Purpose: Prior research has shown that operational intellectual capital (OIC) and investments in lean practices (ILP) lead to better operational performance. However, there have been no empirical studies on the synergetic effects between OIC components and ILP. More specifically, the question – can the efficacy of ILP be increased through OIC? – has not been studied. Accordingly, the purpose of this paper is to report the empirical results of potential synergetic effects between OIC, as a knowledge-based resource, and ILP.Design/methodology/approach: The empirical data used for this study were drawn from the fifth round of the Global Manufacturing Research Group survey project (with data collected from 528 manufacturing plants). The hypotheses are empirically tested using three ordinary least square (OLS) models.Findings: The authors’ findings highlight the importance of leveraging a system of complementary knowledge-based resources (OIC dimensions) and addresses the need for the reformulation of lean theory in terms of the emergent knowledge-based view of the firm. The results facilitate greater understanding of the complex relationship between ILP and operational performance. Building on the contribution of Menoret al.(2007), the authors argue that OIC represents a strategic knowledge-based resource that is valuable, hard to imitate or substitute and, when leveraged effectively, generates superior operational and competitive advantage.Practical implications: From a managerial standpoint, this study provides guidelines for managers on how to leverage OIC to enhance the efficacy of ILP. The authors argue that firms consider investing in OIC to increase the return from ILP, which, in turn, will enhance their operational performance and provide competitive advantage. The authors findings provide strong evidence of the importance of human, social and structural capital to enhance the efficacy of ILP.Originality/value: This is the first research paper that extends the application of the intellectual capital theory in lean literature, and argues that the OIC contributes to the efficacy of ILP. The analysis facilitates greater understanding of the complex relationship between OIC dimensions, ILP and operational performance.

The main aim of this paper is to evaluate if manufacturing firms can boost their performance through green innovations. The literature on this topic shows contradictory findings. We have concentrated on the effect of advanced manufacturing technologies (AMT) on green innovations. To the authors’ best knowledge, this research is the first to examine the impact of a firm’s own AMT on green innovation and the firm’s performance at the same time. Green innovation in our research relates to green product innovation. The data analysis is performed through three-step OLS regression analysis and two evaluation models. One model looks at AMT and how they affect green innovation, and the second model looks at how AMT and green innovations affect performance. Our findings suggest that AMT contribute to both the firm’s performance and green innovation. We found that technology is a moderator for green innovations. While the majority of research emphasizes that firms will not eco-innovate unless they receive subsidies or severe restrictions are imposed, we show that out of all innovations, 66% are green innovations. Restrictions such as having ISO 14000 certification do not contribute to green innovation, but rather the age of the firm does.

This paper aims to investigate the influence of intellectual capital (IC) on supply chain collaboration (SCC) and business performance. Two mechanisms of SCC are considered, including interorganizational communication (IOC) and shared vision (SV). Based on knowledge-based view, this study explains how IOC and SV mediate the impact of IC on business performance. Structural equation modeling was used to test the hypothesized relationships based on the survey data of 1008 firms in the manufacturing industry. The results demonstrate that IC promotes IOC directly and influences the establishment of SV indirectly through IOC in the supply chain context. In addition, IOC mediates the relationship between IC and business performance. This study establishes the interface between internal knowledge base and external supply chain relationships by examining IC as an antecedent for SCC, and it also contributes to knowledge management literature by revealing that the organization's knowledge resources can influence its performance through collaborative supply chain relationships.

This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial.

Global Manufacturing Research Group is a group of enthusiasts that researches manufacturing practices all around the world. So far the group has collected 1493 filled plant questionnaires, from 25 countries [Whybark, C., Wacker, J., & Sheu, C. (2009). The evolution of an international academic manufacturing survey. Retrieved from September 5, 2010, from http://www.decisionsciences.org/DecisionLine/Vol40/40_3/dsi-dl40_3pom.pdf]. This research divided the sample into three groups (low performers, medium performers and best performers). An extensive exploratory research on the data is conducted to find out why the best performers are better than the others and what should the others do to become more profitable. First, best performers are identified and then their competitive priorities and applied manufacturing practices are analysed. The results show that quality is most important to best performers. Best performers score better in comparison with their competitors, they score higher in the usage of most explored ‘best practices’, and the level of rejects of the incoming material is significantly higher for the best performers. This result can be interpreted as that lower and medium performers should review their incoming material inspection and lower their manufacturing costs accordingly as these are considerably higher in the group of middle and low performers in comparison with high performers.