H. Ping Tserng

Analyzing the Role of National PPP Units in Promoting PPPs: Using New Institutional Economics and a Case Study

The global expansion of public-private partnerships (PPPs) has generated interest in establishing national PPP units for implementing or analyzing PPPs. Despite the good intentions for establishing such units, results have been mixed. Unless its role is clarified, a national PPP unit cannot be successful because it lacks adequate authority to respond appropriately to the changing environment. However, relevant research in construction journals is too limited to provide policymakers with constructive suggestions. This study aims to improve this situation by aligning a theoretical model with empirical data. The PPP activities can be analyzed as a game between the host government and private promoters trying to maximize their respective payoffs; thus, in accordance with new institutional economics (NIE) theory, a national PPP unit can be considered an endogenous equilibrium outcome of a game. On the basis of this perspective, three game (four consensus) theoretical models are constructed to find equilibriums: a single game for a single authority, repeated games for a single authority, and repeated games for government with multiple subordinate authorities. This study also uses a case study to present the history of PPPs in Taiwan and the evolutionary role of the National PPP Taskforce, Taiwan. National and international data confirm the theoretical model, which indicates that the common role of a national PPP unit is as a trust-creator between the public and private sectors. This pilot study contributes to the theoretical foundation that policymakers need to accelerate the learning process for implementing a PPP. It also provides researchers in the construction field with an NIE methodology for analyzing other governance structures in the construction field.

Developing an electronic acquisition model for project scheduling using XML-based information standard

In recent years, effective communication among project participants has been regarded as one of the critical factors for achieving project success. Participants in a multi-contract project acquire external real-time scheduling information from other involved parties and use this to make appropriate decisions in regard to project control. However, there are two major obstacles to project participants gaining efficient access to external information in a distributed data environment: (1) the variety of data structures that project members may use, and (2) the lack of an automatic mechanism for data acquisition. Based on the ontology defined by the eXtensible markup language Schema for Scheduling (XSS), the Data Acquisition Language for Scheduling (DALS), the Hierarchy Searching Algorithm (HSA), and an automatic mechanism called Message Transfer Chain (MTC), an Electronic Acquisition Model for Project Scheduling (e-AMPS) centralized in an information agent, Message Agent (MA), was developed. Each participant equips a Message Agent as his unique information window to automatically acquire external information and provide other participants with scheduling information as well. The ultimate goal of this study is to build an automatic communication environment for multi-contract projects to solve the abovementioned difficulties, and thus achieve effective communication among project participants.

Predicting Construction Contractor Default with Option-Based Credit Models—Models' Performance and Comparison with Financial Ratio Models

Construction contractor evaluation is a critical issue in successfully completing a project. It is important for project owners and other stakeholders to identify potentially failing contractors and to avoid awarding them contracts. Previous studies developed construction contractor default prediction models incorporating managerial or economic variables into traditional financial ratio models to enhance predicting power. However, managerial variables are subjective and qualitative, and both economic variables and financial ratios are only available periodically and may not provide the necessary information in time. This study predicts contractor default by employing three option-based credit models (BSM, CB, and BS) based on stock market information, and the empirical results show that all of the models have strong discriminatory power in ranking contractors from riskiest to safest. The misclassification rates of the three models are BSM: 10%, CB: 10%, and BS: 12.7%, all of which are smaller than that of...

A production modeling with genetic algorithms for a stationary pre-cast supply chain

Material costs comprise major portion of total costs in construction projects. In developed countries, 40-45% of total construction cost is for materials alone. In some developing countries, material costs make up as much as 60-65% of the total cost for a construction project (Formoso & Revelo, 1999). This research studied the relationships between pre-casts production operations and site construction activities to develop a pre-cast production planning model. The model was established to meet the site construction demands for pre-cast elements, satisfy internal resource constraints, and optimize total production costs of production resources. The algorithms employed for solving the pre-cast production planning model were genetic algorithms and the branch-and-bound method. The near-optimal solutions were evaluated with the quality of total production cost. In the empirical study, an actual case was used to evaluate the performance of the model and algorithms. The results show that the model and the solving algorithm provide better quality of solutions than the pre-casters production plan and improve the actual production results in the illustrative case.

Developing an ARIS-House-Based Method from Existing Information Systems to Project-Based Enterprise Resource Planning for General Contractor

In recent years, general contractors in the construction industry have gradually begun to implement a system called enterprise resource planning (ERP). During the ERP implementation process, contractors performed required analyses on daily operation functions demanded by the enterprise. The analyses focused on function mapping to ensure that ERP satisfies all the requirements, including the functions of existing information systems, and meets future requirements. The process of function mapping in the construction industry typically involves a series of lengthy and time-consuming meetings, and face-to-face discussions; systematic analysis procedure was lacking. This research will propose a novel function mapping approach, the Architecture of Integrated Information Systems (ARIS)-house-based (AHB) method, to enhance the effectiveness of meetings and improve the efficiency of discussions. In addition, AHB method will use the structure of ARIS-house diagram to guide the function mapping process, streamline existing information systems, meet future requirements, and successfully implement ERP. Finally, this research will use a case study to verify the effectiveness of the AHB method for contractor to implement ERP.

