Roberto Revetria, Guido Guizzi, and Pietro Giribone
Forecasting, New Products, Probability Density Functions, Monte Carlo Simulation, Stochastic Approach, Forecasting, New Products, Monte Carlo Simulation, Stochastic Approach, Forecasting, New Products, Monte Carlo Simulation, Stochastic Approach
Starting in the second half of last century, researchers and managers have gradually paid more attention to that chapter of decision-making sciences that goes by the name of “Demand Analysis.” In the literature today, there are a wide range of templates to allow you to predict future demand for goods and services, with the obvious ultimate goal of providing managers with quantitative elements to improve the different planning processes related to this type of decision (purchasing raw materials, semi-finished materials, optimum input and output warehouse inventory, production planning). The matrix common to all the most widely-used models is the use of the consolidated historical data to “train” the prediction algorithm. In this paper, the authors describe a method to study those cases where the historical data is not available (new markets, new products, new technologies, etc.) and the stochastic element consequently takes on extreme importance in defining the prediction process.
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