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The purpose of this chapter is to determine the essence of stagnating socio-economic systems through the prism of the theory of economic conflicts.

Comparative analysis of conceptual approaches to treatment of stagnation of socio-economic systems – the theory of cycles, the theory of economic growth, and the theory of economic conflicts – is performed. According to the theory of economic conflicts, signs of stagnation of socio-economic systems are determined with the help of methods of horizontal and trend analysis. The research objects are leading developed countries (major advanced economies – G7), which, according to the existing scientific and economic paradigm, should not stagnate, and countries of the Commonwealth of Independent States (CIS), which, in the contrary, may show signs of stagnation. The analyzed indicators are growth rate of GDP in constant prices, growth rate of GDP per capita in constant prices, and the level of unemployment rate. The research is performed in the period of post-crisis restoration of modern socio-economic systems, including the forecast period (2010–2022) based on the data of the International Monetary Fund.

As a result of the research, the essence of stagnation of socio-economic systems is determined, and the following characteristics are given: emergence after crisis, negative influence on economy, universal nature, and manageability.

The obtained conclusions show opposition of stagnation and sustainable development. Stagnation is absence of economic growth and development, regardless of social and ecological costs of economic activities. Contrary to it, sustainable development means stable economic growth with low social and ecological costs of economic activities. That’s why stagnation of economy is a negative phenomenon. Unlike crises, stagnation could and should be avoided with the help of the corresponding (anti-stagnation) measures of crisis management.

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.

This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.