## 4 Findings Results
### Descriptive Findings
#### 4.1.7 Insights from the Most Globally Cited Documents  
Key insights from the results can be obtained from the analysis of the most globally cited documents on the impact and trends in the field of demand forecasting using AI and ML techniques see Figure 9 and Table 3. [PERSON] (2018) is the most cited paper, with 117 citations, demonstrating its strong influence and consistent interest within the research community due to a high total citation (TC) per year of 23.40 and a normalized TC of 3.60. Although published more recently, [PERSON] (2018) already has 34 citations, with a maximum normalized TC of 17.38, indicating a potentially emerging impactful area. Papers such as those by [PERSON] (2018) and [PERSON] (2018) show extremely high TC per year rates of 10.25 and 9.50, respectively; therefore, their influence is rapidly growing within the community. Meanwhile, [PERSON] (2018) presents a strong citation rate, with a TC per year of 14.20, highlighting its current relevance in discussions. Figure 8: Different countries with high publication rates. The heavy citation of papers, such as [PERSON] [29], in a very short period may serve as an indicator of sources that are likely to be important for those interested in fast-moving and emerging topics. The more specialized interest in clean logistics and sustainable supply chains is reflected in [PERSON] [30], with a normalized TC of 4.64. On the other hand, [PERSON] [31] has an average TC per year of 4.11 with 37 citations, suggesting more enduring research that remains relevant for influencing the field over time. For those aiming to research studies that will have a significant impact, papers with a high normalized TC, such as [PERSON] and [PERSON], should be prioritized, as they are highly likely to introduce new methodologies or insights. For a more historical perspective or foundational knowledge, [PERSON] could significantly contribute to understanding long-standing theories or practices. Some recommendations for future research include focusing on high-impact papers like [PERSON] and [PERSON], as they have contributed massively, monitoring emerging trends observed in recent high-citation papers, balancing these with long-term impactful studies such as [PERSON], and covering a diversity of journals to tap into the interdisciplinarity of the research area. Regarding research goals and methods, papers like [PERSON] integrated different ML models, such as random forest, SVM, and neural networks, to enhance the accuracy of demand forecasting using large-scale retail datasets. [PERSON] [28] formed hybrid models that combine regression and neural networks to decrease forecasting errors related to data from industrial production and sales. [PERSON] et al. explored deep learning models, including CNN and LSTM, in detail for application to optimizing demand forecasting in operations management. Other notable papers include [PERSON], [PERSON], and [PERSON], who worked on process optimization within supply chains using AI-driven models, showing improved efficiency in inventory management. [PERSON], [PERSON], and [PERSON] (2016) compared traditional versus ML-based forecasting methods, highlighting the competitiveness of ML models in terms of accuracy and scalability. [PERSON] [32] employed ensemble learning methods in decision-support systems, demonstrating improved robustness and accuracy in supply chain datasets. [PERSON] presented several state-of-the-art DL models like transformers and demonstrated their applicability to dynamic supply chains, achieving high accuracy for real-time applications despite implementation complexity and computational costs. [PERSON] [33] analyzed data-driven demand forecasting using clustering and decision trees to improve the management of large datasets. [PERSON] [34] assessed the impact of AI on demand forecasting in production economies and highlighted timely and accurate predictions across various industries.