The multilevel model for the formation and assessment of resource flows of a metallurgical enterprise is presented, which, at the logistics positions, reconcile the enterprise flow processes at all management levels, providing procedures for regulating the parameters of material and financial flows due to parametric and structural coordination in the short period of time and system coordination and adaptation of goals in the long term period. Drawing on the theory of logistics, it is possible to define the resource flow in project management as an aggregate of the enterprise's own and attracted resources, considering in the process of interconnected and interdependent changes and movements carried out to achieve the objectives of the project. Optimization models of rational options selection for attracting additional resources, which allow implementing energy-saving projects under conditions of suspending finances at definite time periods due to a change in the project implementation schedule are described.

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Majority of the IT companies realized that ability to analyse and use data, could be one of the key factors for increasing of number of successful projects, portfolios, programs. Key performance indicators based on data analysis helps organizations be more prosperous in a long term perspective. Also, statistical data are very useful for monitoring and evaluation of project results which are very important for managers, delivery directors, CTO and others high level management of company. The Data Science methods could make more efficient project management in several of business problems. Analysis of historical data from the project life-cycle based on Data Science models could provide more efficient benefits for different stakeholders. Differential of the project data vector with target as an integral evaluation of the project success which allow for the complex correlations between separate features. Therefore, the influence of features importance and override creatures could be decreased on the target. This study propose new approach based on Data Science providing more efficient and accurately project management, taking into account best practices and project performance data.

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