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Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts affects directly all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on State Dependent Parameter (SDP) estimation techniques to identify the non-linear behaviour of such managerial adjustments. This non-parametric SDP estimate is used as a guideline to propose a non-linear model that corrects the bias introduced by the managerial adjustments. One-step-ahead forecasts of SKU sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a non-linear pattern undermining accuracy. This understanding can be used to enhance the design of the Forecasting Support System in order to help forecasters towards more efficient judgmental adjustments.

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A prognostic model for constructing hypotheses about the relationship of combinations of cytokines with the proliferative activity of cancer cells is proposed. The model is based on the use of inductive inference methods. The methodology takes into account the synergistic interaction of cytokines and uses sequential construction of logical formulas for selecting groups of cytokines, a statistical analysis of contingency tables and logical integration of the obtained estimates. Implementation of the proposed method in the information system of forecasting the effect of targeted anticancer drugs in immunotherapy will greatly accelerate research in this area.