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The purpose of this paper is twofold. Our first objective is to measure the level of technical efficiency of Québec dairy farms. Our second objective is to gauge the robustness of our results with respect to the selection of a functional form and of a distribution for the inefficiency index. We estimate efficiency frontiers for Cobb‐Douglas (C‐D), translogarithmic (TL) and generalized Leontief (GL) production functions with half‐normal, truncated normal and exponential distributions. Our results, based on likelihood dominance criterion (LDC) indicate that the GL production technology dominates the other two functional forms, and this ranking is robust to changes in the distribution of the inefficiency index. Efficiency scores and ranks are highly correlated for all the functional forms and distributions. The differences in the mean levels of efficiency are statistically significant across functional forms and distributions, although the magnitude of the difference is minuscule. The very high mean level of efficiency and the low standard deviation confirms that Québec dairy farms are very homogenous in terms of getting the most from their inputs. This is not surprising, given that the sector has been very stable policywise and that it has been difficult for dairy farmers to expand. To augment the comparisons, results obtained from data envelopment analysis (DEA), are added to the analysis. In this case, the correlation coefficients between DEA and parametric specifications are found to be very low.

A mixed integer programming model that aims at supporting the tactical wood procurement decisions of a multifacility company is presented. This model allows for wood exchanges between companies. Furthermore, the material flow through the supply chain is driven by both a demand to satisfy ("pull" strategy) and a market mechanism ("push" strategy), enabling the planner to take into consideration both wood freshness and the notion of quality linked to the age of harvested wood into log, chips, and end-product demands. An inability to consider alternative plans for implementation, and the difficulty of assessing the performance of these plans in an uncertain environment, are two shortcomings of the manual planning process. A planning process, based on human planner – decision support system interactions that allows a company to overcome these shortcomings is therefore presented. The process combines Monte Carlo methods and an anticipation mechanism that will, in the long term, enable the company to take into account equipment transportation costs. The proposed planning process leads to a multicriteria decision-making problem where the human planner has to select a plan to implement from a set of candidate plans. A hypothetical test case shows that it is possible to manage the wood flow from stump to end market in such a way as to preserve freshness and extract higher value from the logs processed in the mills. The test case also shows that the proposed planning process achieves an average profitability increase of 8.8% compared with an approach based on a deterministic model using average parameter values. Finally, a sensitivity analysis reveals that the accuracy of standing inventory on harvest blocks and the anticipated market conditions are the most important parameters to consider in selecting a good wood procurement plan.

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