BENCHMARKING THE PERFORMANCE OF EGYPT’S CONSTRUCTION INDUSTRY USING KEY PERFORMANCE INDICATORS
Ahmed Bahaaeldin Ibrahim Ghonamy;
Abstract
ABSTRACT
Construction contractors works in Egypt need to find a unique tool to measure project management performance. This tool is very important to improve contractors’ effectiveness. Key performance indicators (KPI) and Benchmark techniques can be the requested tool to evaluate such effectiveness of construction project management performance in Egypt. A framework is established to select and calculate KPIs values. One hundred and sixty two engineers participated in a Questionnaire to select main six KPIs that affect construction projects in Egypt. The selected factors are 1) cost performance, 2) construction time performance, 3) quality management, 4) safety management, 5) cash flow indicators and 6) customer satisfaction on product. Weighting System using Analytic Hierarchy Process method is implemented to set relative weight between selected KPI by using face to face sessions. Brain storming sessions are used with directors in construction companies to select a method of calculation for each factor. The main outputs are generation of equation for each KPI related to Egyptian market, especially for quality management and cash flow indicators. Three tier one construction companies are selected to share their projects data to calculate their project management performance, KPIs equations are calculated for each project then for each company to develop individual KPIs for each company then create benchmarking for the construction projects in Egypt. Produce predictive analytics modulus using Artificial Neural Networks (ANN) and Linear Regression (LR) are used to calculate time performance of construction projects, thirty projects used in training process and ten projects used in testing process. ANN model error or accuracy was 3.8% in training process and 6.6% in testing process and LR model errors or accuracy is 5.6% in training process and 5.6% in testing process. Both algorithms have testing error less than 7% which yielded acceptable results for ANN and LR models, however LR is recommended than ANN because testing error is 5.6% while in ANN testing error is 6.6%. Research outputs and results are very essential for construction companies to improve their performance, and compare their performance to other companies work in Egypt. Also it is genuine method for decision makers and investors to estimate correctly their project performance.
Construction contractors works in Egypt need to find a unique tool to measure project management performance. This tool is very important to improve contractors’ effectiveness. Key performance indicators (KPI) and Benchmark techniques can be the requested tool to evaluate such effectiveness of construction project management performance in Egypt. A framework is established to select and calculate KPIs values. One hundred and sixty two engineers participated in a Questionnaire to select main six KPIs that affect construction projects in Egypt. The selected factors are 1) cost performance, 2) construction time performance, 3) quality management, 4) safety management, 5) cash flow indicators and 6) customer satisfaction on product. Weighting System using Analytic Hierarchy Process method is implemented to set relative weight between selected KPI by using face to face sessions. Brain storming sessions are used with directors in construction companies to select a method of calculation for each factor. The main outputs are generation of equation for each KPI related to Egyptian market, especially for quality management and cash flow indicators. Three tier one construction companies are selected to share their projects data to calculate their project management performance, KPIs equations are calculated for each project then for each company to develop individual KPIs for each company then create benchmarking for the construction projects in Egypt. Produce predictive analytics modulus using Artificial Neural Networks (ANN) and Linear Regression (LR) are used to calculate time performance of construction projects, thirty projects used in training process and ten projects used in testing process. ANN model error or accuracy was 3.8% in training process and 6.6% in testing process and LR model errors or accuracy is 5.6% in training process and 5.6% in testing process. Both algorithms have testing error less than 7% which yielded acceptable results for ANN and LR models, however LR is recommended than ANN because testing error is 5.6% while in ANN testing error is 6.6%. Research outputs and results are very essential for construction companies to improve their performance, and compare their performance to other companies work in Egypt. Also it is genuine method for decision makers and investors to estimate correctly their project performance.
Other data
| Title | BENCHMARKING THE PERFORMANCE OF EGYPT’S CONSTRUCTION INDUSTRY USING KEY PERFORMANCE INDICATORS | Other Titles | مؤشر ألاداء لصناعة التشييد فى مصر باستخدام مقاييس الأداء الرئيسية | Authors | Ahmed Bahaaeldin Ibrahim Ghonamy | Issue Date | 2018 |
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