Search Results (2,575)

Under the open-innovation paradigm, firms’ management of innovation output has surpassed traditional approaches such as confidentiality and patenting, evolving toward mechanisms such as strategic knowledge disclosure (SKD). As firms become increasingly embedded in global open-innovation networks, reconciling the tension between the need for innovation-knowledge disclosure and the reality of external-relationship embedding has emerged as a research agenda. Grounded in open-innovation theory, this study uses a panel of A-share manufacturing companies spanning 2009–2021 to examine how customer dependence (CD) affects suppliers’ SKD. Employing fixed-effects negative binomial panel regression, as well as robustness checks, we find that stronger CD significantly weakens suppliers’ SKD. Mechanism analysis shows that this effect operates through the channel of research and development (R&D) investment. Suppliers with high CD are more likely to reduce R&D investment, thereby suppressing their SKD. We further find that knowledge accumulation positively moderates the relationship between CD and suppliers’ SKD, while market competitiveness negatively moderates it. By constructing a theoretical framework for suppliers’ SKD under CD, this study enriches our understanding of the mechanisms and boundary conditions of firms’ SKD in terms of supply-chain relationships. The findings offer actionable insights to help suppliers embedded in supply-chain business partnerships formulate SKD. Full article

The challenge of inventory optimization is extremely important for all manufacturing companies, as inventory costs significantly impact operational efficiency. The Economic Order Quantity (EOQ) model was developed to address this issue, and it is widely used to formulate it, as it generally considers only a few parameters and a single objective. This research develops a simulation-based framework that integrates multiple EOQ-based inventory policies and performs multi-objective optimization using the NSGA-II algorithm. The framework optimizes total cost, fill rate, and average inventory level and finally generates a Pareto front as a result. To reduce computational costs, we use a machine learning-based random forest model, which replaces a significant amount of the simulations with predictions. This reduces the simulation cost to approximately one-sixth of the original, while the quality of the simulation changes only minimally, as the hypervolume value decreases by only 4%. The proposed framework can be used as an effective decision-support tool for inventory optimization under stochastic demand conditions. Full article

This study assesses liquidity, profitability, and working capital management (WCM) strategy in Polish manufacturing companies in the pressure-casting industry, drawing on evidence from the pre-COVID-19, COVID-19, and Russia–Ukraine war periods. Using panel data from 19 companies representing 90% of the Polish aluminum diecasting industry, we employ non-parametric tests (Mann–Whitney U and Kruskal–Wallis) to analyze the data. The period after the COVID-19 crisis coincides with the Russian–Ukrainian war. These countries are Poland’s neighbors. This period of uncertainty for Poland has led to supply chain disruptions and reduced investments. For manufacturing companies, this is dangerous because they have limited development opportunities. The results indicate the adoption of a conservative WCM strategy in Polish aluminum foundries during the pre-COVID-19, COVID-19, and Russia–Ukraine war periods, characterized by increased inventory levels, extended operating cycles in large firms. Additionally, the results showed reduced the level of receivables in large companies and visible decrease in the level of financial liquidity and profitability—however, these differences are not statistically significant. Polish aluminum foundries are adapting their WCM strategies toward an optimal, conservative approach that incorporates both safe and risky elements to ensure continued operations and profits. In addition, larger Polish aluminum foundries exhibit distinct liquidity patterns relative to smaller foundries, particularly in indicators of inventory, receivables, and fixed assets. In addition, the Russia–Ukraine war period exhibits distinct liquidity characteristics in Polish aluminum foundries compared with the COVID-19 and pre-COVID-19 periods, particularly in inventory turnover and operating cycle. The results of this study offer several novel contributions to the existing literature on financial security indicators by examining unexplored factors related to size and period. The results of this study have several practical implications for business leaders seeking to adopt an optimal liquidity, profitability, and WCM strategy. Full article

