Alfonso Bastias

Measuring achievement of learning organizations in construction

Purpose – The purpose of this article is to provide an introduction to the LEONARDO diagnostic tool which assists organizations in determining their current level of achievement towards a learning culture.Design/methodology/approach – Using a combination of the analytical hierarchy process and the Delphi technique, the research obtained the critical questions for evaluating an organizations investment and progress towards achieving a learning culture. The questions are categorized based on five areas of a learning culture and three levels of learning within an organization. These questions are translated into a web‐based diagnostic that is validated and released for use by engineering and construction organizations.Findings – The paper provides an overview of the LEONARDO diagnostic tool including the mathematical weighting system employed for scoring organization progress and investment, the web‐based availability, and the evaluation report card provided to users. The paper also provides a summary of th...

A computer environment to support the strategic decision-making process in construction firms

A computer environment to support the strategic decision‐making process in construction firms is presented. The system implements modelling concepts originally developed to evaluate project execution strategies, extending and generalizing the modelling methodology to a broader range of strategic decisions. An application to a construction firms strategic planning is used in this paper to illustrate the modelling process. The computer system is designed to help the users in building a conceptual model for the decision problem, this model is a simplified structure of the variables and interactions that influence the decisions being analysed, including internal as well as external factors. An analytical model is then designed to predict the impact of these strategies, integrating expert knowledge and assessments of the strategic planning team into a mathematical model. The mathematical component uses concepts of cross‐impact analysis and probabilistic inference to capture uncertainties and interactions amon...

Classification and Analysis of Decision Support Systems for the Construction Industry

The implementation of a Decision Support System (DSS) in the construction industry involves many variables. The nature of the problem to be solved is the key factor, but the methodological approach to the problem, the DSS technique and the nature of the data available must also be considered. This paper presents a classification and analysis of DSS for the construction industry to help decision-makers choose the appropriate systems in the field of construction engineering and management. A rigorous content analysis was conducted on over one-hundred journal articles spanning over thirty years from the Journal of Construction Engineering and Management published by the American Society of Civil Engineering (ASCE). Five different taxonomies were applied to analysis the nature of the problem, decision, complexity, data-system and tool-technique. The findings show that DSS are mostly static, unstructured, and model-centric, and that simulation and suggestion techniques are the most used for the industry. In general, there are many ways of solving DSS problems in the construction industry, but the decision maker must strive to find the best solution for each specific problem. This study is intended to assist in the proper selection of DSS for a variety of construction engineering and management problems.

Construction Project Peer Reviews as an Early Indicator of Project Success

AbstractIn an effort to proactively identify and mitigate potential project management problems in construction projects, the United States General Service Administration (GSA) developed and implemented a project peer review process in conjunction with the University of Colorado at Boulder and Peter Associates. This paper discusses the peer review process and analyzes whether the questions used in the review can predict project success, as measured by cost, schedule, and the project peer rating, for 22 past projects. The data were analyzed retrospectively by using the point biserial correlation coefficient. The results indicate that project peer reviews conducted during the construction process can be used to assess and predict final project performance. Questions regarding working relationships, communication, timing, project controls, and relational approaches to project participants had significant correlations with project success. In addition, the relationship between the designer and contractor appe...

Identification of best practices to achieve innovation, corporate entrepreneurship and spinoff in Chilean companies

The Corporate Entrepreneurship is the entrepreneurial behavior that has an objective to create new businesses inside medium and big companies, focusing on innovation of products and processes and the development of new markets. Spin Off is a broad concept that refers to a separation of a unit within the parent company. The motives to achieve this are various, but generally are referred to the search of wining flexibility and simplicity in the operation of a particular business. Corporates Spin Offs is originated by one or more individuals (Spin-Off entrepreneurs)C that are looking for the development and capture of a high potential idea, that is based on the experienced acquired within the parent company[1, 2]. In a collaborative effort between CORFO1- Corporación de Fomento a la Producción, CPC2 - Confederación de la Producción y del Comercio, y el IDB3 - entrusted Universidad del Desarrollo with a study to evaluate and identify the best practices that incentive the Corporative Spin Off in Chile. The program included the participation of a total of 46 companies, through five independents agencies, identifying the more relevant characteristics that the companies has to have in order to increase the probabilities of success in the implementation of corporative Spin Offs.

Modelo de aprendizaje para la selección de un proyecto diseño-construcción (llave en mano) en el sector público

The primary method of public sector project delivery in the United States (U.S.) has traditionally been design-bid-build delivery. The public sector has historically separated design and construction contracts. In the 1990s, the U.S. public sector began to experiment with design-build project delivery, which combines design and construction in one contract. In 1997, a decision support system was developed to provide a formal selection model for public sector design-build projects. The model supports public owners in determining which projects are appropriate for design-build delivery. This initial model was static in nature and was based on a regression analysis of 104 projects. The analysis resulted in a predictive model with five performance criteria: overall satisfaction; administrative burden; conformance to expectations; schedule variance; and budget variance. Since 1997, the number of design-build projects has increased dramatically and public sector design-build methods have evolved. The original model can be improved with new data and a new framework to provide for an adaptive model as the industry continues to evolve. This paper presents a formalized application and use of learning capabilities to supplement the original static model. This model adjusts parameters and functions using artificial intelligence as the main knowledge engine. This approach can be adapted to many applications of decision support in the design and construction industry.