Process waste analysis for offsite production methods for house construction – A case study of factory wall panel production

There is a growing interest in the use of offsite manufacturing (OSM) in the construction industry disregarding criticisms of lacking real improvement from some offsite approaches adopted by housebuilders as compared to their onsite counterparts. Quantitative performance measures from previous studies are based on conventional onsite methods, with little attention paid to the performance and process improvements derived from various OSM methods. In response, a case study was conducted based on two OSM methods using standardized and non-standardized processes for the production stage of a factory-manufactured wall panel. Value system analysis and root cause analysis using the 5Whys method was adopted to evaluate possible improvements in terms of process waste. The study reveals that OSM production methods that replicate site arrangements and activities involving significant manual tasks do not necessarily provide a marked improvement from the conventional onsite method. Thus, there is a need to re-evaluate the processes involved to eliminate such embedded process wastes as non-value-added time and cost and to consider automating critical activities. The analysis adopted in the case study provides measurable evidence of the performance gained from having a structured workflow over a non-structured workflow. It also reveals how process wastes are generated in the production process of wall panels offsite

On the distribution of bids for construction contract auctions

The statistical distribution representing bid values constitutes an essential part of many auction models and has involved a wide range of assumptions, including the Uniform, Normal, Lognormal and Weibull densities. From a modelling point of view, its goodness is defined by how well it enables the probability of a particular bid value to be estimated – a past bid for ex-post analysis and a future bid for ex-ante (forecasting) analysis. However, there is no agreement to date of what is the most appropriate form and empirical work is sparse. Twelve extant construction datasets from four continents over different time periods are analysed in this paper for their fit to a variety of candidate statistical distributions assuming homogeneity of bidders (ID not known). The results show there is no one single fit-all distribution, but that the 3p Log-Normal, Fréchet/2p Log-Normal, Normal, Gamma and Gumbel generally rank the best ex-post, and the 2p Log-Normal, Normal, Gamma and Gumbel the best ex-ante – with ex-ante having around three to four times worse fit than ex-post. Final comments focus on the results relating to the third and fourth standardised moments of the bids and a post-hoc rationalisation of the empirical outcome of the analysis

An assessment of construction labour productivity in Malaysia

The construction industry is one of major strategic importance. Its level of productivity has a significant effect on national economic growth. The analysis of published census/biannual surveys of construction by the Department of Statistics of Malaysia shows that Malaysia managed to achieve construction labour productivity growth between 1996 and 2005 despite increases in cost per employee. The decrease in unit labour costs is attributed to the value added improvement per worker through the increase in capital intensity. The marginal decline in capital productivity is due to the gestation period and the overcapacity of the industry. The civil engineering sub-sector recorded the highest labour productivity and is the most labour competitive in terms of unit labour cost and added value per labour cost. The residential sub-sectors recorded greatest change in the productivity indicators between 1996 and 2005

Hybrid approach to reducing estimating overfitting and collinearity

© 2019, Emerald Publishing Limited. Purpose: The purpose of this paper is to present an approach to address the overfitting and collinearity problems that frequently occur in predictive cost estimating models for construction practice. A case study, modeling the cost of preliminaries is proposed to test the robustness of this approach. Design/methodology/approach: A hybrid approach is developed based on the Akaike information criterion (AIC) and principal component regression (PCR). Cost information for a sample of 204 UK school building projects is collected involving elemental items, contingencies (risk) and the contractors’ preliminaries. An application to estimate the cost of preliminaries for construction projects demonstrates the method and tests its effectiveness in comparison with such competing models as: alternative regression models, three artificial neural network data mining techniques, case-based reasoning and support vector machines. Findings: The experimental results show that the AIC–PCR approach provides a good predictive accuracy compared with the alternatives used, and is a promising alternative to avoid overfitting and collinearity. Originality/value: This is the first time an approach integrating the AIC and PCR has been developed to offer an improvement on existing methods for estimating construction project Preliminaries. The hybrid approach not only reduces the risk of overfitting and collinearity, but also results in better predictability compared with the commonly used stepwise regression

Analysis of the distribution of the number of bidders in construction contract auctions

The number of bidders, N, involved in a construction procurement auction is known to have an important effect on the value of the lowest bid and the mark-up applied by bidders. In practice, for example, it is important for a bidder to have a good estimate of N when bidding for a current contract. One approach, instigated by Friedman in 1956, is to make such an estimate by statistical analysis and modelling. Since then, however, finding a suitable model for N has been an enduring problem for researchers and, despite intensive research activity in the subsequent 30 years, little progress has been made, due principally to the absence of new ideas and perspectives. The debate is resumed by checking old assumptions, providing new evidence relating to concomitant variables and proposing a new model. In doing this and in order to ensure universality, a novel approach is developed and tested by using a unique set of 12 construction tender databases from four continents. This shows the new model provides a significant advancement on previous versions. Several new research questions are also posed and other approaches identified for future study

