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Border States, founded in 1952 and with annual sales of approximately $4.02 billion, has been ranked the sixth largest electrical distributor in the U.S. by Electrical Wholesaling. It is 100% employee-owned, and has more than 130 branches in 31 states, employing 3,450 people. In handling more than 200,000 SKUs and an inventory value of more than $650 million across that network of operations, the company found it faced critical challenges in managing lead times.
Supply chain disruptions, volatile supplier performance and outdated forecasting models led to inefficiencies, increased costs, and customer service challenges. Traditional methods of lead time calculation often rely on simplistic historical averages that fail to capture real-time complexities. The consequences of inaccurate lead times include stockouts, excess inventory, missed revenue opportunities and inefficient capital allocation. Border States needed a new way to handle lead time predictions.
Border States partnered with supply chain optimization technology vendor GAINS to integrate AI-driven insights into their supply chain planning processes. GAINS developed the Lead Time Predictor Service, an AI-driven innovation designed to empower businesses to predict lead times with unprecedented accuracy. The idea is to enable companies to proactively manage supply chain risks, optimize inventory levels and improve service reliability, all while enhancing sustainability and reducing costs.
The implementation at Border States involved several critical steps, including:
- Data cleansing and model training. Historical supply chain data was structured and fed into machine learning models to establish predictive accuracy.
- AI model deployment. The solution was deployed across Border States’ procurement and inventory management systems.
- Continuous optimization. The model was fine-tuned based on ongoing supplier performance and real-time market shifts.
The GAINS’ Lead Time Predictor leveraged advanced machine learning (ML) models to analyze vast amounts of supply chain data, including supplier performance, order histories, transit times and external market variables. By moving beyond static historical averages, the system was able to dynamically predict lead times at a material/order level, as well as identifying emerging supply chain disruptions in real time. The software also provides actionable insights for strategic decision-making, and enhances supplier collaboration through improved forecasting accuracy.
Kory Jacobson, regional procurement director at Border States, points to another welcome aspect of great value – the company’s internal staff’s willingness to embrace the new system. “Adoption soared when teams saw predictions they could trust,” he says, adding that staff were able to override anything if they felt the need. The initial confidence threshold — the level at which adoption would be worthwhile — was set at 65%, but actual adoption now means more than 90% of Border States’ purchase orders are driven by GAINS predictions, offering a high degree of automation that significantly reduces the need for manual intervention. The system also delivered 97% material availability, ensuring seamless order fulfillment, and a 32% reduction in purchase orders, despite a 25% increase in locations. As an added bonus, the GAINS technology gave Border States a lower carbon footprint, because smarter procurement reduced the need for expedited shipping.
Amber Salley, VP of Industry Solutions at GAINS, touts the system as a game-changer for supply chain professionals looking to mitigate risk and enhance resilience. “Traditional ERP systems and standard forecasting models struggle to process complex, heterogeneous datasets,” she explains. The GAINS Lead Time Predictor handles this complexity, she says, distinguishing itself by offering 65% more accurate lead times and reducing lead time errors by 31%.
“We really turned a blind spot into a competitive advantage,” Border States’ Jacobson says. “This innovation in lead time calculations doesn’t just react to disruptions. It predicts these disruptions. It helps us plan for these disruptions. And it really helps us increase the resilience in our supply chain.”
Overall, both companies agree, the result was a paradigm shift in supply chain planning.
GAINS says the relevance to the supply chain industry in general is clear, because of multiple common benefits. In terms of revenue growth, improved demand planning reduces lost sales due to stockouts. Then come cost savings, because there are reduced carrying costs, fewer emergency shipments and optimized inventory levels. Further, the operational efficiency gained through automation frees up teams to focus on strategic initiatives rather than reactive firefighting. Lastly, but critically, there is a boost to supply chain sustainability. Smarter procurement decisions minimize waste and unnecessary transportation, reducing environmental impact.
“The GAINS Lead Time Predictor redefines how supply chains manage uncertainty,” says Salley. “By transforming lead time forecasting from a static metric into a dynamic, AI-powered insight, this innovation enables businesses to move beyond reactive supply chain management toward proactive, strategic decision-making.”
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