The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal requirements among stakeholders, and difficulties in tracing product provenance. This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and blockchain technologies. AI can automate halal compliance checks, identify potential irregularities in sourcing and composition, and facilitate risk management. The blockchain offers an ideal platform for tracking product provenance throughout the halal supply chain. This ensures trust and confidence among consumers by providing verifiable information on ingredient origin and production processes. This paper further strengthens the potential of this framework by presenting an illustrative example that utilises knowledge graphs, machine learning, and smart contracts. This exemplifies the potential application of the proposed framework in the context of halal pre-certification processes. By fostering transparency and streamlining compliance procedures, the proposed holistic framework, empowered by AI and the blockchain, can significantly enhance trust and confidence among stakeholders within the halal food industry.

Supply chain quality management practices are necessary to improve processes, meet consumer quality needs, and enhance supply chain quality management performance in sustainable food networks. Food supply chain quality management and associated practices are considerably studied in global food systems, less so for alternative food networks. There are salient differences between global food systems and alternative food networks, which may reflect on the applicable supply chain quality management practices in the food systems and networks. This paper reviews the literature on supply chain quality management practices, with a focus on alternative food networks. A systematic literature review methodology is adopted, resulting in the analysis of seventy-eight papers, identifying a total of one hundred and three supply chain quality management practices. The identified supply chain quality management practices were analysed in relation to their link to a) place, production, and producer and b) link to (bio)processes. Emerging themes from the analysis are discussed, and some areas of future research were put forward.

The application of statistical and artificial intelligence (AI) tools in building energy prediction (BEP) is considered one of the most effective advances towards improving energy efficiency. Thus, researchers are constantly propagating the energy prediction field with many prediction models using diverse statistical and AI tools. However, many of these tools are employed in unsuitable data conditions or for wrong situations. Using the Institute of Electrical and Electronics Engineers (IEEE) and Scopus databases, 92 journal articles on statistical and AI tools in BEP were systematically analysed. Furthermore, a quantitative bibliometric analysis was conducted to pinpoint the trends and examine knowledge gaps. This research reviews the performance of nine popular and promising statistical and AI tools with a primary focus on 7 pertinent criteria within the building energy research domain. Although it was concluded that no one tool is best in all criteria, a diagrammatic framework is provided to serve as a guide for appropriate tool selection in various situations. This study contributes to appropriate tool selection in the development of BEP models and their related drawbacks. Additionally, this study also evaluated the performance of the high-performing tools on a standard dataset.

Purpose: Food safety management systems (FSMSs) are implemented in food supply chains primarily to adhere to regulations as well as meet customer requirements. The literature contains examples of successful and failed implementation of FSMSs, including the influencing factors. This paper aims to explore the literature on the effectiveness factors of FSMSs to identify the literature findings, the emerging themes and suggestions for future research.Design/methodology/approach: A systematic literature review (SLR) is used to synthesise research on the factors influencing the effectiveness of FSMSs. The methodology consists of formulating the research questions, identifying, selecting and evaluating relevant studies, analysing, synthesising and presenting findings.Findings: The effectiveness factors of FSMSs identified from the literature are clustered into five categories, namely (1) FSMS culture; (2) FSMS inspection, verification and audit; (3) FSMS knowledge; (4) Legal regulations; and (5) Prerequisite programmes. Moreover, it was determined that the effectiveness factors featured in a variety of contexts.Research limitations/implications: SLR is an objective, rigorous and reliable approach to exploring the effectiveness factors of FSMSs. However, the results are contingent on research studies selected for review, which depends on the research criteria used, available and accessible research studies and the criteria for inclusion and exclusion of studies. This dependency introduces bias, an intrinsic limitation of the SLR approach.Originality/value: In this study, the effectiveness factors of FSMSs are identified utilising an SLR approach, integrating findings from previously reported research. The factors and resulting emerging themes identified in this study are beneficial, including in improving food safety management practices.