Scoring rules and abnormally low bids criteria in construction tenders: a taxonomic review

In the global construction context, the best value or most economically advantageous tender is becoming a widespread approach for contractor selection, as an alternative to other traditional awarding criteria such as the lowest price. In these multi-attribute tenders, the owner or auctioneer solicits proposals containing both a price bid and additional technical features. Once the proposals are received, each bidder’s price bid is given an economic score according to a scoring rule, generally called an economic scoring formula (ESF) and a technical score according to pre-specified criteria. Eventually, the contract is awarded to the bidder with the highest weighted overall score (economic + technical). However, economic scoring formula selection by auctioneers is invariably and paradoxically a highly intuitive process in practice, involving few theoretical or empirical considerations, despite having been considered traditionally and mistakenly as objective, due to its mathematical nature. This paper provides a taxonomic classification of a wide variety of ESFs and abnormally low bids criteria (ALBC) gathered in several countries with different tendering approaches. Practical implications concern the optimal design of price scoring rules in construction contract tenders, as well as future analyses of the effects of the ESF and ALBC on competitive bidding behaviour

Environmental Regulation and Green Productivity of the Construction Industry in China

There are few studies analyzing whether different types of environmental regulation have differential impacts on the efficiency of the construction industry. Using 2012-2016 panel data from 30 provinces in China, the green total factor productivity (GTFP) of the construction industry is measured with a global Malmquist Luenberger productivity index based on the epsilon measure (EBM-GML) model. Thereafter, a panel Tobit regression model is proposed to explore the relationship between three types of environmental regulation and the GTFP of the construction industry. The results show that: (1) from 2012 to 2016, the GTFP of the Chinese construction industry grew slowly at an average annual rate of 0.14%; (2) both one-phase lagged command-and-control and current phase market-based environmental regulation had a positive linear relationship with GTFP. One-phase lagged voluntary environmental regulation on the other hand, had an inverted U-shaped relationship with GTFP; (3) the three types of environmental regulation can be combined to establish a suitable environmental regulation system. The findings of this study provide guidance for the sustainable development of the construction industry by combining the actions of different types of environmental regulation

Building a Digital Transformation Maturity Evaluation Model for Construction Enterprises Based on the Analytic Hierarchy Process and Decision-Making Trial and Evaluation Laboratory Method

With digital transformation underway in various Chinese construction enterprises, each enterprise has progressed differently, and a clear direction for future digital transformation and upgrading is lacking. As such, the importance of measuring the level of digitization among Chinese construction enterprises is increasing. This paper presents a model for evaluating digital transformation maturity within construction enterprises. The model considers six aspects: digital strategy, digital business applications, digital technology capabilities, and so on. The digital maturity of enterprises is determined using the Analysis of Hierarchy (AHP)-Decision Making Experiment and Evaluation Laboratory (DEMATEL) method. Technical abbreviations are explained when first used. This study demonstrates that digital business applications are the most significant primary indicator, with a weight of 29.53%. The success of digital transformation in the construction industry is strongly influenced by the interconnection between digital technology and construction sites, as well as other factors such as new technical personnel, digital infrastructure, digital innovation, and innovation iteration ability. It is crucial to understand how digital technology and the construction industry can effectively connect in order to achieve success in this realm. This paper aims to enhance the digital transformation capabilities and efficiency of construction companies and boost their core competitiveness through targeted measures

Towards a conceptual model of the job performance of construction professionals: a person-environment fit perspective

The architecture, engineering, and construction (AEC) industry is well known for low productivity, with a critical obstacle being the difficulties associated with understanding the mechanism of job performance. This due to established theories being incapable of capturing and dealing with the complexities associated with construction projects, with extant performance studies falling short in the context of complex projects by not grounding the factors involved in a common conceptual model that articulates the mechanics of job performance. In response, this study builds on person-environment (P-E) fit theory using a stimulus-organism-response paradigm, to develop a foundational conceptual model of the performance-related working behaviors of construction professionals. This study emphasizes the mediating role of P-E fit assessment and integrates key P-E relationships in a simplified framework, with a common layer based on P-E fit assessment factors that both accommodates previous studies and lays a coherent foundation for future studies. The model can also be applied in a reverse, diagnostic, capacity to identify corrective approaches and appropriate management strategies, and is easily adaptable to more general situations beyond the construction industry. Concluding remarks concern the major contributions made and prospects for future development

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site