The diversity of industrial robot manufacturers, proprietary programming languages, and graphical interfaces complicates robotic system deployment and operation. This heterogeneity highlights the need for intuitive, accessible, and human-centric interaction methods. This paper presents a framework for natural language-based monitoring and selected control of autonomous mobile robots using a Large Language Model (LLM) interface, structured API communication, and digital twin visualization. The approach enables operators to interact with robots through natural language without requiring detailed knowledge of robot-specific interfaces or technical commands. The framework is evaluated from the operator’s perspective, focusing on its ability to simplify access to robot information, reduce interaction complexity, and support selected control tasks. Digital twin integration provides additional visual feedback on robot status and its operating environment. The proposed framework contributes to the development of more transparent, accessible, and human-oriented robotic systems aligned with Industry 5.0 principles.
This study explores whether the additional tax deduction policy for R&D expenses raises firms’ R&D investment and advances industrial upgrading. Based on a sample of Chinese listed intelligent manufacturing firms, we empirically analyse the policy’s effect on corporate labour productivity. Results indicate that the policy significantly improves labour productivity, and the conclusion holds after parallel trend, placebo and propensity score matching robustness tests. Mechanism tests show the policy mitigates financing constraints, encourages R&D engagement and lifts capital investment to boost labour productivity. Heterogeneity analysis suggests stronger effects for eastern/central-region firms, large enterprises and firms in industries with slow technological renewal. This paper reveals the policy’s influencing channels and provides empirical evidence for improving labour productivity in intelligent manufacturing.
The increasing difficulty in recruiting qualified personnel, combined with growing workforce heterogeneity, has intensified the need for inclusive and human-centric technological solutions in occupational settings. Extended Reality (XR) is increasingly considered a promising assistive technology to support workers facing structural, cognitive, or skill-related barriers. This systematic literature review adopts a capability-oriented perspective to investigate how XR supports vulnerable workers in educational and industrial contexts. Using a structured taxonomy of human abilities, the study maps XR applications to specific capability domains and vulnerable worker categories. The findings show a concentration of evidence on unexperienced and low-skilled workers and on performance-related capabilities, while other groups and capabilities remain underexplored. By explicitly linking vulnerable user characteristics to capability enhancement, this review provides a structured user-capability matrix that supports more inclusive, human-centred, and evidence-based design of XR applications in occupational settings.
In volatile manufacturing environments, particularly in project-based plant engineering, servitization is regarded as a strategic approach to enhance competitiveness and resilience. However, empirical evidence on the relationship between servitization capabilities and firm- and/or network-level performance remains limited. This study develops and tests a multi-level framework to examine how classical and digital servitization capabilities, enabled by predictive/analytical artificial intelligence (AI), link to operational performance. A sequential mixed-methods design combined expert interviews and structural equation modelling based on survey data. Qualitative findings identified digitalization, customer relationship management, and operational excellence as key capability domains. Quantitative results show that neither classical nor digital servitization capabilities significantly link to firm-level performance. Instead, both capabilities strongly link to network-level coordination, collective operational performance, and supply chain resilience. The findings shift the performance focus of servitization from individual firms to manufacturing ecosystems and highlight network orchestration and capability renewal as key drivers of competitive advantage.
Efficient operational management is crucial for businesses, particularly in industries with perishable goods such as dairy production. In the competitive market environment of dairy foods, optimizing both sales efficiency and waste avoidance is essential for sustaining profitability and competitiveness. The primary purpose of this research is to assess the sales and waste avoidance efficiencies of regional business units within a multinational dairy production company. Data were collected from 17 regional areas across Mexico and were analyzed using a two-stage Window Analysis Data Envelopment Analysis approach. Results revealed the company’s strong sales efficiency at 0.939 across the evaluated regions. However, waste avoidance efficiency shows greater variability across the regions with an average score of 0.600. The analysis discovered important challenges in minimizing waste across different regions and time periods to optimize company’s operational processes to enhance its profitability and competitiveness.
