Many researchers and project managers have attempted to improve project performance by applying new philosophies such as lean principle, just-in-time, pull scheduling, and last planner. However, very little research has been conducted on setting definite quantitative goals for performance improvement while considering the defect rate involved in the construction operations. This research explores practical solutions for construction performance improvement by applying the six sigma principle. This principle provides the metrics required to establish performance improvement goals and a methodology for measuring and evaluating improvement. The proposed approach is expected to achieve more reliable workflows by reducing process variability to fit in a desirable range-thereby improving the overall performance through the evaluation of the quality level in current construction operations. To verify the suggested methodology, two case studies have been presented and process simulation analyses are performed to observe the performance changes based on the six sigma principle. Critical total quality control, as the sigma level rises, is also discussed.

Temporary fabrication plants such as rebar assembly and precast segment shops are increasingly used in large scale construction projects to provide a construction site of its material needs. A plant needs to be operated in such a way that it is flexible enough to adapt to changing project demands while minimizing inventories. Meeting such needs requires careful control of the level of raw materials and assembly products fabricated in the plant, the two main types of inventories. However, in practice the ordering of raw materials and assembly times are ad hoc, leading to excess inventories and added costs to the project. This paper presents a methodology for effective, efficient, and economic control of inventory levels in temporary rebar assembly plants. Ordering processes are formalized to convert existing approaches into a pull production system. Given this transformation, a methodology is presented that employs Monte Carlo simulation and optimization techniques to identify inventory levels that minimize inventory costs while simulating variability in demand, procurement lead times, and production capacity. A retrospective case on a rebar assembly plant shows that the same amount of work can be performed with significantly less inventory levels when applying the proposed production methodology. It also provides evidence that the cost savings from inventory costs outweigh any additional holding or delivery costs associated with a pull production system.