Purpose The purpose of this paper is to estimate and analyse the technical efficiency (TE) component of productivity for a sample of winegrowers from the Douro Demarcated Region in Portugal. Design/methodology/approach The data were collected through face-to-face surveys and includes a sample of 110 farmers’ vineyards with specific input-output information and other data about production systems during the year of 2017. The authors use a two-stage data envelopment analysis using bootstrap techniques to obtain TE scores in the farmers’ vineyards and to examine the determinants of its efficiency. Findings The results show that some farmers’ vineyards have a low efficiency level and that there are essential determinants of the production system, which can influence its efficiency. This suggests considerable opportunities for improvement of wine grape productivity through better use of available resources considering the state of technology. Originality/value This work has overcome the lack of data in the farmers’ vineyards, the lack of efficiency studies in the region and also allowed to evaluate the production systems and to assess their impact on efficiency.

Abstract.To determine the key variables of the vineyard efficiency is imperative to account the combined effects of the inputs interactions since they have implications on the overall final production. This paper estimates the productive efficiency of a wine-farm sample from the Douro Demarcated Region (DDR) while identifies economic, social and environmental indicators that characterizes the DDR grape production system. The data was collected by face-to-face surveys performed at farm level to build a pilot study. The majority of the sampled twenty farms are dedicated to mountain viticulture and mainly feature the cordon (simple and double) training system. Through the Data Envelopment Analysis (DEA) method, the productive efficiency of the sampled was performed and the results clinched different efficiency scores. The main explanation is related to the heterogeneity of the adopted production system. In addition, they revealed how grape producers could improve their productive efficiency by adopting particular practices and identifying the key factors of their system.

Portugal is a country traditionally dedicated to viticulture and characterized by the production of wines of high quality. However, although it continues to be a major player in the world, both in the extension of vineyards and in the production of wine, it is certain that in recent years Portugal have lost market share in these areas. In this context, it is interesting to analyze if this situation could be related to the level of productive efficiency of vineyards. Therefore, the aims of this study are to analyse the farms that are efficiently allocating resources to achieve maximum production and to identify characteristics that make the farm more efficient. In addition, we want to analyse the productive efficiency of the farms from a regional perspective. To achieve this purpose, we use a database collected by face-to-face surveys from a sample of 154 wine-growing farms with specific input-output information from 2017. These farms are locating in the three regions of the North of Portugal (Minho, Douro and Trás-os-Montes), which represents more than 40% of the Portuguese vineyard area. To analyse the productive efficiency of the farms, we use the Stochastic Frontier Analysis (SFA). The results show that the efficiency level in the wine-growing farms from the North of Portugal is arround 67%, but with significant differences at regional level. Many of these discrepancies may be due to structural factors, such as the type of grape produced in each region. In conclusion, the findings make evident that the most efficient farms are not the most profitable due to the structure of the existing value chain.

In this paper we consider a practical lot-sizing problem faced by an industrial company. The company plans the production for a set of products following a Make-To-Order policy. When the productive capacity is not fully used, the remaining capacity is devoted to the production of those products whose orders are typically quite below the established minimum production level. For these products the company follows a Make-To-Stock (MTS) policy since part of the production is to fulfill future estimated orders. This yields a particular lotsizing problem aiming to decide which products should be produced and the corresponding batch sizes. These lot-sizing problems typically face uncertain demands, which we address here through the lens of robust optimization. First we provide a mixed integer formulation assuming the future demands are deterministic and we tighten the model with valid inequalities. Then, in order to account for uncertainty of the demands, we propose a robust approach where demands are assumed to belong to given intervals and the number of deviations to the nominal estimated value is limited. As the number of products can be large and some instances may not be solved to optimality, we propose two heuristics. Computational tests are conducted on a set of instances generated from real data provided by our industrial partner. The heuristics proposed are fast and provide good quality solutions for the tested instances. Moreover, since they are based on the mathematical model and use simple strategies to reduce the instances size, these heuristics could be extended to solve other multi-item Agostinho Agra ; Michael Poss ; Micael Santos lot-sizing problems where demands are uncertain.