scholarly journals Parametric estimation of supplier's plant construction costs

2022 ◽  
Vol 1 (2) ◽  
Author(s):  
Remo Rossi
Keyword(s):  
Linear Regression Analysis ◽  
Construction Cost ◽  
Parametric Estimation ◽  
Cost Driver ◽  
Industrial Building ◽  
High Tech ◽  
Construction Costs ◽  
Product Cost ◽  
Average Percentage ◽  
Cost Calculations

Cost engineers of buying enterprises perform detailed product cost calculations of externally manufactured components. The aim of these calculations is to determine what a product should cost and to support purchasing functions in fact-based negotiations. While product cost engineers have deep knowledge in the calculation of direct cost, they need support in the calculation of supplier´s indirect cost categories. The calculation of industrial rent, which is expressed in annual cost per m² of occupied plant building floor space can be improved by providing accurate construction cost estimates. Construction costs are strongly impacting the calculation of supplier´s annual building depreciation, which is a crucial cost driver for the determination of the industrial rent. Academic literature is actually not providing an accurate and suitable cost model for product cost engineers, which is estimating construction cost per m² depending on different industrial building categories and alternative supplier plant locations. The paper aims to close this gap by applying linear regression analysis on a set of European construction cost data considering two industrial building categories: “warehouses/basic factory units” and “high-tech factories”. By regressing construction cost against construction labor rates within different supplier plant locations it was possible to form suitable and accurate parametric regression functions with R² values between 0.74 and 0.88. Next to high R² values acceptable mean average percentage errors between 7.45% and 11.77% could be realized by comparing estimated with observed construction cost. The estimation of industrial construction costs based on the paper´s results can be used to improve the calculation of industrial rent, which is one cost element, that has to be covered within product cost engineer´s Should Cost Calculations.

scholarly journals An Investigation into Factors Influencing Construction Costs Based on Japanese, UK and US Contractor Practice

2012 ◽  
Vol 2 (2) ◽  
pp. 27-35
Author(s):  
Hong Xiao ◽  
David Proverbs
Keyword(s):  
Indirect Costs ◽  
Construction Cost ◽  
Office Building ◽  
Construction Process ◽  
Close Attention ◽  
Construction Costs ◽  
Cost Performance ◽  
Factors Influencing ◽  
Site Design ◽  
The Uk

Construction cost is a major concern to both clients and contractors. Based on a hypotheticalconstruction project (a six-storey concrete framed office building), cost and otherrelated information was collected through a survey of contractors in Japan, the UK and theUS. Using multiple regression analysis it was found that lower overheads, less prefabricatedcomponents, and fewer design variations could reduce construction cost. Overheadslargely represent indirect costs to contractors and if reduced can lead to increased profitlevels and improved competitiveness. The use of prefabricated components may be problematicwhere there are delays in production, insufficient coordination between design andconstruction, and congested transportation on site. Design variations during constructionbring about uncertainties and are disruptive to the construction process. These factorshave paramount impact on construction cost and demand close attention and consideration.Contractors are advised to reduce the costs of their overheads and utilise prefabricationappropriately, while clients and designers should minimise the number of designvariations during construction if better cost performance is desired.

scholarly journals Study on Foundation Pit Construction Cost Prediction Based on the Stacked Denoising Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Lanjun Liu ◽  
Denghui Liu ◽  
Han Wu ◽  
Junwu Wang
Keyword(s):  
Influencing Factors ◽  
Construction Cost ◽  
Average Error ◽  
Support Vector ◽  
Calculation Error ◽  
Denoising Autoencoder ◽  
Construction Costs ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Project Construction

To accurately predict the construction costs of foundation pit projects, a model based on the stacked denoising autoencoder (SDAE) is constructed in this work. The influencing factors of foundation pit project construction costs are identified from the four attributes of construction cost management, namely, engineering, the environment, the market, and management. Combined with Chinese national standards and the practice of foundation pit project management, a method of the quantization of the influencing factors is presented. 60 deep foundation pit projects in China are selected to obtain 13 main characteristic factors affecting these project construction cost by using the rough set. Then, considering the advantages of the SDAE in dealing with complex nonlinear problems, a prediction model of foundation pit project construction costs is created. Finally, this paper employs these 60 projects for a case analysis. The case study demonstrates that, compared with the actual construction costs, the calculation error of the proposed method is less than 3%, and the average error is only 1.54%. In addition, three error analysis tools commonly used in machine learning (the determination coefficient, root mean square error, and mean absolute error) emphasize that the calculation accuracy of the proposed method is notably higher than those of other methods (Chinese national code, the multivariate return method, the BP algorithm, the BP model optimized by the genetic algorithm, the support vector machine, and the RBF model). The relevant research results of this paper provide a useful reference for the prediction of the construction costs of foundation pit projects.

