How to optimize your instock % in Walmart – Guidelines & Considerations

How to optimize your instock % in Walmart – Guidelines & Considerations

For Walmart’s brands & private label suppliers, driving a consistently above 97.5% instock % average is a MUST. This not only maximizes sales, but also maximizes the potential for Walmart’s replenishment system to accurately forecast & replenish to an item’s true store-level velocities, no matter how diverse those velocities are across Walmart’s ~ 4600 stores. Walmart has one of the most sophisticated replenishment models on the planet, but it is only as good as its data inputs, which can sometimes be off for a variety of reasons. Suppliers can ship on time & in full, but instock %’s can flounder in the mid to low 90%’s for a variety of reasons. Walmart has a replenishment & demand planning team that suppliers can interact with to troubleshoot, optimize & drive instock %. Upon interacting with this team, it is crucial that suppliers come to these folks correct with clear communication, actionable insights, and data-supported resolutions that are turn-key with all necessary forms accurately completed & attached to the communication. Below I’ll outline a few strategies to do so.

Amongst Walmart’s suppliers today, there are several order fulfillment models that drive a degree of variance in instock % resolution approach & strategy, but here are some over-arching basics:

Data Inputs

The general data inputs of Walmart's replenishment equation are Walmart’s demand forecast, Walmart’s Supply Plan, Walmart’s actual order volume, lead times (from supplier to store), order frequency, inventory on order/en route to Walmart’s DC(Distribution Center), inventory on hand at the DC (if the item warrants slotting in the DC), inventory in store, inventory cut by the supplier on outstanding PO’s, sales velocity at the store level, and order constraints like EOQ requirements & casepack configurations. These inputs all interact & evolve in real time to drive Walmart’s replenishment-ordering algorithm. Any inefficiencies that deflate or inflate any one of these inputs can cause a ripple effect, which can cause items’ instock %’s to flounder.

Forecast & Order Volume

If Walmart’s demand forecast for an item is lower than the item’s weekly POS QTY (unit sales at the register) for 3 consecutive weeks by >20%, a forecast adjustment that better matches, or even exceeds, the rate of sale is needed. This type of forecast setting adjustment is executable in Walmart’s system, if aligned on & implemented by Walmart's team, and will trigger higher/more accurate/instock %-resolving orders.

The above-mentioned general guideline for a forecast vs sales analysis & potential adjustment can be taken several layers deep, and utilized at each layer, as Walmart’s stores have store-level forecasts, and each of those store-level forecasts roll up to & dictate a Walmart DC-level forecast, and then each DC-level forecast rolls up to & dictates a master corporate-level forecast. Each DC services ~100 stores, and every product experiences demand variance amongst each 100-store pocket that feeds each DC. Each DC has an outlier of stores that are under or over-performing to the DC’s average. This also renders true with Walmart's DC’s, as some DC’s are over or under-performing to the corporate forecast average. Slower-turning stores can inflate Walmart’s inventory position, thus deflating DC & corporate-level order volume. This effectively hamstrings the higher-performing stores’ instock % position, as the bottleneck of inventory in slower-performing stores triggers a ripple effect up to the corporate ordering level that suppresses orders to that DC. Walmart absolutely incorporates this type of data into its replenishment algorithm, but every now and then needs a “nudge” via an approved forecast adjustment to help cater to this granular level of detail & accelerate instock % recovery vs letting Walmart’s system slowly attempt to correct itself.

This nudge, in other words, “feeds the beasts” by fueling an item’s top-performing stores to achieve their true sell-through potential by driving instock % via store-level forecast adjustments. This data-backed approach is a positive sum gain for Walmart’s shoppers, shareholders, and suppliers.

If the store, DC, and corporate-level forecasts are accurate, ensuring that Walmart’s actual orders reflect the forecast is the next step. If corporate, DC, or store-level order volume has obvious variance spikes from the corporate, DC, or store-level forecast, there may an opportunity to strategically inject inventory into the pipeline to right size it via an approved supplemental order, an “SSO.”

Utilizing 4-, 8-, & 13-week periods of historical sales data, check for a variance between POS QTY, the volume ordered, and the volume forecasted. The tells from this analysis will be obvious. Also, for a forward-looking analysis, check Walmart’s supply plan, which is a snapshot of next ~ 15 week order volume by week vs Walmart’s weekly demand forecast. You can also incorporate your historical weekly average POS QTY sales into this forward-looking analysis for additional perspective on how accurate the forward-looking data is. Also, for a truly wholistic assessment of pipeline health, be sure to incorporate each store and/or DC’s inventory on hand, inventory in transit, and inventory on order. Based on this analysis, there may be opportunities to inject orders/inventory into the store, DC, and corporate inventory pipeline via a Walmart-approved supplemental order or “SSO” in Walmart’s NOVA system.

Pack Configurations & EOQ settings

The more units in an inner & master pack, the more stores need to be out of stock before Walmart’s replenishment system triggers a replenishment order. This guideline also renders true for the larger the economic order quantity (EOQ) setting. Suppliers rightfully optimizing supply chain settings to be a steward of their margins can sometimes trigger a sales & instock %-hindering scenario with the same setting. Suppliers of slower-turning items need to ensure the pack configurations & EOQ settings they negotiate with Walmart do not cap performance, ultimately setting the items up for failure. If an instock % is floundering below 97.5%, check EOQ & pack configuration constraints to see if increased instock % & sales outweigh the efficiency savings, and the potential for being deleted based on performance.


There is So. Much. More. to managing replenishment & maximizing instock %, but the above analysis & actionable recommendations are a great starting point for suppliers looking to really dig in, improve, & drive instock % and sales via replenishment strategy.

Justin Brown

CEO @ remy biosciences | Business Development, Leadership

3y

Nice work Grant. Hard earned expertise on display.

Dana Marie Ponczek

Dedicated to Pet Health & Happiness | Product Development Leader | Brand Builder | Go-To-Market Strategist

3y

Excellent article Grant! Well done! Keep doing great things for our products!

Colton Dillard

Senior Merchant - Frozen Snacks and Appetizers at Walmart

3y

Great article/insights Grant Thomas, MBA. Driving 97.5%+ Instocks isn’t the flashy side of the business for companies/brands, but is the glue needed to have a successful business/partnership with Walmart.

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