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Investments in Industry 4.0 are making a significant impact on various sectors by integrating advanced digital technologies. The chances of financial losses for organisations' investments due to new technologies are high, and there is a low possibility of recovering the investments. Reduced spending on long-term costs by organisations due to low investments and consumer demand for durable goods has resulted in sluggish global trade in advanced and emerging markets. Challenges have arisen for companies in securing loans for investments due to past economic slowdowns, resulting in increased corporate borrowing expenses. The literature acknowledges Supply Chain Finance (SCF) as a vital approach to optimising efficiency. Industry leaders are turning to SCF as high costs persist during the transformation process for manufacturers. SCF can unlock substantial working capital by improving cash flow, which can be used to back essential business initiatives. This evolving environment presents opportunities and requires a deeper investigation of possibilities and challenges in advancing SCF to support Industry 4.0 efforts. Given this, the Special Issue released in the International Journal of Production Research pushes forward this crucial research focus. Nine articles accepted in this Special Issue used different methodologies, theories, and contexts to address research needs and offer valuable insights on supply chain finance and industry 4.0 investment challenges. This Editorial summarises these studies, emphasising possibilities for future research pathways. [ABSTRACT FROM AUTHOR]

Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry. [ABSTRACT FROM AUTHOR]

In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]