In the era of industrial digitalization, companies are increasingly investing in tools and solutions that allow their processes, machines, employees, and even the products themselves, to be integrated into a single integrated network for data collection, data analysis, the evaluation of company development, and performance improvement. To study the impact of Industry 4.0 on the company we used Porter’s (1985) value chain model, which is particularly useful when paying particular attention to corporate areas which have a primary role in customer value creation. Since the primary impact of Industry 4.0 is perceived in value-creating processes, and has so far had the greatest transformative effect in this area, the model can be considered to be appropriate. The objective of our research is to discover how companies operating in Hungary interpret the phenomenon of Industry 4.0, what Internet of Things (IoT) tools they use to support their processes, and what critical issues they face during adaptation. We applied a dual methodology in our investigation: We sent an online questionnaire to manufacturing and logistical service companies to investigate the IoT tools they use, and the problems they face, and received 43 answers we could evaluate. We also conducted four expert interviews with manufacturing firms to get deeper insights into the application, critical issues and development phases of IoT tools. During our research, we found that the spread of real-time data across companies—given the availability of appropriate analytical tools and methods—can have a significant impact on the entire company. In the case of CPS (Cyber Physical System), CPPS and Big Data Technologies, companies using them have been evaluated as having a higher level of logistic service, more efficient processes with their partners, improved cooperation between certain logistic functions, and higher market and financial performance and competitiveness. Applying more efficient production processes, and achieving better productivity and economies of scale, might also result in increased economic sustainability. Furthermore, we have found that companies have started on the path to digital evolution, and investments of this type have already begun.

Agri-food employment in developing countries holds significant importance due to the vital role of the sector. This study synthesizes existing literature to identify the key challenges of employment in the agri-food sector in developing countries. 17,125 articles were identified in the Scopus database and based on a two-stage systematic review of 173 articles (final database), six principal topics were identified and analyzed: (1) family farms; (2) special characteristics of employment; (3) gender disparities; (4) wages; (5) education; (6) productivity. Each segment provided important results. Family farms were found to be crucial for employment, production, and income generation in developing countries. The special characteristics of employment often involve migration and mobility challenges, while gender inequalities persist due to the special nature of production. Agricultural wages are lower compared to other sectors, further accelerating outmigration. Education plays a key role to adopting advanced production methods, but educated people often find better opportunities outside the sector. Finally, productivity is crucial in income generation and is often driven by mechanization. These six segments are interconnected, with education emerging as a key factor. This study contributes to the existing literature by providing a systematic and comprehensive synthesis of the employment challenges in the agri-food sector of developing countries and providing targeted insights for policymakers to address pressing issues such as gender inequality or low agricultural productivity.

Numerous studies handle analyses of revealed comparative advantages of global trade (especially in agriculture sector) using Balassa index, but the selected automobile industry represents new potentials to study. This study focuses on the competitiveness of automobile industry, which is a key sector due to its high value-added activities, a competitive market, with increasing technology requirements and high employment characteristics. The aim of our paper is to analyse the revealed comparative advantages of global automobile trade as well as the duration and stability of Balassa indices by applying Markov transition probability matrices and Kaplan-Meier survival function. The source of data is global automobile exports at HS6 level for 1997-2016. The paper has reached numerous conclusions. First, by analysing characteristics of global automobile trade, it turned out that China, USA, Japan and Germany were the biggest producers of cars, however the top exporters were Germany, Japan and Canada in the period analysed, together giving 40% of all products exported - the top10 countries, however, gave 71% of concentration. Second, our analysis has made it clear that the most traded/exported automobile product is vehicle with only sparkling ignition internal combustion (1500-300cm3) (870323) globally, giving more than 40% of all vehicle exports between 1997 and 2016. Third, the calculation of Balassa indices showed that Spain and Japan had highest comparative advantages in all periods analysed among the most important automobile exporters in the world.

Purpose: The current environment of globally interconnected supply chains and the dynamics of changes and potential threats significantly reduce the time for a possible response. At the same time, there is a growing demand for information necessary to mitigate the consequences. To minimise the damage and increase the resilience of supply chains, it is necessary to identify sources of threats promptly, the extent of possible damage and the possibility of preventing or minimising their impact. The aim of the paper is to structure supply chain threats and search for appropriate datasets. Methodology: The paper analyses the state-of-the-art through a comprehensive literature review and demonstrates secondary data about how free-access business data can be used as Early Warning Signals to forecast supply chain disruptive events, with a particular focus on international maritime transportation. Results: It was confirmed that companies can access many open datasets, and collecting and aggregating these data can improve their preparedness for future disruptive events. As the most important issue, the authors defined the selection of proper datasets and interpreting results with foresight. Conclusions: Identifying and analysing the relevant Early Warning Signals by companies to prevent supply chain disruptions are essential for keeping their supply chains sustainable and their resilience on a sufficient level. It was proved that general business indicators (PMI delivery time, container capacity, inflation rate, etc.) can help to signal the increasing possibility of maritime traffic problems in ports and container unavailability as usual supply chain disruption types in recent years. Therefore, the companies in the supply chains need to find, collect and analyse the appropriate data, which are, in some cases, free and available. However, it is a substantial task for data analysts to identify the most relevant data and work out the analytical methodology which can be applied as Early Warning Systems (EWS).