Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity\r\nof planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties\r\nsurrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic\r\nmethods are extensive in production planning, they seem to be inefficient because of daily fluctuations in real\r\nfactories. Decision support systems can provide productive tools for production planners to offer a feasible and\r\nprompt decision in effective and robust production planning. In this paper, we propose a robust decision support\r\ntool for detailed production planning based on statistical multivariate method including principal component\r\nanalysis and logistic regression. The proposed approach has been used in a real case in Iranian automotive industry.\r\nIn the presence of existing multisource uncertainties, the results of applying the proposed method in the selected\r\ncase show that the accuracy of daily production planning increases in comparison with the existing method.
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