A Methodology and Tool for the Predictive Analysis of Cost Growth in Construction Projects

Author(s):  
Negar Tajziyehchi ◽  
Mohammad Moshirpour ◽  
George Jerzeas ◽  
Farnaz Sadeghnour
Author(s):  
Martin Oloruntobi Dada

Purpose – Using projects executed with both traditional and integrated procurement methods, the study sought to investigate relationships that exist among project participants and the influence of those relationships on cost growth. The paper aims to discuss these issues. Design/methodology/approach – Questionnaires were administered among 274 construction projects located in 12 states including the Federal Capital Territory of Nigeria. Responses were obtained from 96 projects. Data were subjected to both descriptive and inferential analyses. Findings – In terms of cordiality, relationships between client and contractors ranked highest, while those among in-house project teams ranked lowest. Cost growth or cost overrun is significantly correlated with client-contractor relationship, consultant-contractor relationship, client-consultant-contractor relationship and in-house team relationships. No association between procurement method and cost growth was found. Research limitations/implications – The limitation of generalizability of results due to the sampling method used is acknowledged. One implication of the findings is that in the context of this research, any explanation for cost growth has to be found outside procurement methods. Practical implications – Findings may assist project participants on variables to consider in anticipating, preventing or managing cost growth in building construction projects, beyond formularization of contracts and structures. Originality/value – The research has uniquely investigated the association between intangible project team relationships and tangible variable of cost growth.


2021 ◽  
Vol 147 (3) ◽  
pp. 04021002
Author(s):  
Clara Mariana Katsuragawa ◽  
Gunnar Lucko ◽  
Shabtai Isaac ◽  
Yi Su

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.


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