CPG Manufacturer Improves Fulfillment with IOP



Facing unprecedented demand, limited supply, and frustrated customers threatening OTIF penalties, a global consumer products company implements Intelligent Order Promising across 30 distribution centers to improve their available to promise, lower their OTIF fee risks, and increase their fill rates for key customers.

“The IOP [Intelligent Order Promising] solution is able to catch some shortages even I would have missed.”

Outbound Planner


During the COVID pandemic, a multi-billion dollar consumer goods company was required to react and adjust more quickly to inventory planning and order fulfillment to counteract skyrocketing consumer demand for food and consumable products.

Additionally, their major retail partners were increasing their On-Time In-Full (OTIF) requirements to retain consumer trust and loyalty, adding financial penalties as a significant complication to the allocation decision process.

And now, as the global economy continues, it appears that market volatility and consumer buying behavior will permanently redefine a segment that was once characterized as constant and stable.


After a pilot program at one warehouse, IOP was rolled out to 30 locations over the course of 6 months. The solution generated a 4-5% improvement in case fill rate for strategic retail customers and 10x ROI from increased revenue and reduced OTIF fines. Additionally, IOP brought a sense of relief and empowerment by enabling automation, responsiveness, standardization, and other factors that are listed below.

Order processing was automated, instead of a manual, high touch process

Segmentation of case fill was strategic, instead of first-come first-serve

Decision criteria were standardized and visible, replacing an inconsistent approach across the network

Fewer expedites were required due to up-front rules and a consistent process to manage exceptions

Fulfillment guidance was responsive to business dynamics, instead of reacting with a lag

Customer Service strategy was considered upfront in the analysis, instead of in a reactive mode


Download the case study

Download Now