Background. Predicting future sales is necessary to control the flow of goods in supply chains (SC), which is why firms perform forecasting of consumer demand. To study the reliability of the solution, the sensitivity analysis (SA) of the results to changes in the original data is performed. Network decision support (DS) models usually contain a very large number of elements and connections between them, which makes it difficult to perform SA.
Objective. The aim of the paper is to assess the sensitivity of the solution, given in the form of the element ranking of the network DS model, to inaccuracies and contradictions in elements of expert pairwise comparison matrices (PCMs), as well as to changes in individual elements of the supermatrix of the network DS model. To assess the priority of different types of information in the SC management system and to assess the sensitivity of the decision to obtain a more accurate forecast of consumer demand.
Methods. Evaluation of DS models to prioritize information needs in the SC management system is carried out based on a developed method of network analysis. The method for assessing the sensitivity of the decision, proposed for the hierarchical DS model, includes finding stable elements of each level of the hierarchy and assessing the degree of sensitivity of global ranking of elements. The sensitivity of a solution based on a network DS model includes an assessment of the stability of local rankings, finding the matrix elements that most affect the change in consistency and change in the local ranking, as well as the sensitivity to changes in individual elements of the model supermatrix.
Results. The method of complex assessment of sensitivity has been further developed, the stages of assessing the stability of local ranking of elements of the network DS model and the resilience of the pairwise comparison matrix elements to changes in the permissible inconsistency have been improved. A method for SA of results based on the network DS model to changes in individual elements of the supermatrix using machine learning tools is generalized.
Conclusions. In the SC management problem, the elements of the expert PCM are found, which to the greatest extent affect the change in consistency and change in the rankings of decision alternatives. The stable elements of the matrix and elements for revision by an expert were calculated in order to increase the reliability of the solution to the SC management problem based on the hierarchical and network DS models.