A simple approach to adjust factor weights in data envelopment analysis

In data envelopment analysis (DEA), each decision-making unit (DMU) incorporates its most favorable set of weights to calculate its efficiency. This results in a set of weights that varies for each DMU. To reduce the variation in factor weight for all DMUs, this study introduces a statistic approach to construct the lower and upper bounds of factor weights. This approach enables the weight bounds be adjusted to avoid the absence of efficient DMU or reduce the number of efficient DMUs. It can also be used to integrate expert opinions or resolve the conflict between the results obtained using DEA model and the experience of experts if expert information is available. Two examples illustrate that this method has merits in both flexibility and discrimination in performance evaluation. Moreover, the second example shows that the weights of inefficient DMUs are not suitable for ranking.