Contractor Failure: Analysis

Russell, Jeffrey S.

doi:10.1061/(asce)0887-3828(1991)5:3(163)

Cited by 61 publications

(27 citation statements)

References 5 publications

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“…Economic Factors: Failure to manage the risks related to relevant economic factors leads to the strong chance that contractors will suffer economic losses (Wang & Yuan, 2011). Dun and Bradstreet (D&B) Corporation, for example, estimated that more than 60% of contractor failures are due to economic factors (Russell, 1991). According to the literature, insufficient profits, market loss, high interest rates, low consumer spending, and an uncertain future are the factors that contribute to failure.…”

Section: Factors Affecting Contractor's Performancementioning

confidence: 99%

“…If the construction project is complex, it becomes difficult to evaluate the results from limited measurements; thus, management capability as it relates to monitoring typically assumes sophisticated and accurate metrics are available for assessing project status (performance measurement) and controlling development (Al-Jibouri, 2003). Four of the most commonly used monitoring criteria in project performance are: cost, time, quality and safety (Cheng & Chen, 2002;Russell, 1991). Problem solving skills: Research conducted on contractor performance has established that although problem solving skills are highly value, most contractors continue to struggle with these activities (Ahern, Leavy, & Byrne, 2014;Lim & Mohamed, 2000).…”

Section: Management Practicesmentioning

confidence: 99%

See 1 more Smart Citation

Contractor management performance evaluation model at prequalification stage

Rashvand

Majid

Pinto

2015

Expert Systems with Applications

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Section: Factors Affecting Contractor's Performancementioning

confidence: 99%

Section: Management Practicesmentioning

confidence: 99%

Contractor management performance evaluation model at prequalification stage

Rashvand

Majid

Pinto

2015

Expert Systems with Applications

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“…Profitability can greatly influence project cash flow during project progress. Russell [32] has indicated that over 60% of construction contractor failures are attributable to economic factors. Cash flows control impacts significantly upon project management and project success.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence approaches to achieve strategic control over project cash flows

Cheng

Tsai

Liu

2009

Automation in Construction

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“…One of the critical parameters in the designing of a system is considering its cash flow. More than 60% of the failures in the construction section is caused by economic factors (Russell, 1991). Cash flow model would have a significant influence on the project.…”

Section: Introductionmentioning

confidence: 99%

Cash flow prediction using artificial neural network and GA-EDA optimization

Amalnik¹

2019

10.5267/j.jpm

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Cash flow models are one of the spotlights for evaluating a project. The actual data should be modeled then it could be used for the prediction process. In this paper, 996 airplane maintenance basis data are used as a database, and 119 similar data are chosen after clustering. The project is divided into 20 equal periods and first three periods are used for simulating the next point. The predicted data for each point is achieved by using of previous points from the beginning. The model is based on artificial neural network, and it is trained by three algorithms which are Genetic Algorithm (GA), Estimation of Distribution Algorithm (EDA), and hybrid GA-EDA method. Two dynamic ratios are used which are dividing the population into two halves, and the other is a ratio without dividing. The ratio would give a proportion to GA and EDA models in the hybrid algorithm, and then the hybrid algorithm could model the system more accurately. For each algorithm, three main errors are calculated which are mean absolute percentage error (MAPE), mean square error (MSE), and root means square error (RMSE). The best result is achieved for hybrid GA-EDA model without dividing the population and the MAPE, RMSE, and MSE values are %0.022, 28944.59 Dollars, and 837789503.79 Dollars, respectively.

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Contractor Failure: Analysis

Cited by 61 publications

References 5 publications

Contractor management performance evaluation model at prequalification stage

Contractor management performance evaluation model at prequalification stage

Artificial intelligence approaches to achieve strategic control over project cash flows

Cash flow prediction using artificial neural network and GA-EDA optimization

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