الفهرس 
PROJECT AND PROJECT MANAGEMENT
THEORETICAL FRAMEWORK AND RESEARCH DESIGNOnly 14 pages are availabe for public view
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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.
Keywords: Construction Industry; Project Management (PM); Performance Measurements; Key
performance indicator (KPI); Managing projects; Egypt; Benchmarking; Predictive Analytics;
Annual Neural Networks (ANN); Linear Regression (LR).