Artificial Intelligence–Driven Demand Forecasting Models for Enhancing Supply Chain Planning Accuracy in Saudi Industrial Sectors

1. Introduction

Demand forecasting plays a critical role in supply chain management, as it directly affects procurement decisions, production planning, inventory management, and logistics coordination. Forecasting inaccuracies can lead to excessive inventory holding costs, stockouts, production disruptions, and reduced customer service levels (Hyndman & Athanasopoulos, 2021). Historically, organizations relied on statistical forecasting techniques such as moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models. While effective in stable environments, these methods are limited in their ability to respond to demand volatility, external shocks, and structural market changes (Makridakis et al., 2018).

Artificial intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a powerful alternative to traditional forecasting approaches. AI-driven models are capable of learning complex, nonlinear relationships across multiple data dimensions, incorporating external variables, and adapting dynamically to changing environments (Carbonneau et al., 2008; Choi et al., 2018). Algorithms such as artificial neural networks (ANNs), long short-term memory (LSTM) networks, ensemble learning models, and transformer-based architectures have demonstrated superior forecasting accuracy compared to classical methods, particularly in volatile and data-rich contexts (Ivanov & Dolgui, 2020).

Saudi Arabia’s Vision 2030 places strong emphasis on digital transformation, advanced analytics, and AI adoption across industrial and logistics ecosystems. National initiatives such as the National Industrial Development and Logistics Program (NIDLP) explicitly promote the use of data-driven technologies to enhance supply chain efficiency, resilience, and competitiveness (Vision 2030, 2023). In this environment, AI-driven demand forecasting represents not only an operational improvement tool but also a strategic enabler for national industrial development and economic diversification.

2. Literature Review

2.1. Traditional Demand Forecasting Methods and Their Limitations

Traditional demand forecasting methods primarily rely on historical demand patterns to extrapolate future demand. Techniques such as simple and weighted moving averages, exponential smoothing, and ARIMA models assume linear relationships and stable demand structures over time (Makridakis et al., 2018). Although these approaches are computationally efficient and easy to interpret, they struggle in environments characterized by demand volatility, structural breaks, and frequent disruptions.

Moreover, traditional models typically exclude external explanatory variables such as macroeconomic indicators, customer behavior changes, promotional effects, or geopolitical events. As a result, their forecasting accuracy deteriorates significantly in complex and dynamic markets, limiting their usefulness for modern supply chain planning (Hyndman & Athanasopoulos, 2021).

2.2. Advancements Through Artificial Intelligence

AI-based forecasting methods overcome many limitations of traditional approaches by leveraging machine learning and deep learning algorithms capable of modeling nonlinear relationships and high-dimensional data (Carbonneau et al., 2008). Techniques such as random forests, support vector regression, and gradient boosting allow for flexible feature selection and interaction modeling without restrictive statistical assumptions.

Deep learning architectures—particularly recurrent neural networks (RNNs), LSTM networks, and gated recurrent units (GRUs)—are especially effective in capturing temporal dependencies, seasonality, and long-term trends in demand data (Hochreiter & Schmidhuber, 1997). Recent studies show that hybrid and multi-channel architectures integrating convolutional neural networks (CNNs) with LSTM and GRU layers outperform both traditional and standalone deep learning models in demand forecasting tasks (Lim et al., 2021).

2.3. AI in Global Supply Chain Demand Forecasting

Global research consistently demonstrates that AI-driven forecasting improves demand accuracy, supply chain responsiveness, and inventory performance. By integrating structured and unstructured data—such as historical sales, economic indicators, weather conditions, and social media signals—AI models provide more comprehensive and adaptive demand predictions (Waller & Fawcett, 2013; Choi et al., 2018).

AI-enabled forecasting also supports scenario analysis and predictive risk management, allowing organizations to simulate demand under different market conditions and proactively adjust supply chain plans. This capability has proven particularly valuable in mitigating disruptions during global crises such as pandemics and geopolitical instability (Ivanov, 2021).

