Estimating potential improvement project benefits unfortunately have not always been subjected to same level of analytical rigor that practitioners insist on applying in typical Six Sigma work. The fault lies in the lack of an analytical methodology. Project teams can help themselves over this hurdle with a straightforward method for calculating potential and actual benefits.
Analytical Method for Identifying Project Benefits
To estimate project benefits in the Define phase, the project team must develop a clear understanding of the following:
- What is the measurable Y that project proposes to improve?
- What is the current performance of this Y?
- What is the achievable or targeted performance of this Y after improve phase?
For example, suppose the Y in question is the number of defective products that cannot be shipped, which is converted into percent defective per day (assuming production volume remains constant day to day). Suppose the percent defective is currently at five percent and the team thinks that it can bring this down to 1 percent. How does the team go about estimating the project benefits?
The team can do this using the familiar cause-and-effect thought process that has always been applied in fishbone analysis. But unlike a typical fishbone analysis which has only one effect and multiple hypothesized causes, in this analysis, the team has only one cause and multiple hypothesized effects.
| Project Benefits Cause-and-Effect Table: If the Defect Percentage Can Be Decreased from 5% to 1%… |
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|
Benefits |
…Then |
Cost of Doing |
Cost After Doing |
Project |
| Defects Reduction |
Then company can decrease number of scrapped units per day from 50 to 10 given a daily production volume of 1,000 | 50 x 100 x 365 = $1,825,000 (cost per unit scrapped is $100) |
10 x 100 x 365 = $365,000 |
$1,460,000 |
| Warranty Reduction |
Then company can meet order commitment of 980 units per day | Company is failing on average to deliver 30 units per day and the penalty is $1 per unit not delivered |
$0 |
$10,950 |
| Maintenance Reduction |
Not affected as maintenance cost remains the same | Â | Â | Â |
| Labor or Asset Reduction | Not affected as there are no actual headcount reductions or asset sales | Â | Â | Â |
| Actual Transfer of Labor or Assets to Other Use |
Then company can redeploy one quality control inspector from this line to another line | Salary of one quality control inspector is $60,000 a year |
$0 |
$60,000 |
| Transportation Reduction |
Not affected | Â | Â | Â |
| Excess Inventory Reduction | Not affected | Â | Â | Â |
| Cost Avoidance | Not affected | Â | Â | Â |
| Lost Profit Avoidance | Then company can sell an additional 30 units a day | 30 x 10 x 365 = $109,500 (based on $10 profit per unit sold) |
$0 |
$109,500 |
| Profit Enhancement Via Added Sales |
Not affected as company does not have orders beyond 980 units per day | Â | Â | Â |
| Total Estimated Project Benefits |
$1,640,450 |
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Of course In most typical DMAIC projects, there are two typical phases where a Six Sigma project team has to compute project benefits – in Define and in Control. In the Control phase, the project team can repeat the method outlined in the table aove, this time using the actual yield gains produced by the improvements. Suppose Y moved from 5 percent to 0.5 percent then the actual project benefits will be much more than the estimated.
Conclusion: More Accurate Projections
The crux of the cause-and-effect thought process for estimating and computing project benefits lies in asking the following question: “If we can improve our project Y from the current state to the improve state, then what benefits will follow?” By using this analytical framework, Six Sigma project teams can obtain a more accurate and comprehensive computation of project benefits.
