Backshoring in Light of the Concepts of Divestment and De-internationalization: Similarities and Differences

The paper aims to identify how Industry 4.0 technologies affect the quality and speed of the managers’ decision-making process across the different stages of the value chain, based on the example of the manufacturing sector. The paper adopts qualitative research, based on nine in-depth interviews with key informants, to capture senior executives’ experiences with implementing Industry 4.0 technologies in their organisations. The research is focused on three manufacturing industries: the automotive, food and furniture industries. The research shows that depending on the stage of the value chain, different Industry 4.0 technologies are more suitable for the support of managers’ decisions. Various Industry 4.0 technologies support decision-making at different stages of the manufacturing value chain. In the Design stage, 3D printing and scanning technologies play a crucial role. In the case of Inbound Logistics, robotisation, automation, Big Data analysis, and Business Intelligence are most useful. During the Manufacturing stage, robotisation, automation, 3D printing, scanning, Business Intelligence, cloud computing, and machine-to-machine (M2M) integration enable quick decision-making and speed up production. Sensors and the Internet of Things (IoT) optimise distribution in the Outbound Logistics stage. And finally, Business Intelligence supports decisions within the Sales and Marketing stage. It is also the most versatile technology among all particular stages. The paper provides empirical evidence on the Industry 4.0 technology support in decision-making at different stages of the manufacturing value chain, which leads to more effective value chain management, ensuring faster and more accurate decisions at each value-chain stage. When using properly selected Industry 4.0 technologies, managers can optimise their production processes, reduce costs, avoid errors and improve customer satisfaction. Simultaneously, Industry 4.0 technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks. This knowledge allows organisations to make better decisions and take proactive actions to prevent problems.

This article analyzes the grounds and possibility to induce and develop reshoring activities in the luxury goods sector. The premise of this analysis is the contemporary redefinition of the luxury goods sector, which relates to the increasing volume of goods marketed as luxuries. In this paper, we examine whether reshoring can develop in a similar manner on the respective tiers of the luxury ladder in the luxury fashion, automotive, and jewelry industries. Both premises and examples of reshoring in the luxury goods sector are conceptually analyzed and empirically illustrated.

Purpose: The main objective of the research is to identify a competence gap in "Industry4.0" – the difference between the competencies currently acquired by students at universities with a technical and economic profile, and the competencies desired by companies from the industrial processing sector. Design/methodology/approach: Empirical material was obtained in two studies. The first survey was conducted among 120 companies in the industrial engineering sector, while the second was carried out among over a thousand students and graduates of economic and technical universities. Findings: This work contributes to an in-depth understanding of companies’ needs regarding “Manager 4.0” competencies, and enables the identification of existing educational gaps. Our research results show that there is a competence gap on the labour market in each of the analysed categories of competencies: social, personal, managerial, technical and professional. At the same time, some differences are visible between students of economic and technical universities. The findings of the study suggest the need to redesign student education programs at universities so as to provide interdisciplinary education taking into account key competencies for Industry 4.0. Research limitations/implications: We identified three limitations of our research, resulting both from the size of the research sample of the analyzed comapanies, the possible ambiguity of the respondents' understanding of the examined competences (ambiguity of their interpretations) and their mutual interdependencies, as well as the subjective assessment of the students themselves. Practical and social implications: The study indicated the need for specific employee competencies, the development of which requires interdisciplinary study programmes in areas including production engineering and management. Besieds, the results of our research are particularly important for adapting employee training systems. We assume that the development of new training programs best suited to the needs of the market (need for specific employee competencies) should be done through cooperation between companies in the industrial processing sector and the academic community. Originality/value: The conclusions of the research shed new light on requirements regarding managerial positions in companies from the industrial processing sector, by indicating the need to modify curricula at universities in selected areas of competence. Keywords: Industry 4.0, Manager 4.0, human resources, managerial competencies. Category of the paper: Research paper.