High-tech manufacturing is a technology- and knowledge-intensive strategic industry. Its total factor productivity (TFP) directly impacts national competitiveness and economic quality. In China, despite rapid growth, TFP performance varies across sub-sectors and firms. In this study, TFP was adopted as the central outcome variable to capture the comprehensive production and technological efficiency of high-tech manufacturing firms. The Technology–Organization–Environment (TOE) framework was integrated with Dynamic Qualitative Comparative Analysis (Dynamic QCA) to examine the causal complexity, dynamic evolution, and industrial heterogeneity of TFP, using a sample of Chinese A-share-listed companies from 2015 to 2024. The results showed that high TFP depends on configurations rather than on a single factor. Three configurational paths were identified, including “technology–innovation–scale synergy,” “technology–scale dual core,” and “technology-led productivity optimization.” All paths require a strong technological foundation. Conversely, a lack of technology leads to low total factor productivity across all sectors. Moreover, the effectiveness of these pathways evolves over time. The dual-core pathway serves as a stable baseline model. The synergy pathway is reinforced in fast-iteration sectors. Due to weak innovation support, the productivity optimization pathway declined after 2019. Third, different sectors show distinct patterns. Fast-iteration sectors use synergy to handle rapid technical changes. Slow-iteration sectors use the dual-core model to share R&D risks. Productivity-optimized sectors stagnate because they focus on automation instead of innovation. This work reveals deep patterns in TFP growth and provides theoretical support and practical insight for strategic choices of firms, industry resource allocation, and industrial policy optimization. Full article

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems. Full article

As sustainable development strategies keep advancing and the external environment remains unstable, green innovation resilience has become a critical capability for enterprises to cope with uncertainty and achieve low-carbon transformation. This study uses panel data of China’s A-share listed companies from 2010 to 2023 to explore how executive environmental attention drives firms’ green innovation resilience. Based on the attention-based view, this study explores the direct effect of executive environmental attention and the moderating role of environmental regulation and corporate social responsibility. The results show that executive environmental attention is significantly positively correlated with green innovation resilience. In addition, environmental regulation and corporate social responsibility both strengthen the positive effects of executives’ environmental attention and green innovation resilience, and this impact is mainly reflected in high-tech industries. Heterogeneity analysis further shows that the promoting effect of executives’ environmental attention on green innovation resilience is more significant in large-scale, high industry competition and manufacturing enterprises. By adopting a micro-level perspective, this study deepens our understanding of the cognitive basis for firms’ green sustainable development in an uncertain environment. It also provides theoretical evidence and practical implications for enterprises to enhance green innovation resilience by strengthening executive environmental cognition and improving internal and external governance mechanisms. Full article

Digitalization and artificial intelligence (AI) are seen as promising pathways for small and medium-sized enterprises (SMEs) to enhance performance while preserving environmental and social resources. This paper identifies organizational determinants of AI maturity that can enable SMEs to use AI in a more sustainable, responsible, and capacity-enhancing manner. AI adoption becomes relevant to sustainability not only because a company adopts advanced technology but because this technology is embedded in leadership practices, employee competencies, interdisciplinary collaboration, and organizational learning. From this perspective, perceived benefits and management commitment are not outcomes of sustainability but mechanisms that help explain how SMEs transition from technological awareness to building organizational capacity. Such capacity building can be a necessary prerequisite for subsequent sustainability-oriented outcomes, such as efficient resource utilization, employee upskilling, responsible AI management, and long-term resilience. We conducted a cross-sectional survey among 402 managers from Polish SMEs (62 micro, 193 small, 147 medium) across manufacturing, services and trade industries. Respondents (mean age ≈ 42.5 years) assessed perceived benefits of AI, engagement of top leadership, AI maturity and competitive pressure. Partial least-squares structural equation modeling revealed that perceived benefits strongly predicted leadership engagement (β = 0.647), explaining 62.8% of its variance. Perceived benefits (β = 0.384) and leadership engagement (β = 0.362) in turn were the key drivers of AI maturity, with the model accounting for 65.5% of variance in AI maturity. Competitive pressure positively but weakly moderated the relationship between perceived benefits and leadership engagement (β = 0.011), while its moderating effect on the relationship between perceived benefits and AI maturity was not significant (β = −0.008). These findings suggest that articulating clear benefits of AI and securing active leadership engagement are more decisive for advancing AI maturity than external competitive pressure. The contribution of the study is to integrate the perceived benefits of AI, top management commitment and AI maturity into a model, empirically validated and interpreted from a socio-technical perspective of sustainable digital transformation in SMEs, while quantifying the moderating role of competitive pressure in the under-researched context of Central and Eastern Europe. For practitioners, investing in awareness of AI’s benefits and developing committed leadership may yield more sustainable digital transformation than reacting solely to external pressures. Full article

