In the battle of “survival of the fittest,” every business must constantly re-evaluate its processes to improve how it delivers products or services to customers, while at the same time reducing its cost of doing business. Businesses that establish an effective continuous improvement culture will be able to sustain their competitive edge.
Beginning in 2003, Black & Veatch established Six Sigma to unify and control change in its processes. The company used the methodology to drive improvements in all functional areas of the business. However, more than 70 percent of the improvement projects were focused on engineering design practices. Now in its fifth year, Six Sigma is being used to optimize design processes by linking design efficiency metrics to total installed costs.
Link to Design Processes
One of the first realizations when evaluating potential engineering improvements was that design solutions varied for similar applications from project to project and professional to professional. Some of the variation was explainable, such as differences in site conditions, contract requirements and code requirements. But some of the variation appeared to be due to differences in design approaches, individual rules of thumb and code interpretation. This variation between professionals had a direct impact on the total installed cost.
To find an approach that would consistently achieve lower installed costs, teams used the DMADV (Define, Measure, Analyze, Design, Verify) road map to develop a direct link between installed costs and design parameters, such that:
Cost metric = Design metric + Project conditions
Cost metric = Total installed cost normalized across projects
Design metric = Design efficiency linked to a project design condition
Project conditions = Project-specific conditions such as the seismic factor, building code, project location, union status and material type
Case Study: Structural Foundation Piling
In one such DMADV project, a team looked specifically at foundation piling. Piles are cylindrical shaped concrete members used to support large mat foundations in softer soil conditions. An initial review of piling designs indicated there was a varying number and capacity of piles used for similar foundations. The study also found there was an inconsistent process for selecting the optimum pile capacity and a lack of standard metrics to ensure the design was achieving the optimum installed cost. Therefore, the goal of the Six Sigma project was to reduce the cost of piling by determining the root causes of variation and developing a design metric that improved the probability of choosing an optimum solution.
The project focused on three primary building foundations used on typical projects, representing approximately two-thirds of the total required piles. Then, historical costs, bills of quantity, structural loads and other design data was collected for 43 foundations, spanning a time period of 10 years.
To normalize the piling costs, a cost metric was created that linked the total installed cost of the piles to the design dead load. Likewise, a design metric was established that linked the total pile capacity to the design dead load. The results of the analysis found that the large variation in costs was primarily due to variations in the design metric. A root cause analysis determined the primary cause of variation was the design approach used by professionals.
A regression analysis was then completed to link the design metric, cost metric and project conditions to understand the magnitude of the relationship between design and cost (R^2 = 75 percent). Based on the root cause analysis and regression results, lower and upper design tolerance levels (LTL and UTL) were established as design efficiency limits. These limits were based on an analysis of historical results, evaluating the differences between similar projects and locations. In addition, a lower specification limit (LSL) was established to address the fact that some unique site conditions would prevent 100 percent of the projects from achieving the tolerance goals (Figure 1).
The result was an aggressive shift in the group mean and a significant reduction in variation (Figure 2). The capability of the new process was ZST = 3.25, resulting in a 4 percent defect rate (primarily below the lower specification limit) due to potential site condition issues.
By following the structured Six Sigma approach, the team objectively identified the true root causes and determined their magnitudes on cost. To determine the financial benefit, the team compared the actual design metrics to the predicted condition using the new process and tolerances. The improvement shifts were evaluated individually, considering project and site conditions. Based on this analysis, more than $4 million dollars in savings were achieved over a two-year period.