The goal of variance analysis is for managers to understand why variances arise, to learn, and to improve their firm’s future performance. For instance, to reduce the unfavorable direct materials efficiency variance, Webb’s managers may attempt to improve the design of its jackets, the commitment of its workers to do the job right the first time, and the quality of the materials. Sometimes an unfavorable direct materials efficiency variance may signal a need to change the strategy related to a product, perhaps because it cannot be made at a low enough cost. Variance analysis should not be used to “play the blame game” (find someone to blame for every unfavorable variance) but to help managers learn about what happened and how to perform better in the future.
Companies need to strike a delicate balance between using variances to evaluate performance and to promote organizational learning. If performance evaluation is overemphasized, managers will focus on setting and meeting targets that are easy to attain rather than targets that are challenging, require creativity and resourcefulness, and lead to learning. For example, Webb’s manufacturing manager will prefer an easy standard that allows workers ample time to manufacture a jacket. But doing so will weaken the drive to learn and to come up with new ways to produce. Overemphasizing performance might have other negative consequences—the manufacturing manager might push workers to produce jackets within the time allowed, even if this leads to poorer quality, which would later hurt revenues. Negative effects such as these can be minimized if variance analysis is seen as a way to promote learning.