Housing supply and its relationships with land supply

2015 ◽  
Vol 8 (3) ◽  
pp. 375-395 ◽  
Author(s):  
Guowei Gu ◽  
Lynne Michael ◽  
Yapeng Cheng
Keyword(s):  
Least Squares Method ◽  
Linear Regression Analysis ◽  
Time Lag ◽  
Housing Price ◽  
Construction Cost ◽  
Ordinary Least Squares ◽  
Price Impact ◽  
Housing Supply ◽  
Land Supply ◽  
Content Type

Purpose – This paper aims to explore the determinants of housing supply and the relationships between land supply and housing supply in terms of quantity and time in Shanghai, China. Design/methodology/approach – Official statistical and property registration data[] from Shanghai are used to carry out multiple linear regression analysis. Findings – The authors find that land supply affects housing supply with a three-year time lag. Both construction cost and housing price impact supply with a one-year time delay. The construction cost elasticities range from 0.74 to 1.51, while housing price elasticity is 2. The authors also find that plot ratio may play more important role in the developer’s first housing sale than either plot area or sales price. An average time period from obtaining the land for residential development until marketing the product is established at 36.8 months. Research limitations/implications – Only ordinary least squares method is applied in this analysis and the property portal on which this research relies is still at an early stage. Originality/value – This research contributes to a wider understanding of issues surrounding housing supply in the local markets within China and provides the foundation for local government to better manage supply.

scholarly journals Construction Cost of Green Building Certified Residence: A Case Study in Taiwan

2019 ◽  
Vol 11 (8) ◽  
pp. 2195 ◽  
Author(s):  
Chen-Yi Sun ◽  
Yin-Guang Chen ◽  
Rong-Jing Wang ◽  
Shih-Chi Lo ◽  
Jyh-Tyng Yau ◽  
...  
Keyword(s):  
Residential Buildings ◽  
Public Information ◽  
Residential Building ◽  
Green Building ◽  
Construction Cost ◽  
Construction Costs ◽  
Practical Strategy ◽  
The Government ◽  
The Relationship

The green building certification system of Taiwan, EEWH (Ecology, Energy Saving, Waste Reduction and Health), has been in operation for more than 20 years (since 1999). In order to understand the relationship between green building certification and the construction costs of residential buildings, this study obtained 37 green building-certified residential cases and 36 general residential cases available from public information and conducted a comparative analysis. The results of this study showed that the average construction cost of a green building certification residential building was only 1.58% higher than a general residential building, indicating that green building certification does not require a large increase in costs. However, for residential buildings, achieving a high-grade (gold-grade or diamond-grade) green building certification means an increase of 6.7% to 9.3% in construction costs. This shows that the pursuit of higher levels of green building certification does require higher construction costs. In addition, the results of this study can not only provide important references for the government in making green building policies, but also offer a practical strategy for developers for decision-making.

scholarly journals Study on Key Cost Drivers of Prefabricated Buildings Based on System Dynamics

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Na Lou ◽  
Jingjuan Guo
Keyword(s):  
Cost Control ◽  
Rapid Development ◽  
Construction Cost ◽  
Cost Driver ◽  
Systematic Research ◽  
Cost Drivers ◽  
Building Cost ◽  
Cost Constraints ◽  
The Cost ◽  
Prefabricated Building

The prefabricated building as a major initiative has been put forward by China in recent years to promote the transformation and upgrading of the construction industry, but its rapid development also faces high cost constraints. Therefore, it is necessary and urgent to study the key cost drivers and cost control paths of prefabricated buildings. Most of the current research focuses on the construction cost of prefabricated building as a static object. This article, on the other hand, regards the construction cost of prefabricated building as a dynamic formation process and conducts systematic research from product systems, technical systems, construction processes, and management modes. The influence factors of prefabricated building cost are defined and screened with the help of HSM and previous research results. A cause-and-effect model and cost control model of prefabricated building cost driver are established. Based on the model test of the actual project, the cost generation of prefabricated buildings is simulated. Through sensitivity analysis, key cost drivers of prefabricated building are identified and ranked as degree of design standardization, unit price, prefabrication rate, information technology level, transportation mode, labor level, machinery level, transportation distance, etc. Accordingly, corresponding strategies are proposed for the cost control of prefabricated buildings.