Interactive path planning for multi-equipment landfill operations

Abstract A methodology and several algorithms for interactive motion planning are developed for multi-equipment landfill operations in an automated landfill system (ALS). A system for establishing ALS is also proposed in the paper. To develop a multi-truck/multi-compactor ALS, the major problems can be classified into three principal categories: (1) navigation system for multiple devices, (2) job-site geometric model, and (3) instantaneous motion planning and control system for equipment in the work site. To solve the problems from the three categories, this paper will present a methodology to simulate the operational processes of landfill vehicles and equipment in pre-planning a landfill project as well as finding efficient and collision-free motion patterns to control autonomous landfill equipment during the construction phase. Furthermore, by linking this system to a global positioning system (GPS), the efficient traffic routing and collision-free path for each piece of equipment can be calculated by using real-time positional data acquisition in a 3-D geometric model of a landfill site.

Predicting Construction Contractor Default with Barrier Option Model

AbstractThis is the first study to apply the barrier option model to predict defaults of construction contractors and to assert that the path-dependent characteristic of the model is very suitable for describing the behavior of contractor default. Different from existing contractor-default prediction models, this research uses a much larger contractor sample in empirical analyses to alleviate sample-selection biases, and employs a Receiver Operating Characteristics (ROC) curve to assess the model performance. Empirical results of this study show that the proposed model outperforms traditional financial ratio models in differentiating the risk of defaulted and nondefaulted construction contractors. Additionally, the barrier option model has markedly better discriminatory power than when applied to non–construction-related industries. The results of this paper support the postulation that the barrier option model has significant advantages for the construction industry.

An improved approach to the subcontracting procurement process in a lean construction setting

AbstractThe subcontracting procurement process is one of the most important issues impacting the costs of engineering projects and construction projects, in particular. Traditional procedures of subcontracting procurement tend to limit the opportunities for price negotiation and cooperative relationships between contractors and neglect potential issues such as engineering interface, construction risk, and waste. Based on case studies of construction projects, we propose a “lean” subcontracting procurement process (LSPP) drawing from lean construction theory. The process consists of a novel Seven-Arrangement operation plan and four types of standard operating flows. Not only does the proposed LSPP help sub-contractors eliminate various types of waste in construction projects, it also establishes a common information platform and cooperative environment that help participating contractors understand the work emphasis of each operation and the whole operation in sequence. As a result, the relationships betwe...

Prediction of default probability for construction firms using the logit model

AbstractRecently, the high incidence of construction firm bankruptcies has underlined the importance of forecasting defaults in the construction industry. Early warning systems need to be developed to prevent or avert contractor default; additionally, this evaluation result could facilitate the selection of firms as collaboration or investment partners. Financial statements are considered one of the key basic evaluation tools for demonstrating firm strength. This investigation provides a framework for assessing the probability of construction contractor default based on financial ratios by using the Logit model. A total of 21 ratios, gathered into five financial groups, are utilized to perform univariate logit analysis and multivariate logit analysis for assessing contractor default probability. The empirical results indicate that using multivariate analysis by adding market factor to the liquidity, leverage, activity and profitability factors can increase the accuracy of default prediction more than usin...

A novel dynamic progress forecasting approach for construction projects

Highlights? We propose a novel composite prediction approach to forecast the current progress during the construction phase. ? Four Typical S-curves and practical construction cases are provided for testing its prediction ability. ? Getting better prediction accuracy almost within 10%. In this paper, we propose a novel construction project progress forecasting approach which combines the grey dynamic prediction model and the residual modified model to forecast the current progress during the construction phase. Firstly, four typical S-curves simplified from various sigmoid curves are proposed and fitted to the grey dynamic prediction model. For higher prediction accuracy, three different residual modified models are taken to amend the initial prediction value which was derived from the above step. The mean absolute percentage error (MAPE) and standard deviation of the estimate of Y (SDY) are used to assess the accuracy of the composite results. The better residual modified prediction model is adopted to combine the grey dynamic prediction model to form the novel progress forecasting approach. Then, practical completed construction cases are provided for testing the prediction ability of the proposed progress forecasting approach. Results show that the forecasting approach proposed to forecast construction progress during construction phase is able to get better prediction accuracy almost within 10% whether typical S-curves or practical cases. The new approach relatively provides an accurate, simple and stable method for predicting construction progress in comparison with the previous traditional forecasting methods.