2.4. AI Adoption in the Saudi Arabian Context

Empirical studies indicate a growing adoption of AI technologies in Saudi supply chain operations, driven by government-backed digital transformation initiatives and increased investment in analytics infrastructure (Alsharif et al., 2022). AI-based systems have been shown to improve demand visibility, inventory optimization, and decision-making quality across manufacturing and logistics sectors.

Additionally, the integration of AI with Internet of Things (IoT) technologies enhances real-time data collection and forecasting responsiveness in Saudi industrial supply chains. Market analyses project sustained growth in AI adoption across Saudi logistics and industrial sectors through 2030, reflecting alignment with Vision 2030 objectives and national digital strategies (Grand View Research, 2023).

3. Methodological Framework

3.1. Conceptual Model

An AI-driven demand forecasting system for industrial supply chains typically consists of five integrated layers:

• Data Ingestion Layer – Aggregates data from ERP systems, IoT sensors, market intelligence platforms, and external environmental sources.
• Data Preprocessing Layer – Cleans, normalizes, and transforms data while performing feature engineering and handling missing values.
• AI Forecasting Engine – Employs ML and DL models such as LSTM networks, ensemble methods, and transformer architectures.
• Decision Support Interface – Visualizes forecasts, confidence intervals, and scenario outcomes for planners and executives.
• Feedback and Learning Loop – Continuously retrains models using actual outcomes to improve accuracy over time.

3.2. Model Selection and Training

Model selection depends on forecasting horizons and data availability. Short-term forecasting benefits from LSTM-based architectures that capture recent demand fluctuations, while mid- and long-term forecasting requires models incorporating macroeconomic and market indicators (Ivanov & Dolgui, 2020). Model performance is evaluated using metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). Explainable AI techniques further enhance transparency and managerial trust in forecasting outputs (Lim et al., 2021).

4. Application in Saudi Industrial Sectors

4.1. Manufacturing

In Saudi manufacturing sectors such as petrochemicals, metals, and consumer goods, AI-driven forecasting improves raw material planning, production scheduling, and coordination with suppliers. These capabilities directly support NIDLP objectives related to localization, efficiency, and export competitiveness (Vision 2030, 2023).

4.2. Logistics and Distribution

AI forecasting enhances inventory balancing, demand allocation across distribution centers, and proactive stock reallocation in logistics networks. This is particularly critical in Saudi Arabia’s rapidly expanding e-commerce sector, where seasonal demand spikes and service-level expectations are high (Waller & Fawcett, 2013).

4.3. Energy and Petrochemicals

In energy and petrochemical supply chains, AI forecasting supports export planning, feedstock optimization, and alignment with volatile global demand cycles. The integration of real-time market and economic data significantly improves planning accuracy in these capital-intensive sectors (Ivanov, 2021).

5. Benefits of AI-Driven Demand Forecasting

AI-driven forecasting improves planning accuracy, reduces inventory costs, and enhances supply chain agility. Improved responsiveness enables organizations to anticipate market shifts, manage disruptions proactively, and support strategic decision-making across procurement, production, and distribution functions (Choi et al., 2018).

6. Implementation Challenges

Key challenges include data quality issues, system integration complexity, and shortages of skilled AI professionals. Overcoming these barriers requires investments in digital infrastructure, data governance frameworks, and workforce capability development (Bughin et al., 2017).

7. Policy and Strategic Recommendations

To accelerate AI adoption in Saudi industrial supply chains, policymakers should promote national data standards, incentivize AI research partnerships, and expand AI-focused workforce development programs. Establishing sector-specific AI innovation hubs can further accelerate practical deployment and knowledge transfer.

8. Conclusion

AI-driven demand forecasting offers a transformative pathway for enhancing supply chain planning accuracy in Saudi Arabia’s industrial sectors. By leveraging advanced machine learning and deep learning models, organizations can achieve higher forecasting precision, operational efficiency, and resilience. Alignment with Vision 2030 initiatives and sustained investment in data, infrastructure, and talent will be critical to realizing the full potential of AI-enabled supply chain forecasting.