Quantification of Concrete Waste Generated in Housing Construction Sites via Site Sampling: Development of a Multiple Linear Regression Model

2015 ◽  
Vol 802 ◽  
pp. 676-681
Author(s):  
Siti Hafizan Hassan ◽  
Hamidi Abdul Aziz ◽  
Izwan Johari ◽  
Mohd Nordin Adlan
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Multiple Linear Regression Model ◽  
Real Data ◽  
Housing Construction ◽  
Construction Sites ◽  
Average Percentage

Waste generated in construction sites has recently increased and has become an uncontrollable cause of environmental problems and profit loss to contractors. The lack of real data or research on such wastes is due to the lack of suitable policies regarding this issue. The actions of contractors are not controlled by rules on this issue. This situation leads to the lack of action or awareness on the side of the contractor. Concrete waste is also part of the waste generated in construction sites. We determine the concrete waste generated in construction stages and conduct multiple linear regression analysis of the amount of column waste generated. The methodology employed in this study involves site observations, interviews with site personnel, and sampling at housing construction sites. The estimation method is utilized for the sampling of concrete waste. Results show that the average percentage of column waste is 13.93% and that of slab waste is 0.34%. These percentage values are derived from the total order of the concrete. The difference is due to the sizes of structures and method of handling. The regression model obtained from the sample data on column waste resulted in an adjustedR2value of 0.895. Therefore, the model predicts approximately 89.5% of the factors involved in concrete waste generation.

scholarly journals Increased fly ash utilization — value addition through forest road reconstruction

2017 ◽  
Vol 44 (3) ◽  
pp. 223-231 ◽  
Author(s):  
Tomi Kaakkurivaara ◽  
Heikki Korpunen
Keyword(s):  
Fly Ash ◽  
Construction Material ◽  
Construction Materials ◽  
Cost Calculation ◽  
Value Addition ◽  
Construction Costs ◽  
Forest Road ◽  
Uniform Layer ◽  
Cost Calculations ◽  
Fly Ash Utilization

Increasing forest bioenergy utilization is increasing the need to discover more applications for fly ash to avoid dumping charges. Our study concentrates on defining the work phases of reconstruction work and estimation of construction costs for a method using biomass based fly ash. Cost calculations were carried out for two mixed structures of fly ash and aggregate, two uniform structures of fly ash, and a conventional aggregate structure, where construction material volumes were calculated per kilometre for each structure. Our study defined suitable machines and their productivity per hour for different work phases. Cost calculation equations were formed for the used machines and the transportation of construction materials. Our study showed that building a 250 mm thick uniform layer of fly ash was the best alternative for minimizing construction costs. However, building a 500 mm thick uniform layer of fly ash was the best alternative for minimizing dumping charges.

Investigating impact of the order activity costing method on product cost calculations

2008 ◽  
Vol 27 (4) ◽  
pp. 176-189 ◽  
Author(s):  
David J. Meade ◽  
Sameer Kumar ◽  
Kevin R. Kensinger
Keyword(s):  
Product Cost ◽  
Cost Calculations

scholarly journals Exploring the Components of Cost on Construction Projects

2018 ◽  
Vol 2 (2) ◽  
pp. 42-50
Author(s):  
Abimbola Windapo ◽  
Alireza Moghayedi ◽  
David Oliphant ◽  
AbdulRauf Adediran
Keyword(s):  
South Africa ◽  
Construction Projects ◽  
Resource Planning ◽  
Construction Cost ◽  
Research Approach ◽  
Construction Work ◽  
Construction Costs ◽  
Cost Overruns ◽  
Planning And Control ◽  
Electrical Installation

This study examines the components of construction projects and whether there are construction resources that are the key project constituents. The rationale for the study stems from the unexplained assumptions regarding the primary components responsible for increases in construction costs in South Africa, as South Africa lacks a national building cost database. The study adopts a qualitative research approach that employs a case studies of six new and six refurbished projects in obtaining the necessary data for use in answering the study objectives. The study found that the primary cost constituents of construction projects are materials and sub-contracted work, accounting for 63.69% and 74.6% of the value of renovation and new construction work respectively and on the average, the major materials by value are reinforcement, cement and filling, while Electrical Installation is the primary sub-contracting item by value. Based on these findings, the study concludes that the future levels of construction work can be predicted knowing levels of specialist sub-contractor costs and building material costs. The study recommends that the sub-contractor and material inputs into construction projects are carefully managed, both on the projects and the construction industry, to limit construction cost increases and cost overruns on projects. The study contributes to the literature on resource planning and control in construction. Keywords: Cement, Construction Cost, Electrical Installation, Reinforcement, Specialist Sub-contractor.

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