Agentic AI: Revolutionizing Supply Chain Planning

The transformative potential of Agentic AI has captured global attention, marking a significant milestone in technological advancement. To fully grasp its impact, it's crucial to examine the logical progression of its development.

We are breaking down Agentic AI’s evolution for supply chain planning use cases into three main buckets:

1. Enhancing and sustaining data quality
2. Model explainability
3. Autonomous capabilities / Planner co-pilot

Enhancing and sustaining data quality

Going with the first principle, data cleansing and completeness is one of agentic AI's most promising and foundational use cases. Without reasonable data, the subsequent steps are sub-optimal.

Data cleansing

Agentic AI can autonomously clean and prepare input data. Agents can detect and fix inconsistencies, errors, and outliers in sales, price, and inventory data.

• Format standardization: Ensure consistent data formats across different sources and systems.
• Deduplication: Identify and remove duplicate records to prevent data inflation and distortion.
• Missing data handling: Address missing values using statistical methods or machine learning algorithms to fill gaps.
• Data validation: Implement and enforce validation rules to ensure data integrity and consistency.

Anomaly detection

Agentic AI can identify unusual patterns or outliers in sales history, price, and inventory data through various techniques.

Statistical methods: Use probability distributions to model expected behaviour and flag significant deviations.

• Machine learning algorithms: Unsupervised learning techniques can detect patterns and anomalies without labelled data.
• Local Outlier Factor (LOF): This algorithm examines the local density of data points to identify outliers with lower density than their neighbours.
• K-Nearest Neighbors (kNN): Use kNN to classify data points into normal and abnormal ranges, working well for small and large datasets.

Model explainability

Agentic AI will play a crucial role in model explainability for demand forecasting and inventory optimization results. This is particularly important as AI systems become more complex and autonomous in their decision-making processes. Figure-1 is TrueGradient’s home page depicting an agent for this use-case.

Explainability in demand forecasting

Agents will improve the transparency of demand forecasting models by providing detailed breakdowns of factors influencing predictions, such as historical sales data, market trends, and external variables. It will generate natural language explanations for forecast results and translate complex statistical data into understandable insights. It will help visualize decision trees or feature importance graphs to illustrate how different variables contribute to the final forecast.

Inventory optimization explainability

Agents will enhance explainability for inventory optimization by offering clear rationales for suggested stock levels, considering lead times, demand variability, and storage costs. It will demonstrate the impact of various scenarios on inventory decisions through interactive simulations. It will provide audit trails of decision-making processes, allowing stakeholders to trace the logic behind specific optimization recommendations.

Note: The non-deterministic nature of AI systems can make it difficult to provide simple explanations for decisions. We will discuss the mitigation approach in our next article.

Autonomous capabilities / Planner co-pilot

Agentic AI will become an assistant to the planner aka planner co-pilot.

Automated reordering

Agents will create and execute reorders autonomously based on system alerts and predefined thresholds. These agents will continuously monitor inventory levels in real-time and automatically place orders for new stock when levels drop below-specified points.

Intelligent inventory movement

Agents will analyze inventory levels across multiple locations and suggest optimal inventory movements to address stock imbalances. For example, if one store is experiencing an out-of-stock situation for a particular item, the agent can recommend transferring inventory from another location with excess stock. This proactive approach will help maintain consistent product availability across the network.

Dynamic pricing adjustments

Agents will be able to autonomously adjust pricing strategies in response to inventory levels and demand fluctuations. For instance, if certain items are overstocked, the agent might suggest temporary price reductions to accelerate sales and prevent excess inventory.

In our next article, we will go deeper into these use cases and discuss their practical challenges and mitigation approaches.

At TrueGradient, we are innovating to empower the supply chain planning community. If you want to learn more about LLM / Gen-AI / Agentic AI - feel free to contact us!