Performance Gap
Discrepancies between the predicted number of passing units and the actual output define the stability of a production run. Yield loss variance measures the difference between the historical or expected defect rate and the real-world failure rate observed in a batch. This value acts as a warning signal that the manufacturing process has drifted from its established control limits.
Root Investigation
Analyzing this delta helps production managers identify new sources of error or degradation in equipment. If a project typically has a two percent failure rate but a new lot shows a five percent loss, the yield loss variance of three percent triggers an immediate audit. The investigation might uncover a batch of contaminated flux and worn out nozzles.
Calculating this variance for each stage of assembly (print, place, reflow, test) allows for pinpointing exactly where the losses occur. This data is essential for accurate quoting, as a high variance makes it difficult to predict the final cost of a job. Consistent monitoring of these figures leads to tighter process control and more predictable delivery schedules.
Financial Constraint
Large variances impact the bottom line by increasing the scrap cost and reducing the total number of sellable units. This measurement is only valid when compared against a robust baseline of historical performance.
