Approaches to demand modeling for goods transportation in corn logistics

Abstract

The article deals with the concepts and problems of corn logistics in Ukraine. As a direction for increasing the efficiency of corn logistics, the forecasting of demand for transport services for corn transportation through modeling is proposed. It is established that demand modeling is carried out in order to obtain the main indicators of the goods transportation process, as well as their forecasting and further improvement. Probabilistic modeling involves not only building models for analyzing and optimizing the structure of routes and choosing the capacity of transport, but also predicting the volume of traffic with an assessment of their impact on the main indicators of efficiency, reducing the time and resources spent on logistics operations.

The basic models of demand for transportation of domestic scientists are considered. The research results of the demand for transport services show that the random nature of demand has to be taken into account. The basic demand-forming unit is the request for transport service - the customer's need for services, backed by purchasing power and presented on the market for its satisfaction. The service request is the basis and the cause of interaction between the elements of the logistics system of goods delivery – a forwarder, a carrier, a goods terminal and a goods owner. The set of potential and real requests for the services of the enterprise form the demand for its services, respectively, the set of requests for services of all enterprises of the region represents the demand for transport services in the region.

The works of scientists who use econometric models as a basis for demand modeling (linear OLS regression model, autoregressive model with distributed ADLM lag, unlimited vector VAR autoregressive model) are studied. Such models calculate the elasticity of demand for goods trasportation, depending on the level of economic activity, where the key indicators of the assessment are the industrial production index and gross domestic product by purchasing power parity and different modes of transport.

The article also considers the nature, scope, and parameters of demand modeling using programs such as TRANS-TOOLS, STAN, TAPAS, SYNTRADE, INTERLOG, Urban Distribution, and their effectiveness for corn logistics.