Learning and knowledge creation are acknowledged as crucial drivers associated with entrepreneurial orientation (EO) and innovation capability (IC). Absorptive capacity (AC) harmonizes internal innovation-building activities with external opportunities, while organizational learning capabilities (OLC) foster entrepreneurial cognitive skills and innovation capabilities. This study aims to (1) predict the impact of absorptive capacity and organizational learning on IC and (2) to estimate the mediation effect of OLC and AC on the relationship between entrepreneurial orientation and innovation capabilities. Drawing upon the dynamic capability theory, the study had applied a cross-sectional survey targeting Algerian firms to test the hypotheses. The study findings indicate that EO is positively associated with innovation capability. Besides, even though both OLC and AC partially increase EO’s interaction impact on innovation capability as a mediation role, suggesting that the more a firm is entrepreneurially oriented, the more learning and knowledge creation processes are involved in the interaction relationship. This study’s core focus is to overcome the substantial role of learning and knowledge creation processes as an intangible proactive resource that fosters the EO mindset to get involved in innovation activities. It has been discovered that AC and OLC as a connectedness mechanism links external knowledge and internal learning processes to accompany EO readiness during innovation development.

Previous research has identified metrics that are applicable to both lean and agile strategies and has hypothesised that financial and efficiency metrics are more relevant to the lean supply chain strategy, while customer service and flexibility metrics are more relevant to the agile supply chain strategy. These metrics need to be assessed empirically to confirm their relevance and validate these hypotheses. Drawing upon contingency theory, which mandates that supply chain performance metrics should vary based on the supply chain strategy, the research methodology resulted in developing a survey instrument that has been subsequently tested in 45 large enterprises and analysed by Partial Least Square-Path Modelling using XLSTAT software v.2020.4. The results support the existing beliefs and suggest that financial and efficiency indicator sets are more applicable to the lean supply chain strategy, whereas customer service and flexibility indicator sets are more pertinent to the agile supply chain strategy. This research distinguishes itself, through its novelty, in validating an adaptable framework for supply chain performance metrics, acknowledging the necessity of developing a suitable supply chain performance system. Ultimately, the findings of this research might serve as an initial foundation for practitioners in shaping the design of supply chain performance systems since the strong relationship between SC strategies and specific metrics may serve as a strategic approach to evaluate and improve performance.

Previous research has been limited in examining the causal relationship between green transformational leadership (GTL) and green supply chain management (GSCM), with the intermediary influence of green human resource management (GHRM), within the pharmaceutical sector of the Kingdom of Saudi Arabia (KSA). This gap persisted despite the recognition in Saudi Vision 2030 of logistics, specifically, supply chain management (SCM), as fundamental to the national development agenda, given that contemporary competitiveness lies in the efficacy of supply chains (SCs) rather than individual companies. Moreover, the achievement of economic progress hinges significantly on how well these accomplishments align with sustainability demands and obstacles. This paper aims to investigate the extent to which GTL fosters GRHM practices to enhance the maturity of GSCM performance in the pharmaceutical industry in the KSA. In other words, the research goal is to explain the variance of GSCM due to GHRM and GTL. Drawing upon the Resource-Based View (RBV) and the Ability–Motivation–Opportunity theory (AMO), GTL can enhance many aspects of GHRM, such as employee performance measurement, training content design, recruitment criteria, and green-based rewards policies, which positively influence GSCM practices. The methodology employed is deductive and translated into a questionnaire derived from a comprehensive review of the existing literature. This questionnaire was subsequently tested through the collection of 111 responses from pharmaceutical companies operating in the KSA. The results show the critical effects of GTL and GHRM on GSCM in this sector. The research provides fresh theoretical perspectives and actionable recommendations based on the outcomes. As for originality, this research explores the contribution of transformational leadership and green human resource management in enhancing SC sustainability. The research provides directions for future research to investigate the mediating or moderating impacts of other significant factors, such as green thinking or eco-friendly behaviour, on SCM sustainability. As for practical implications, this research came up with an in-depth understanding of SC managers and professionals regarding their practices related to sustainability.

Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding leadership type, human resource management practices, innovation orientation, change management, information and communication technology deployment, process maturity, and resource availability. These differences present challenges when leading any change. Process mining (PM) tools can optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”. The present study investigates how a PM project is implemented in an FSME operating in the agri-food sector, focusing on challenges faced in every project phase to extract the most appropriate process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle methodology combined with quality and lean management tools such as the fishbone diagram, Pareto diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing process for an FSME operating in the agri-food sector. To achieve theoretical production capacity (TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle methodology allowed for the identification of five critical causes. An action plan to eliminate causes was proposed to the FSME managers.

This study explores the estimation of import lead times in the oil and gas services industry by combining Business Intelligence (BI) and Machine Learning (ML) within the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework. Addressing the challenge of limited data availability, we employ data synthesis techniques alongside machine learning algorithms to model and predict lead times accurately across diverse import scenarios. The methodology begins with a dual perspective on the problem, encompassing both business and technical requirements, followed by systematic data collection, cleansing, and preparation. BI tools facilitate the visualization of key insights, allowing for enhanced data comprehension and performance tracking. Machine learning models are then applied to predict lead times under varying import conditions, optimizing decision-making in terms of transport mode, entry port, and customs handling. The final solution is deployed through an interactive chatbot interface, streamlining access to lead time estimates for import-export specialists. This integrated approach enables proactive lead time management, cost optimization, and greater operational efficiency within import processes, providing a strategic advantage in the industry.

Purpose The aim of this paper is to contribute to empirical research dealing with the measurement of green and sustainable supply chain management(SSCM) practices. The paper intends to empirically evaluate the practices maturity related to green supply chain management (GSCM) in one of the most strategic sectors in Saudi Arabia, namely, pharmaceutical sector. Design/methodology/approach Based on a research questionnaire, data were collected from 111 respondents in pharmaceutical companies. Data analysis has been conducted based on Statistical Package for the Social Sciences (SPSS) program to evaluate the extent to which pharmaceutical companies in Kingdom of Saudi Arabia (KSA) are mature regarding each sustainable SCM dimension. Findings The results reflect high adoption of green practices related to SCM by pharmaceutical companies operating in KSA that are highly meeting environmental requirements that represent one of the core objectives of KSA vision 2030. Research limitations/implications The study presents a platform based on which future studies can link the maturity of Sustainable SCM with the firm's performance. Practical implications This study provided professionals and managers in the pharmaceutical sector with in-depth insights regarding the maturity of their green practices related to SCM. This study also proposed a framework that could be by managers to continuously assess their Sustainable SCM practices. Originality/value This research intends to demonstrate to what extent SSCM in pharmaceutical sector are mature.