Tl;dr: This case study shows how Wells Lamont reduced forecasting time by 33%, improving decision confidence and enabling faster, data-driven planning, by using demand forecasting software to improve accuracy, efficiency, and decision-making.
For manufacturers and distributors, demand forecasting software is essential to improving accuracy and operational performance. Wells Lamont, a premier glove company founded in 1907 in Aberdeen, South Dakota, has long been recognized for delivering durable, innovative products across multiple industries. As demand grew, maintaining forecasting excellence while keeping operations efficient became a critical priority. To scale efficiently, Wells Lamont adopted forecasting process automation to streamline workflows and improve decision-making.
The leader of the planning team, Matt Crist, recognized that the traditional manual forecasting process was taking approximately 15 hours per week. Both time-consuming and limited in actionable insight. To continue scaling the business while making faster, data-driven decisions, he sought a solution that could streamline forecasting without compromising accuracy.
The Challenge
The lack of forecasting process automation made forecasting time-consuming and limited its strategic value, and the Wells Lamont’s planning team faced several key obstacles:
- Excessive time spent on weekly forecasts
- Limited visibility into emerging demand patterns
- Difficulty turning manual forecasts into actionable decisions
The company needed a way to reduce operational burden while strengthening confidence in demand projections.
A Data-Driven Forecasting Approach with Intuendi
To address these challenges, Matt led the initiative to integrate Intuendi’s AI-powered demand forecasting software into daily operations. By leveraging predictive analytics and real-time demand signals, the team could:
- Reduce weekly forecasting time by 33%, from 15 hours to 10 hours
- Improve accuracy and insight into SKU-level demand trends
- Make data-driven decisions more efficiently across the planning cycle
This approach helped improve forecast accuracy while reducing manual workload. It allowed Wells Lamont to see demand changes earlier and respond proactively rather than reactively.
The Results
The improvements demonstrated how forecasting process automation can enhance both efficiency and decision confidence:
- Forecasting time reduced by 33%
- Enhanced confidence in SKU-level projections
- Freed team capacity for strategic planning and growth initiatives
By streamlining forecasting, Wells Lamont turned a labor-intensive process into a more strategic, insight-driven function.
The Advantage Gained
Through forward-thinking leadership and the strategic adoption of Intuendi demand forecasting software, Wells Lamont strengthened its operational resilience. The planning team now spends less time on routine tasks and more time making informed, proactive decisions, ensuring the company can scale efficiently while maintaining its reputation for reliability and quality.
