were adopted in finding these results in order to minimize the uncertainty and vagueness of the collected data and improve the quality of the information. GAs were then applied to searching for the optimal site layout in a medium-size government project using the GeneHunter software. The objective function involved minimizing the total travel distance. An optimal layout was obtained within a short time. This reveals that the application of GA to site layout planning is highly promising and efficient.

Goal-refinement appraisal of soft projects (GRASP)

Industrial projects are often complex and burdened with time pressures and a lack of information. The term 'soft-project' used here stands for projects where the ‘what’ and/or the ‘how’ is uncertain, which is often the experience in projects involving software intensive systems developments. This thesis intertwines the disciplines of project management and requirements engineering in a goal-oriented application of the maxim ‘keep all objectives satisfied’. It thus proposes a method for appraising projects. In this method, a goal-oriented analysis establishes a framework with which expert judgements are collected so as to construct a confidence profile in regard to the feasibility and adequacy of the project's planned outputs. It is hoped that this appraisal method will contribute to the activities of project ‘shaping’ and aligning stakeholders’ expectations whilst helping project managers appreciate what parts of their project can be progressed and what parts should be held pending further analysis. This thesis offers the following original contribution: an appreciation of appraisal in the project context; a goal-oriented confidence profiling technique; and: a technique to produce goal-refinement diagrams – referred to as Goal Sketching. Collectively these amount to a method for the ‘Goal Refinement Appraisal of Soft-Projects’ (GRASP). The validity of the GRASP method is shown for two projects. In the first it is used for shaping a business investigation project. This is done in real-time in the project. The second case is a retrospective study of an enterprise IT project. This case tests the effectiveness of forecasting project difficulty from an initial confidence profile.