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Material Economy and Logistics 06/2022

ISSN: 1231-2037
Pages: 36
Publication date: 2022
Place publication: Warszawa
Binding: paperback
Format: A4
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DOI: 10.33226/1231-2037.2022.6.1
JEL: I30, H12, M11, O32

The aim of the article is to assess the condition and indicate future transformations of international supply chains. The method of analysis and critical assessment of the state as well as a projection of changes in the studied field were used. The COVID-19 pandemic and the military aggression against Ukraine should be considered a major turning point that changes the way people think about shaping and managing international supply chains. The current standard of their management, based on a parasitic pursuit of often undue financial surplus, should be redefined. It is postulated to strive for the formation of symbiotic chains of relationship management that contribute to the socio-economic modernization of less developed countries. To be also expect projects to build a greater degree of resilience of these chains by less stringent treatment of the need to lean the chains and to a greater degree to green them. When also aiming to reduce economic risk, the length of transport corridors should be expected to be minimized, which means the geographical relocation of companies forming supply chains.

Keywords: supply chains; management goals; supply chain models; new supply chain organization and management
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DOI: 10.33226/1231-2037.2022.6.2
JEL: O35, O36, A13

This article attempts to identify relatively new phenomenon in the economy of sharing, which is the crowd logistics. Despite the growing popularity, such initiatives are not yet the subject of many scientific papers, relatively few articles deal with these issues. The essence of crowd logistics is to create connections among market participants, with specific logistical resources, and those who report logistic needs. This is aimed at minimizing ineffective use of resources and activating unused competences. Using an exploratory approach based on a review of 77 active crowd logistics initiatives, solutions used in practice were investigated. These initiatives have been divided into groups defined in this article, and the main characteristics of crowd logistics versus traditional business logistics were highlighted. Attempts were also made to identify directions for development and the potential impact of crowd logistics on traditional businesses. This paper is based on the analysis of secondary sources: articles, reports of consulting companies and case studies available in branch magazines, on company websites and presented by the companies themselves as part of webinars.

Keywords: crowd logistics; sharing economy; gig economy
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DOI: 10.33226/1231-2037.2022.6.3
JEL: F50, R41

Central and Eastern Europe has a special place in the logistics service of international trade, not only due to the strategic geographic location determining the transit position in the Trans- Eurasian supply chains. Also due to the significant share of services provided by enterprises located in the countries of the region. The outbreak of the armed conflict on February 24, 2022, initiated by the attack of Russian troops on the territory of Ukraine, should be considered a key determinant of changes in the economic ties that have existed so far. As a result of the so far made and expected subsequent decisions in the field of international political and economic relations, the region’s logistic connections will undergo reconfiguration. Importantly, their effects will be visible not only in the countries of Central and Eastern Europe, but also on a supra-regional scale.

The paper addresses the issue of the short and long-term effects of economic disturbances resulting from the Russian-Ukrainian war and their implications for the logistic service system. It is based on the perspective of geopolitical dependencies and changes in the strategic order, which is reflected in the ongoing crisis. The paper uses statistical data determining the hitherto involvement of individual countries in international flows and indicates the potential consequences of restrictions on international trade in goods. Ongoing conflict limitations in access to resources and the possibilities of their effective delivery in supply chains will cause far-reaching changes of global importance. Sanctions and retaliatory sanctions in Russia’s relations with the West, preventing the implementation of the current supplies from Ukraine covered by military operations, will shape the market selected goods and logistics services to a large extent. At this stage, it is important to be aware of the consequences of the conflict that has begun.

Keywords: Russo-Ukrainian war; logistics channels; resource availability changes; supply chains reconfiguration
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DOI: 10.33226/1231-2037.2022.6.4
JEL: C45, C53, C55, D81, R41

In recent years, more and more attention has been paid to the use of Big data technology, machine learning and AI. Enterprises strive for a competitive advantage through the appropriate use of data analytics. Big data can be used in many different industries, e.g. in the transport or medical industry, and potentially in all of them. A huge problem in the supply chain is the risk of delay, which may be influenced by many factors, including illegible label on the package, lack of warehouse workers or congestion in cities. The article focuses on the use of Big Data technology to detect the risk of delays in the supply chains of medicinal products. Its purpose is to present the concept of Big Data, Big Data architecture for the drug supply and to present the results of research related to the prediction of the risk of delays in its implementation in a real enterprise. The set goal determined the choice of the following research methods: analysis of literature and the use of modeling, which allowed to design and implement the architecture for the drug supply chain to collect data in the studied enterprise. The last part of the article presents a logistic regression model for predicting delays in the supply chain of medicinal products. The research established that the model has a high predictive ability.

Keywords: Big Data; supply chain management; risk management
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