An iconic international energy drink brand needed to improve warehouse layouts in its direct-to-store distribution operation. It partnered with NTT DATA’s supply chain operations experts to improve efficiency in its 50 U.S. distribution centers (DCs). Because the company had numerous DCs across the United States, any benefits gained at one facility would have the potential to be scaled and have a significant impact on the entire distribution network.
A reconfiguration effort was crucial considering that many of the brand’s distribution centers have small footprints (around 25,000 sq. ft.). These force strict limits on the number of pallet positions available to house inventory, making optimizing storage and load staging crucial.
The company worked with NTT DATA to determine how to optimize delivery truck loading cycle times through warehouse layout design improvements and establish a pilot program to ensure buy-in with local DC operations teams.
- Improves operator work ergonomics
- Provides cost-effective, flexible options to advanced material handling equipment
- Builds an item segmentation and inventory slotting tool for current and future locations
The project included an overall warehousing assessment to develop a set of recommendations and investment options to improve the ergometrics and other human factors around material handling and loading operations. The joint team began by conducting assessments of the current state of warehouse processes to identify areas to improve including product receiving, put-away, storage, staging and truck loading.
It was evident that improved warehouse slotting would prove beneficial to a redesign of its staging (pick face) layout, and the addition of basic material handling equipment (MHE).
The small footprint and limited pallet locations of the individual warehouses demanded the development of a client-specific item segmentation and slotting optimizer tool. This application would slot the correct SKUs in their proper locations, minimizing the need for operators to restock active pick faces from reserve inventory. The tool used historical demand data at the SKU-level for each of the DC locations.
Various tool levers such as pallet depth and service level allowed various scenarios to be modeled — each with a corresponding design layout and inventory slotting recommendation. The optimizer tool also highlighted opportunities to create mixed SKU pallets (as opposed to single SKU pallets) that contained a handful of low velocity SKUs, each meeting the needs of an individual retail customer.
Last, the NTT DATA team identified low-capital MHE that reduced the use of forklifts and pallet jacks while also improving the ergonomics of operator work.
Long-term, value-added benefits
- Optimized warehouse layouts reduced loading times by 13%
The redesigned layouts cut truck loading times by positioning the correct inventory in the right place through improved slotting. Commingling low-velocity SKUs freed up positions for more pallets of high-velocity SKUs. These efforts also reduced the number of truck loading interruptions due to the need to pick a pallet out of bulk reserve locations to replenish staging areas.
- Recommended cost-effective material handling solutions
Recommendations of basic material handling equipment (e.g., floor conveyances, pallet positioners) improved both loading cycle times, ergonomics and other human factors. Unlike more advanced material handling equipment, the options were both cost-effective and flexible – ensuring ease of deployment and redeployment at future locations.
- Built an item segmentation and inventory slotting tool for current and future locations
A mathematics-driven design based on SKU velocity enabled different warehouse locations with different demand profiles and SKU compositions to optimize inventory slotting based on their specific needs. The tool was used to optimize current locations and helped determine facility sizing requirements for new locations.
- Deploying recommendations across the network
The team developed a high-level pilot program with established timelines and key implementation tasks and applied the program across diverse regions and differently sized locations. The program quantifies benefits to a specific location and establishes buy-in with local operations teams.
About this case study
An international energy drink brand increases efficiency in its 50 U.S. distribution centers.