Journal of Quality and Reliability Engineering

Research Article

Simulation-Based Fuzzy Logic Approach to Assessing the Effect of Project Quality Management on Construction Performance

Table 10

Settings of the scenarios and their corresponding simulation outputs.
Scenarios →123
Input settings (Performance Levels of the PQM practices)
ExpeditingStable (60%)Stable (60%)Stable (60%)
Supplier qualificationReactive (38%)Stable (60%)Stable (60%)
Int. and ext. examinationsReactive (38%)Stable (60%)Stable (60%)
Change and communicationsReactive (38%)Stable (60%)Stable (60%)
Operability and value reviewReactive (38%)Stable (60%)Reactive (38%)
Constructability reviewReactive (38%)Stable (60%)Stable (60%)
Personnel qualificationStable (60%)Stable (60%)Continual (76%)
Risk managementReactive (38%)Stable (60%)Reactive (38%)
Safety managementStable (60%)Stable (60%)Continual (76%)
Quality levels of the project requirements ( )
Material supplyPoor Good Good
Design informationAverage Good Very good
Work conditionsGood Very good Very good
Statistical parameters of the number of disruptions ( )
Excavation 2.471.931.42
1.110.811.07
Bedding 2.221.450.95
1.110.500.78
Pipe installation 2.721.941.44
1.350.801.06
Backfilling 2.221.460.94
1.110.500.78
Statistical parameters of the duration of delays ( )
Excavation 168.8445.5721.43
85.5846.0028.75
Bedding 251.19176.10161.68
114.0084.9987.56
Pipe installation 164.9051.8941.63
85.9548.6547.27
Backfilling 164.9087.6956.64
85.9529.9146.97
Outputs and output analysis
MPE (meter/hour) 2.533.473.70
Productivity increase—37.15%46.24%
TIPL—132%120%
RPI —0.280.39
MPE: mean productivity estimates; TIPL: total increase of the performance levels; RPI: relative productivity improvement.