This systematic literature review investigates the recent applications of artificial intelligence (AI) in supply chain management (SCM), particularly in the domains of resilience, process optimization, sustainability, and implementation challenges. The study is motivated by gaps identified in previous reviews, which often exclude literature published after 2020 and lack an integrated analysis of AI’s contributions across multiple supply chain phases. The review aims to provide an updated synthesis of AI technologies—such as machine learning, deep learning, and generative AI—and their practical implementation between 2021 and 2024. Following the PRISMA framework, a rigorous methodology was applied using the Scopus database, complemented by bibliometric and content analyses. A total of 66 studies were selected based on predefined inclusion criteria and evaluated for methodological quality and thematic relevance. The findings reveal a diverse classification of AI applications across strategic and operational SCM phases and highlight emerging techniques like explainable AI, neurosymbolic systems, and federated learning. The review also identifies persistent barriers such as data governance, ethical concerns, and scalability. Future research should focus on hybrid AI–human collaboration, transparency through explainable models, and integration with technologies such as IoT and blockchain. This review contributes to the literature by offering a structured synthesis of AI’s transformative impact on SCM and by outlining key research directions to guide future investigations and managerial practice.

This study explores the application of the BPM lifecycle to optimize the market analysis process within the market intelligence department of a major energy company. The semi-structured, virtual nature of the process necessitated careful adaptation of BPM methodology, starting with process discovery through data collection, modeling, and validation. Qualitative analysis, including value-added and root-cause analysis, revealed inefficiencies. The redesign strategy focused on selective automation using Python 3.10 scripts and Power BI dashboards, incorporating techniques such as linear programming and forecasting to improve process efficiency and quality while maintaining flexibility. Post-implementation, monitoring through a questionnaire showed positive results, though ongoing interviews were recommended for sustained performance evaluation. This study highlights the value of BPM methodology in enhancing decision-critical processes and offers a model for adaptable, value-driven process improvements in complex organizational environments.

This study explores the application of the BPM lifecycle to optimize the Market Analysis process within the Market Intelligence department of a major energy company. The semi-structured, virtual nature of the process necessitated careful adaptation of BPM methodology, starting with process discovery through data collection, modeling, and validation. Qualitative analysis, including value-added and root-cause analysis, revealed inefficiencies. The redesign strategy focused on selective automation using Python scripts and Power BI dashboards, incorporating techniques such as linear programming and forecasting to improve process efficiency and quality while maintaining flexibility. Post-implementation, monitoring through a questionnaire showed positive results, though ongoing interviews were recommended for sustained performance evaluation. This study highlights the value of BPM methodology in enhancing decision-critical processes and offers a model for adaptable, value-driven process improvements in complex organizational environments.

Artificial-intelligence-enabled agri-food entrepreneurship is the creation of new ventures and the entrepreneurial renewal of existing firms, whose value propositions and business models are built around learning-based computational systems applied across the food value chain, from primary production and processing to distribution, consumption, and the recovery of losses and by-products. The core enabling technologies are machine learning and computer vision, generative artificial intelligence (AI) and large language models, and AI-enabled robotics and predictive analytics, deployed with complementary digital infrastructure such as the Internet of Things, big data, cloud and edge computing, blockchain, and digital twins. The concept is broader than technology adoption because its subject is the entrepreneur: how opportunities are recognised or created, how business models convert them into revenue, and why so few technically capable solutions become durable businesses. This entry makes four contributions: It specifies a mechanism—because AI lowers the cost of prediction but not of judgement, opportunities concentrate in decisions that are prediction-intensive, recurrent, and attached to an outcome that the customer already measures. It provides a taxonomy of business-model archetypes that separates value creation from value capture. It establishes the boundary conditions under which digital-entrepreneurship theory transfers to food systems—seasonality, fixed adoption costs, contested data governance, and liability. Finally, it converts the synthesis into seven testable propositions and an evidence calibration separating controlled benchmarks, field pilots, and commercial deployment.