The role of uncertainty in evaluating business efficiency

What is it about?

Accounting for uncertainty is vital in order to achieve stable and reliable performance evaluations for firms as business environments are becoming increasingly volatile and unpredictable. Robust data envelopment analysis (DEA) models with budgeted uncertainty have been attracting particular attention in the literature for modelling uncertainties to obtain robust efficiency in a way that guarantees solutions do remain feasible. A concern with such robust DEA models - which has been ignored in the literature - is that incorporating high uncertainty levels might result in too conservative efficiency measures, possibly reducing the decision support value of such information. To overcome this concern, this paper tackles uncertainties using variable budgeted uncertainty which is a generalisation of the budgeted uncertainty. We introduce a novel robust DEA model with variable budgeted uncertainty that is less conservative than extant robust DEA models. Furthermore, we suggest a solution for specifying the probabilistic bounds for constraint violations of the uncertain parameters in robust DEA models.

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Why is it important?

A comparison of our novel robust DEA model with existing robust DEA models shows an average reduction in the price of robustness approximately by 20%. Finally, usefulness and applicability of the suggested model are demonstrated by using a large-scale dataset in the context of grocery retail logistics.

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