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Society, companies and institutions are involved in a digital transformation that can be pervaded in various industries or sectors, and this also applies to communication, sales and distribution channels. The possibilities of e-commerce have also increased and world trade has been further developed. In 2020, more than two billion people bought goods or services over the Internet. Customer satisfaction depends on the solution of the last mile process, the method of picking up shipments as well as the time and place of picking up the shipment. The most common forms of off-premises delivery are automated parcel locker or machine (APM) and pick-up and drop-off delivery (PUDO). The aim of the paper is to analyse the level of the PUDO and APM network in European countries and in the V4 countries with regard to the size of the country and the population. For this purpose, it was necessary to focus on determining the population per 1 PUDO and the number of inhabitants per 1 APM in individual European countries and subsequently in the V4 countries. The obtained data were processed and recalculated in Excel. The results showed that within European countries the best values were achieved by Finland with 526 inhabitants per 1 PUDO and Spain with 188 inhabitants per 1 APM. Regarding the V4 countries, the Czech Republic achieved the best value in the case of inhabitants on PUDO with 729 inhabitants per 1 PUDO and in the case of APM Poland with 3,184 inhabitants per 1 APM.

Research background: Artificial intelligence is a term that is now known to almost everyone and is among the trends and innovations of Industry 4.0 for 2020. It is a much-discussed topic in the field of technology. Artificial intelligence and machine training are the driving forces across different industries. In many cases, artificial intelligence helps people in their work and simplifies it or even completely replaces the human workforce. Purpose of the article: The purpose of the article is to state how artificial intelligence can affect and solve existing problems in last mile delivery. For example, inefficiency is a major problem with last mile delivery because the last section of delivery usually involves a number of short-distance stops. However, a long waiting time for the customer to deliver the goods or incorrect allocation of resources and vehicles to the required areas can also be a problem. And it is artificial intelligence that should help solve such problems. Methods: Comparison, Empirical and retrospective analysis are used within the analysis of different modes of last-mile delivery. Findings & Value added: The research results shows the ways in which artificial intelligence can help solve problems in last mile delivery. Examples include The Vehicle Routing optimization (VRO), which aims to calculate the most optimal delivery route or artificial intelligence technology, which is used to interpret various events, manage data, and apply predictive intelligence.

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