Transforming Lead Time Management With Machine Learning

The impact of lead time variability on supply chain management is a critical factor impacting operational efficiency, cost management and service delivery. Lead time is used in inventory management, production scheduling, order fulfillment and supplier management. It also involves decisions that determine optimal inventory levels, when to place orders and how much to order, stocking policy, available-to-premise calculations, supplier performance, and supplier selection.

While lead time uncertainty has always existed, it mattered less when orders were large, inventory was cheap, and single-sourcing was common. Today’s supply chain is far more complex and traditional methods often fall short in accurately forecasting lead times. Too often, it’s treated as a single metric when it’s actually a complex web of interdependent timelines that collectively define the efficiency and resilience of a supply chain. It plays a critical role in decisions that determine optimal inventory levels, when to place orders and how much to order, stocking policy, available-to-premise calculations, supplier performance and supplier selection.

And while there is typically some automated lead time calculation used, it's simplistic and based strictly on past orders and no forward-looking view. The result is incorrect lead times which impacts accuracy on supply chain planning and execution. And incorrect inventory levels increase costs as well as reduce cash flow and the return on assets. In short, when variability in one area goes unmanaged, the entire system feels the strain.

A few key elements are important to ensure lead time prediction success.

Data is Foundational for Lead Time Prediction Management

The first step in effectively managing lead times begins with accurate, granular data from across the supply chain — e.g., supplier performance metrics, order histories, transit times and other external variables. However, gathering data is just the beginning. Then the data must be turned into actionable intelligence to make a significant difference. This is where machine learning and predictive analytics help to identify patterns, anticipate disruptions, and simulate scenarios to test potential responses.

This improved orchestration also results in:

Related story: When Supply Chain Decision Making Meets Volatility

• Better Inventory Management: Lead time prediction is primarily used to predict lead time in inventory management and replenishment planning.
• Enable Value Chain Coordination: An accurate prediction capability coupled with analytics can identify trends and correlations that support a data-driven decision-making process.
• Enhanced supplier relationships: It facilitates proactive communication and collaboration with suppliers to identify and inform suppliers of trends and potential supply disruptions.
• Better Customer Service: Improved fill rates, available-to-promise (ATP) accuracy for customers, and identification of supply disruptions and their impact on ATP commitment dates will positively impact customer relations.

Manage Uncertainty and Boost Resiliency

Lead time prediction provides advanced warning of possible supply chain disruptions and a basis for a risk mitigation strategy that identifies common attributes that cause changes in lead times.

Agentic AI Brings More Accuracy to Lead Time Prediction

Using artificial intelligence and machine learning creates models that can provide a better alternative to today’s default lead time calculation. Because they take into account complex relationships and data patterns, agentic AI/ML algorithms provide more accurate lead time predictions. This modeling also enables more flexibility in a dynamic business environment.

Case in point: GAINS customer Border States implemented its Lead Time Predictor solution, integrating agentic AI-driven insights into its supply chain planning processes. Border States' inventory was bloated and it was looking for a more data-driven approach to help it see where and how to invest in inventory to help maximize efficiencies.

The steps included:

1. Data Cleansing and Model Training: Historical supply chain data was structured and fed into machine learning models to establish predictive accuracy.
2. AI Model Deployment: The solution was deployed across Border States’ procurement and inventory management systems.
3. Continuous Optimization: The model was fine-tuned based on ongoing supplier performance and real-time market shifts.

Once Border States implemented the lead time prediction solution, it had a wealth of data that helped it to invest in inventory in a much smarter way. With it, Border States saved $20 million. Other benefits realized include:

• 90 percent automation of purchase orders, reducing manual intervention;
• 97 percent material availability, ensuring seamless order fulfillment;
• 32 percent reduction in purchase orders despite a 25 percent increase in locations; and
• lower carbon footprint, with smarter procurement reducing expedited shipping needs.

LTP also helped Border State with the following:

• Revenue Growth: Improved demand planning reduces lost sales due to stockouts.
• Cost Savings: Reduced carrying costs, fewer emergency shipments, and optimized inventory levels.
• Operational Efficiency: Automation frees up teams to focus on strategic initiatives rather than reactive firefighting.
• Supply Chain Sustainability: Smarter procurement decisions minimize waste and unnecessary transportation, reducing environmental impact.

Leveraging advanced agentic AI and ML models is revolutionizing lead time predictions and turning uncertainties into strategic advantages. Embracing these tools will provide supply chains more flexibility and resilience in a quickly evolving industry.

Amber Salley, an award-winning supply chain professional, is the vice president of industry solutions at GAINS.