The article examines the content and process of forming the principles of a customer-oriented approach to the management of transport enterprises in the context of sustainable development. The author's definition of the concept of “principles of a customer-oriented approach to the management of transport enterprises” is given on the basis of its understanding as strategic and tactical areas of activity aimed at maximizing the satisfaction of customers' needs and expectations through the use of digital technologies and innovative approaches. The key (...) principles of a customer-oriented approach to managing transport enterprises in the context of sustainable development are developed, among which the following are highlighted: understanding customer needs; personalization of service; interaction at all stages; digital transformation of processes; data analysis and forecasting; creation of a digital environment; training and development of personnel; culture of service and open communication; continuous analysis and improvement; focus on the sustainability of relations, etc. The author's own model of digital transformation of transport enterprises using the principles of a customer-oriented approach is proposed. A generalized characterization and impact on the processes of customer focus of individual digital technologies for managing customer relations of transport enterprises is presented. (shrink)

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination (...) coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen. (shrink)