Application of Artificial Intelligence Methods for Improvement of Strategic Decision-Making in Logistics

Highly evolving economic environment requires from logistics companies fast response and agile solutions. Recently development of digital technologies gives significant advantages to logistics business. Hence many optimized processes belong to operational management level. At the same time the importance of digital technologies adoption to strategic management level should not be underestimated, as it allows gaining competitive advantages alongside the supply chain. In our research we develop a conceptual framework for matching operational and strategic management decisions in order to achieve the stated strategy. The choice of the appropriate strategy is conducted with artificial intelligence tools, machine learning in particular. The present study demonstrates high efficiency of applying artificial intelligence tools both in operational management and strategic decision-making. The research is focused on transportation and inventory management as the most resource consuming and challengeable logistics operations. At the same time these processes makes the most considerable influence on the configuration of supply chains. So the article proposes a multi-level conceptual approach that includes several steps aimed on identifying key metrics for different market strategies in logistics and introduction of artificial intelligence tools to different management levels in order to contribute to decision-making promptly. On the first step we suggest the model targeting to optimization of transportation costs via reduction of logistics cycle duration, and estimation of logistics-related assets. On the second step it is suggested to define the most appropriate market strategy by defining a set of metrics relevant for each strategy. So the proposed approach allows obtaining the most suitable market strategy for logistics companies with artificial intelligence tools. The optimization solutions suggested by the authors are tending to be practically applied and claims their high relevance in terms of digital transformation and adoption in strategic logistics management.