Ikhlef Jebbor

Lean production philosophy and sustainability approach have become a critical framework for efficiency improvement, waste reduction, and promoting sustainable manufacturing practices. In the age of artificial intelligence (AI), there is a synergy, which has now found new dimensions, data-driven decision-making, predictive analytics, and operational agility. AI technologies promise to transform industrial processes by converging lean production and sustainability principles, a synergy explored in this paper. AI APIs enable the use of AI to improve resource utilization, reduce environmental pressure, and maintain economic growth inherent to all business sectors while also fostering social accountability. In this study, a robust regression model is employed to study the role of AI in moderating the lean practices and sustainability outcomes relationship, using a sample of 528 manufacturing firms. The results show that the contribution of AI technologies to economic, ecological, and social sustainability is effectively multiplied by that of lean production. This research offers a framework to help practitioners and policymakers optimize production systems in line with Sustainable Development Goals. Finally, the study delivers actionable recommendations for navigating skill gaps and cybersecurity risks that were identified. In sum, this paper contributes to the rapidly emerging conversation by providing empirical evidence on AI’s moderating role in the lean–sustainability relationship and offering a strategic framework for practitioners.

This paper reveals profound obstacles to sustainable supply chain integration in Industry X.0 in the United Arab Emirates (UAE) by utilizing a hybrid Fuzzy Delphi-TOPSIS approach and enriching the viewpoints of 102 experts in oil/gas (45%), logistics (30%), government (15%), and academia (10%). The top obstacles are a lack of favorable leadership (Fuzzy Delphi Threshold (FDT), FDT = 0.82) and insufficiency of sustainability professionals (FDT = 0.82), with strategy prioritization training (Rank 1, Closeness Coefficient Index (cci) cci = 0.1255) and employee engagement (Rank 2, cci = 0.1499) being among the most important solutions as opposed to technological solutions. Most importantly, AI-related technologies had a low ranking of seventh place because of their lack of implementation, which proves that human capital enhancement is always prioritized before technological adaptation. The oil/gas industry values AI with respect to regulatory compliance commitments to emissions monitoring, whereas SMEs accentuate the problem of training because of the limited resources available to them, which also indicates the societal relevance of the concept of AI to social entrepreneurship and the blockchain-based transparency and access to green technologies. This study contributes (1) a decision-oriented framework bridging the traditional 2050 vision of the UAE and the realities it faces day to day, (2) empirical insights into the need for cultural principals within governance so as to prevent the so-called paperwork syndrome, and (3) a theoretical advancement that sees AI as an enhancer of human-centric methodologies. The conclusions provide policymakers with knowledge of the importance of the ability to contextualize investments in organizational culture prior to technology implementation in order to provide effective sustainability transitions.

Lean manufacturing (LM) aims to improve production efficiency by systematically reducing waste and fostering continuous improvement. This research analyzes how LM strategies are carried out, what is important for them to succeed and the benefits they bring to the manufacturing industry in Morocco’s north. Using a comprehensive questionnaire survey, the author shows that Kaizen and similar incremental improvement techniques matter and detailed their effect on top performance metrics, like how much a company produces and its cost efficiency. Using advanced statistical methods, the study investigated whether employee engagement, managerial support, and process adaptability act as mediators between lean practices and performance outcomes. The results confirm that adopting LM significantly enhances operational performance, emphasizing its central role in achieving manufacturing excellence. This paper illustrates the importance of LM as well as the practical advice to industries that wish to pursue sustainable competitive advantage by adopting lean transformation.

Economic, environmental, and social sustainability challenges are critical to managing sovereign credit risk in the MENA region. This paper proposes a framework for selecting comprehensive strategies using a Fuzzy Multi-Criteria Decision-Making (MCDM) approach. A hybrid Best-Worst Method (BWM) and Fuzzy TOPSIS model are applied to rank sustainability strategies related to sovereign credit risk, addressing the vagueness and uncertainty in stakeholder data. Ten strategies are evaluated across three sustainability dimensions: economic, environmental, and social. The findings highlight that Developing Human Capital and Regional Cooperation and Trade Integration are top priorities for economic sustainability, emphasizing skill development and trade relations. Public-private partnerships and renewable energy investments are most viable for environmental sustainability, leveraging private capital for large-scale projects. Social sustainability is best achieved through public-private partnerships and renewable energy investments, which create jobs and improve energy access, promoting socio-economic stability. Political stability and governance reforms are identified as enabling factors for sustainability initiatives. This research contributes to the systematic body of knowledge on sovereign risk management in developing economies, offering a consistent framework for decision-makers in the MENA region to prioritize sustainable growth and creditworthiness amid uncertainty.

Frequency measurement is crucial in electronic measurement, and digital frequency meters are essential for engineering. Their use in smart appliances increases demand for wider ranges and complex designs. This paper presents the design and implementation of a digital frequency meter using FPGA technology, in particular the Xilinx Spartan 7. The system incorporates automatic range commutation, providing frequency measurements from 0.01 Hertz to 9.99 Megahertz. The frequency is displayed on a three-digit display with two decimal positions, while the range is shown on eight LEDs. Measurement is based on counting signal cycles within a time frame defined by a basic time generator. When the frequency exceeds the counting limits, the system automatically changes scale. The development part of this paper is divided into two steps: first, a digital architecture is designed using VHDL software for the combinational and sequential logic functions, preceded by validation by simulation. In the second step, analogue conditioning circuits are developed, including filters for noise reduction and a comparator for signal shaping. The system is tested with a signal generator to validate the functionality of the FPGA-based frequency meter. Findings show that the FPGA implementation provides accurate frequency measurements with effective automatic range commutation.