[ INSIGHT ] ARTIFICIALINTELLIGENCE
by Amber Salley Vice President of Industry Solutions, GAINSystems asalley@gainsystems.com | 312-532-1575
How to Manage Lead Time 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 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.
trends and correlations that support a data-driven decision making process. It also provides a basis for proactive risk mitigation and collaboration across trading partners. Helps manage uncertainty and boost resiliency. Lead time prediction advances supply chain resilience by providing advanced warning of probable supply chain disruptions to provide a basis for risk-mitigating actions. A BETTER ALTERNATIVE AI brings more accuracy to lead time prediction. Using AI and machine learning creates models that can provide a better alternative to today’s default lead time calculation. AI/ML algorithms provide more accurate lead time predictions because they take into
The first step in effectively managing lead times begins with accurate, granular data from across the supply chain—supplier performance metrics, order histories, transit times and other external variables. Gathering data is just the beginning, however. From there, 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: Improves inventory management. Lead time prediction is primarily used to forecast lead time in inventory management and replenishment planning. Enables value chain coordination. An accurate prediction capability coupled with analytics can identify
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. Lead time values are frequently maintained in an ERP or other transactional system of record. Typically, there is some automated lead time calculation used but it is simplistic and based strictly on past orders and no forward-looking view. The result is incorrect lead times, which impacts supply chain planning and execution accuracy. In short, when variability in one area goes unmanaged, the entire system feels the strain. The evolution of machine learning in supply chains has resulted in better ways to manage lead times using more accurate, timely predictions.
account complex relationships and data patterns. This modeling provides more accurate prediction scenarios and more flexibility in a dynamic business environment. Leveraging advanced 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.
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28 Inbound Logistics • May 2025
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