Harnessing Machine Learning to Transform Lead Time Management

Calculating lead times demands precision, preparation and solutions.

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Rethinking Lead Time

To say lead time — the interval between placing and receiving an order — can significantly impact and organizational performance is an understatement.

Not accounting for lead time variability can leave your organization adrift, scrambling and unable to meet demand. Lead time is often treated as a single metric. However, it’s a complex web of interdependent timelines that collectively define the efficiency and resilience of your supply chain. Each lead time represents a potential pinch point. When variability in any one area goes unmanaged, the entire system feels the strain.

Types of Operational Lead Times

Procurement lead time represents a direct connection to your supplier network — a critical node in your supply chain. Lead time variability here doesn’t just delay production; it triggers a domino effect, forcing last-minute adjustments that inflate costs and strain relationships. Similarly, order processing lead time directly impacts your customer experience. When inefficiencies in verification or credit checks delay fulfillment, customer trust is eroded, and downstream bottlenecks for inventory and logistics occur. Perhaps the most frustrating is decision-making lead time. How quickly an organization can gather data to make informed decisions often dictates whether a disruption is mitigated or magnified. Without speed and alignment in decision-making, even the best strategies falter.

  • Lack of Real-Time Data Access: Organizations often rely on outdated or incomplete data and are left reacting rather than proactively addressing issues.

  • Siloed Departments/Lack of Alignment: Logistics, sales and inventory management often operate independently, leading to communication gaps and misaligned priorities.

  • Poor Visibility into Supply Chain Risks: Without predictive analytics or scenario modeling, organizations may not fully understand the potential impacts of a disruption, leading to hesitancy or indecision.

  • Overreliance on Manual Processes/Insufficient Tools or Technology: Organizations that rely on manual workflows for data analysis are slower and error-prone. Without tools like machine learning or real-time dashboards, organizations lack the ability to quickly analyze large volumes of data and predict the impact of different scenarios.

Data: The foundation for effective lead time management

Without data, you're relying on assumptions and “gut instinct.” Without accurate forecasting, lead times can snowball into larger issues. Effectively managing lead times begins with accurate, granular data from across your supply chain — supplier performance metrics, order histories, transit times, and external variables. Gathering data, however, is just the beginning. You must then turn it into actionable intelligence to make the difference. This is where advanced tools like machine learning and predictive analytics can help to identify patterns, anticipate disruptions, and simulate scenarios to test potential responses.

Challenges in Lead Time Predictability

Border States

Regional Procurement Director Kory Jacobson was challenged with finding a way to improve lead time prediction for Border States, the sixth-largest electrical distributor in the U.S. They were uncomfortable with the ambiguity and uncertainty they saw around lead times.

"We saw lead times jump from three weeks to three years with some suppliers,” Jacobson said. ”That level of variability made it nearly impossible to plan effectively and consistently meet our customers’ expectations." 

Despite their expertise and experience, they struggled with three key issues:

  • Inconsistent Supplier Lead Times:  a 52x increase in lead times from some suppliers led to unreliable inventory planning.
  • Manual Processes: Traditional forecasting methods were reactive and labor-intensive; considering the number of data points needed, it was nearly impossible to gain meaningful insights manually.
  • Missed Opportunities: The company faced challenges optimizing inventory levels with inaccurate lead time predictions, leading to excess costs and unmet demand.

The Solution: ML-Driven Lead Time Prediction

To address its lead time prediction issues, Border States worked with long-time supply chain partner GAINS to develop and implement a machine-learning solution designed specifically for lead time prediction.

"This isn’t just a technical upgrade — it’s a fundamental shift in how we approach supply chain management," said Jacobson.

The Process:

  • Cloud-Based Deployment: Moving from on-premise software to a cloud-based solution laid the foundation for Border States to access advanced AI capabilities previously unavailable due to technological limitations. 

  • Machine Learning Integration: Border States then piloted the new machine learning-driven replenishment platform, which makes use of historical data, supplier performance metrics, and external variables to predict lead times with unprecedented accuracy.

  • Custom Solution Development: Border States worked to develop and refine a lead time prediction service tailored to their operational needs in tandem with their solution provider.

"Moving to a cloud-based platform wasn’t just about upgrading our technology — it opened the door for advanced AI capabilities that weren’t accessible to us before."

The result was a solution capable of predicting lead times 31% more accurately and simulating potential scenarios to mitigate risks and reducing inventory cost by $21 million in six months.

Precision Through Complexity

Instead of relying on rudimentary averages, the lead time prediction solution processes intricate variables to deliver more precise lead time predictions.

"Having customized analytics has been a game-changer. Now we can see exactly where lead times are changing and, more importantly, what’s causing those changes. That kind of visibility allows us to be proactive instead of reactive."

Continual Improvement Through Machine Learning

The iterative nature of ML ensures that predictions aren't static. By learning which features most influence lead times, the system adapted to changes in supplier performance, logistics conditions, and material specifications.

"The ability to break materials into 90,000 decision points and predict lead times with 65% greater accuracy has been transformative.” 

Solving the Problem of Limited Historical Data

“One of the most significant challenges in lead time prediction is managing materials with little or no historical usage.” 

Traditional systems struggle, but ML’s lateral learning capabilities — leveraging data from similar materials — allow Border States to accurately predict lead times even for brand-new purchases. 

Collaboration and Visibility as Strategic Advantages

"Improved lead time accuracy doesn’t just benefit us — it strengthens our relationships with both suppliers and customers," Jacobson said. "When we can provide reliable data, trust grows, and collaboration becomes much more effective."

Border States' reputation as a dependable business partner gives them a competitive edge over competitors with outdated or inaccurate practices. 

How Border States Benefits

Adopting machine learning solutions has delivered measurable improvements for Border States: 

"The shift to machine learning has streamlined how we plan and operate, says Jacobson. From reducing stockouts to enhancing inventory precision, the benefits are far-reaching across our organization."

  • Cost Efficiency: Reduced excess inventory by $21 million and reduced the need for expedited shipments.

  • Improved Customer Service: Were able to fulfill customer orders consistently, leading to an 18% reduction in lost sales.

  • Operational Agility: The ability to dynamically simulate scenarios and adjust strategies has strengthened the company’s resilience to supply chain disruptions.

A Blueprint for the Future

Border States demonstrates the power of combining agility, integration and technology to address complex supply chain challenges. Using machine learning for lead time prediction, Border States has improved its operational efficiency and continues to set the standard for customer satisfaction in the industrial distribution industry.

As supply chain complexity grows, organizations must embrace new technologies and an adaptive mindset to stay competitive. Border States’ story offers a compelling roadmap for supply chain professionals looking to transform their operations and thrive in an ever-evolving market.

Jeff Metersky is vice president, solutions strategy at GAINSystems.

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