Knowledge management (KM) is increasingly becoming a critical success factor in Germany’s manufacturing industry due to demographic change, the shortage of a skilled workforce, and the growing need for flexible and resilient production systems. This study contributes empirical evidence on current KM practices in manufacturing and derives practice-oriented design implications for future LLM-supported KM systems. Two consecutive survey rounds involving six companies in Survey 1 and five companies in Survey 2 were conducted in order to identify current KM practices, recurring barriers, and design implications for large language model (LLM)-supported KM. The results show that KM is perceived as highly relevant, but is implemented only incompletely in practice. Across both datasets, central themes such as fragmented documentation practices, reliance on interpersonal transfer of tacit knowledge and uneven integration of digital KM tools recur consistently. Based on the identified practices, the paper further derives areas in which LLMs may support or augment existing KM processes, particularly with regard to semantic retrieval, contextualization, onboarding, and the preservation of tacit knowledge. The findings also highlight that successful implementation of artificial intelligence (AI)-enabled KM in manufacturing will depend on technical feasibility, trust, usability, and organizational acceptance. Full article

This paper introduces the ETMS-RCPSP (Earliness–Tardiness Multi-Skill Resource-Constrained Scheduling Problem)—a novel problem derived from the MS-RCPSP by adding constraints on project completion time or actual production contracts. The goal of the new problem is to control the project completion time as closely as possible to reality—this differs from the original MS-RCPSP, which aimed to minimize project execution time. The objective of the problem is of greater practical significance in ensuring project completion on schedule while also addressing related issues, such as the ability to receive finished products on time as stipulated in the contract. The ETMS-RCPSP is an NP-hard problem whose result can be used for resource allocation in project execution or for resource arrangement in production lines to fulfill economic contracts. To address the ETMS-RCPSP, the paper proposes a new evolutionary algorithm, BDERL (Balanced Differential Evolution with Reinforcement Learning), that combines differential evolution with a problem-specific decoding mechanism and an adaptive parameter control strategy based on reinforcement learning (Q-learning). The proposed algorithm is evaluated on benchmark instances derived from the iMOPSE dataset and the TNG company dataset—a real-world dataset from manufacturing and contract-driven environments. Experimental results demonstrate that the approach consistently reduces total production costs compared to baseline heuristics while maintaining competitive computational efficiency. The findings underscore the efficacy of adaptive hybrid optimization techniques in solving intricate production scheduling problems characterized by limited resources and varied skill competencies. Full article

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution. Full article

Hand scraping (kisage) is a precision finishing technique in which a skilled craftsperson uses a hardened scraping tool to selectively remove minute amounts of metal from a workpiece surface, achieving flatness and surface texture unattainable by conventional machine processes. This technique continues to play a decisive role in the manufacture of high-precision machine tools—particularly for guideway and datum surfaces—yet it faces a serious skill-succession crisis driven by the retirement of master craftspeople and the absence of systematic transmission mechanisms. This paper provides a comprehensive review of hand scraping technology, tracing its historical origins and fundamental principles and organizing the current research landscape into four interrelated pillars structured along two analytical levels: (1) skill digitization and transmission, (2) surface measurement and evaluation, (3) tooling and process innovation, and (4) automation systems. Primary qualitative field data gathered at a specialist machine tool repair company—Ando Kikai Kogyo Co., Ltd. (Ome, Tokyo)—are used to provide evidence on the realities of skill transmission in industrial practice. Building on this analysis, the paper discusses the prospects for artificial intelligence integration, from AI-assisted contact-pattern recognition to semi-automated scraping systems, and proposes a near-future roadmap centered on Human–AI collaboration rather than full automation. The paper argues that genuine mastery of scraping cannot be separated from its physical enactment—that knowledge of scraping and the action of scraping are inseparable—and that the appropriate response is to design Human–AI systems that augment and preserve this embodied knowledge rather than seek to replace it. Full article

