In order to improve the process management of table grape packaging, its performance should be evaluated. However, the literature on performance evaluation indicators is scarce. To address this research gap, we propose a method for the development of performance evaluation indicators for table grape packaging units, which are characterized by labor-intensive and highly seasonal production processes in the agro-economic sector. The stages include the following: contextualizing table grape packaging units, selecting the performance objectives, selecting techniques to be used in the development of the indicators, and applying the method to the packaging units of table grapes. The techniques adopted in the development of the indicators aimed at the cost, quality, flexibility, reliability, and speed performance objectives were data envelopment analysis, principal component analysis, quantification of the batch, compliance with the program within the established deadline, and measurement of the execution time of the batch, respectively. The results obtained in the case study demonstrate that the correlations between the performance indicators do not indicate the need to disregard any of them. Furthermore, the standard deviation values for each indicator are similar. Thus, both results of correlations and standard deviation confirm the importance of the indicators chosen for the performance evaluation of table grape packaging.

The adoption of a global index (GI) for performance evaluation has been increasingly recognized as a useful strategy for decision-making as it simplifies the interpretation and monitoring of the results. Because the GI is often built by adopting a combination of different procedures for normalization, weighting, and aggregation of indicators, it is challenging to select the optimal combination of procedures, since the countless combinations lead to different results. This paper proposes a method for the development of a robust and original GI for the evaluation of table grape production units (TGPUs). Various combinations of procedures were used to develop eighteen GIs for each TGPU. These are located in the lower-middle San Francisco valley in the northeast of Brazil, where their robustness was assessed by identifying outlier GIs and via a graphical analysis. Plausible GIs were reliably identified and a cluster analysis was conducted to categorize the TGPUs into groups considering each performance objective. The identification of the outlier GIs and the use of the plausible GIs in cluster formation constitute a new scientific approach to the topic, which can be extended to other applications and contribute to the sustainable development of several industries.