---
# 70% improvement in order fulfillment with accurate demand forecasting
**URL:** https://www.sigmoid.com/case-studies/70-improvement-in-order-fulfillment-with-accurate-demand-forecasting/
Date: 2024-08-09
Author: Sigmoid
Post Type: page
Summary: 70% improvement in order fulfillment with accurate demand forecasting Optimized inventory and minimized stockouts through near real-time forecasts and faster order fulfillment....Read More...
---
# 70% improvement in order fulfillment with accurate demand forecasting
Optimized inventory and minimized stockouts through near real-time forecasts and faster order fulfillment.
[fluentform id="113"]
### Business Scenario
Sigmoid empowered the operations teams of a leading food services provider to optimize inventory and minimize stockouts through near [real-time forecasts and faster order fulfillment](/ebooks-whitepapers/achieving-real-time-visibility-and-agility-with-supply-chain-control-tower/). The existing model had a slow 1-day runtime to estimate sales across multiple locations and product combinations, hindering agility in adjusting orders based on changing demand. We helped them improve order lead times and operational efficiency by reducing the model runtime by 70%.
### Sigmoid Solution
Sigmoid optimized the order forecast model to increase its accuracy and efficiency by reducing the runtime to under 5 hours. We provisioned and configured the development and production environments on Snowflake from scratch and leveraged its capabilities for enhanced scalability and processing efficiency for forecast models. We also conducted a comprehensive cost analysis to estimate the expenses associated with running the model on the new Snowflake platform.
[Download case study](#ip-cs-Form)
### Business Impact
The solution enhanced operational control over the forecast model's runtime and enabling swift adjustments to order quantities in response to demand changes. This flexibility optimized inventory management, minimizing stockouts and maximizing revenue potential.
55%
reduction in order fulfillment time
Optimized 1.1M+
unique models per product-location
300+
Scaled to handle 300+ new delivery locations monthly
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