The rapid advancement of artificial intelligence (AI) has reshaped corporate innovation, yet the existing literature has largely overlooked the non-linear boundary conditions of AI’s innovation effects. This study asks: what is the functional form of the AI–innovation relationship, and through which mechanisms does it operate? Using a sample of 25,204 firm-year observations from Chinese A-share manufacturing companies (2010–2023), we employ fixed-effects models, U-tests, bootstrap mediation, and text similarity analysis. The findings reveal an inverted-U-shaped relationship with a turning point at 2.948. Absorptive capacity partially mediates this relationship, while industry concentration negatively moderates it. Patent text similarity analysis confirms the “homogenization trap.” Heterogeneity analysis shows AI’s enabling effect is more sustainable in non-state-owned and high-tech firms. This study extends the TOE framework by identifying the optimal AI adoption range and empirically validating the homogenization trap, offering guidance for firms to invest in proprietary AI models and for governments to promote open data initiatives. Future research should test these findings across different institutional contexts, particularly European economies. Full article

Although the importance of intangible assets has been widely recognized, few studies have discussed the increasingly important relationship between environmental legitimacy (EL) and corporate intellectual capital, especially green intellectual capital (GIC) in the context of green transformation. Drawing on the integration of institutional theory and resource-based view, this study aims to investigate the impact of GIC on EL and whether their relationship is moderated by the role of digital transformation (DT). This study was conducted on a sample of 270 manufacturing companies from China, which were tested by hierarchical regression analysis. Results show that all three dimensions of GIC (i.e., green human capital (GHC), green structural capital (GSC), and green relational capital (GRC)) are positively associated with EL. GRC is an important path for GHC and GSC to EL. Moreover, DT positively moderates the effect of each dimension of GIC on EL, and the mediating effect of GRC is found to be moderated by DT. This study identifies the positive impact of intangible assets, represented by GIC, on corporate legitimacy, enhancing our understanding of how intellectual capital improves legitimacy. Companies should pay attention to the accumulation of GIC, especially the maintenance of green relations with stakeholders, and use digital transformation wisely to gain legitimacy and enhance competitiveness. Full article

The increasing amount of untreated electronic waste, particularly in the telecommunications sector, is having a negative impact on the environment, not only by increasing the production of greenhouse gases, but also by reducing the availability of resources such as metals. At the same time, these metals are increasingly in demand to meet the manufacturing needs of new technologies. One solution is to recover metals by recycling end-of-life electronic boards. However, current processes are often implemented by large companies but are not suitable for small organizations or those with fewer resources, thus limiting their participation in local electronic waste management. Based on laboratory-scale analyses, this project compares the metal concentration results of three pre-treatments that could be suitable for smaller organizations: magnetic separation, chemical pre-treatment with sodium hydroxide, and centrifugation. The proposed preparation step, after the shredding of telecom electronic boards down to a particle diameter of less than 1 mm, is two-stage centrifugation. This pre-treatment enables metals to be concentrated efficiently and safely prior to hydrometallurgical processing. Full article

Industry 4.0 is transforming the way companies manufacture, improve, and distribute products, moving toward fast, intelligent, and flexible manufacturing, which will bring about fundamental changes in enterprises’ production capabilities. The Flexible Job Shop Scheduling Problem (FJSP) allows a single job to be divided into multiple operations, each of which can be processed on multiple machines. Due to its high flexibility and complexity, traditional scheduling methods are difficult to meet the needs of dynamic production. Dispatching rules struggle to effectively perceive the global precedence relationships among jobs and the distribution of machine workloads; metaheuristic approaches suffer from slow iterative convergence; existing deep reinforcement learning methods often employ a single policy network to handle both operation sequencing and machine assignment in a coupled manner, which tends to cause training instability and slow convergence. This paper proposes a deep reinforcement learning model that integrates Multi-Proximal Policy Optimization (MPPO) and Dual Attention Network (DAN) to address the FJSP. The model uses the operation message attention block and machine message attention block of DAN to capture the dependency relationships between operations and the dynamic competitive relationships between machines, respectively, and extract deep features. At the same time, MPPO designs dual actor networks to handle operation sequencing and machine assignment decisions separately, and combines a centralized critic to optimize the policy. This balances exploration and exploitation and improves training stability. Experiments are conducted based on the SD1 and SD2 datasets. In FJSP instances of four scales, the model is compared with PPO-DAN, PPO-HGNN, traditional scheduling rules, and OR-Tools. The results show that the algorithm reduces makespan by up to 4.2% on SD1 and 10.1% on SD2. Moreover, it achieves better performance than traditional scheduling rules. Its comprehensive performance is superior to that of the comparison methods, verifying its effectiveness and practical application potential in solving the FJSP. Full article