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In the Industry 4.0 era, the Digital Twin has become one of the most promising enabling technologies supporting material flow. Although the literature on the Digital Twin is becoming relatively well explored, including a certain number of review papers, the context of the Digital Twins application in internal transport systems has not been investigated so far. This paper thoroughly reviews the research on the Digital Twins applied in internal transport systems concerning major research trends within this research area and identification of future research directions. It provides clarification of various definitions related to the Digital Twin concept, including misconceptions such as a digital shadow, a digital model, and a digital mirror. Additionally, the relationships between terms such as material handling, material flow, and intralogistics in the context of internal transport systems coupled with the Digital Twin are explained. This paper’s contribution to the current state of the art of the Digital Twins is three-fold: (1) recognition of the most influential and high-impact journals, papers, and researchers; (2) identification of the major research trends related to the Digital Twins applications in internal transport systems, and (3) presentation of future research agendas in investigating Digital Twins applied for internal transport systems.

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use and low values of forecast errors. However, to this day such methods are still undergoing refinement. In article [1] the authors justify the need for adjustments in the Holt-Winters double exponential smoothing. In article [2] the authors present a selection method of parameters of model exponential smoothing, which are solved by the minimization of the problem. In [3] the authors of the most commonly used techniques for smoothing such as moving average or exponential smoothing. They specially designed weight coefficients which were employed for smoothing the forecast.Currently, forecasting models are often supported by artificial intelligence methods. In article [4] the authors applied immune algorithm for estimating the optimal coefficients of logarithm support vector regression. In [5] neural networks of the Autoregressive Integrated Moving Average (ARIMA) method was used to reduce the sensitivity to input errors. In [6] the Bayesian network was used to predict the stock price.The future of road transportation development impacts investment decisions of companies. An interesting approach to the use of forecasting in the supply chain optimization is described in [7]. The article presents the optimization of the supply chain cost by methods of integer programming. Data needed to optimize the production capacity and warehouse inventory is obtained by forecasting using the method of exponential smoothing. A lot can be found in literature on the use of forecasting in the fields of transport. A guidebook [8] has been compiled for those involved in transport planning. It provides a number of forecasting techniques.In this paper double exponential smoothing method was used to estimate the volume of freight transport. Calculations were made using Holt-Winters double exponential smoothing, but as optimization variables the a and b parameters as well as the initial values of F 1 and S 1 (Equation 1 and 2) were adopted, and optimization ABSTRACT