## 5 Discussion
### Addressing the Research Gaps and Future Directions
#### 5.4.2 Challenges and Future Directions  
Machine learning (ML) techniques and deep learning (DL) have emerged as some of the most pervasive tools in demand forecasting across various industries. However, their use presents several challenges. Firstly, handling time series data is a significant issue because traditional machine learning algorithms, such as random forest regressor and support vector regressor, often fail to capture complex temporal dependencies within the data. This limitation has driven the development of novel methodologies, including the integration of K-means clustering, LASSO regression, and LSTM deep learning models, to more effectively address the complexities of demand forecasting.  
Moreover, many sources of demand forecasting data are unreliable, either due to a lack of historical data, intrinsic biases, or both, which greatly hinders their adoption in business scenarios [37]. Despite these challenges, the potential of combining machine learning and deep learning techniques to enhance the accuracy of demand forecasting is promising. This potential is further realized when these techniques are synergistically integrated with complementary methods, such as time series analysis, data transformation, imputation of missing data, and feature extraction and selection, all of which leverage advances in technology and data processing [25].  
Expanding from the microcosm of machine learning applications to the broader landscape of supply chains, numerous challenges impede the seamless adoption and effective implementation of these technologies. Chief among these challenges is the pivotal role of data quality and accessibility, with issues related to real-time data quality posing significant obstacles to the success of machine learning models [36; 38]. Integrating machine learning and deep learning solutions into existing supply chain frameworks is a complex endeavor, often complicated by interoperability issues and the challenge of seamless integration [37].  
Furthermore, the interpretability of complex machine learning and deep learning models remains a persistent challenge, especially in industries governed by stringent regulatory compliance, where it is crucial to explain the rationale behind predictions [39]. Additionally, implementing machine learning and deep learning solutions can be costly, posing a significant barrier for smaller enterprises that must balance the anticipated benefits against the financial investment required [40].  
The dynamic nature of supply chains, which are prone to unexpected shocks and disruptions, adds further complexity. This requires continuous improvements and adjustments to machine learning models in response to rapidly evolving situations and sudden events. Looking forward, some emerging trends pose additional challenges for machine learning and deep learning in demand forecasting for supply chains. Explainable AI (XAI) is becoming increasingly important to address the challenge of interpretability by emphasizing transparency in decision-making processes alongside model accuracy [41; 42]. Additionally, integrating machine learning and deep learning models with edge computing technologies is crucial for real-time decision-making. Future challenges will continue to revolve around optimizing efficiency and scalability [43].  
This underscores the importance of cross-sector collaboration among academia, industry, and policymakers, necessitating interdisciplinary partnerships to bridge the gap between theoretical innovation and practical, scalable implementation. Moreover, future research needs to focus on sharpening ethical scrutiny regarding how automated decision-making systems function, striving for greater accuracy and resilience in forecasting.