Though e-commerce has been growing for years, store closures and quarantine guidelines during the COVID-19 pandemic dramatically accelerated consumer adoption of online shopping. Traditional retailers who quickly stood up online channels to meet demand now face a new challenge: making online channels profitable rather than losing margin on each sale due to inefficient, hastily developed processes.
Add to that pressure the emergence of a new disruptor in grocery and convenience retail: the rise of “quick commerce.” Quick commerce retailers like Delivery Hero up the ante, promising home delivery within an hour of order placement—with some companies promising delivery times as short as 10 minutes!
As consumer expectations for convenience continue to rise, all retailers—whether omnichannel, e-commerce, or quick commerce—must take action. Retailers are only beginning to explore utilizing networks of dark stores across densely populated areas to improve online fulfillment, but retailers need extreme supply chain efficiency to deliver on promises of convenience while achieving profitability.
AI-Driven Forecasting and Replenishment Drive Dark Store Availability
As retailers face rising expectations, they still struggle with older, more familiar challenges—for example, new product introductions and mixed assortments of both fast and slow movers. Fortunately, they can easily overcome these challenges with AI-driven technology, even when the pressure is turned up.
In today’s competitive environment, AI has become a foundational requirement to automate and improve forecast accuracy, improving dark store availability while eliminating hours of manual planning each week.
A solution using machine learning algorithms can help retailers model the complexities of demand variation, whether from planned promotions, weekday variation, seasonality, or localized demand-influencing factors such as events and weather forecasts. More importantly, it can do this automatically, processing more retail data than any team of human planners ever could. In today’s competitive environment, AI has become a foundational requirement to automate and improve forecast accuracy, improving dark store availability while eliminating hours of manual planning each week.
An advanced replenishment system can leverage that forecast to balance the risk of stock-outs with the risk of waste for every order based on inventory and demand levels—a capability that becomes extremely important when dealing with fresh products. While high availability is a crucial target for any retailer, it’s especially important for online retailers for whom substitutions can be quite challenging.
Space Optimization Improves Dark Store Picking Efficiency
While shoppers don’t enter dark stores, good space planning is still critical to meeting the value proposition of extreme speed. Because space is at a premium in small-format dark stores, inventory and space planning teams must work hand-in-hand to achieve the highest possible availability and the greatest possible speed—especially for quick commerce retailers whose goal is to pick orders within minutes of placement.
Dark store planogramming approaches can vary significantly from retailer to retailer depending on their picking strategy. Some retailers shelve items within a category together in a traditional, store-like planogram familiar to pickers. Others choose not to follow traditional planograms, instead separating similar items to decrease the likelihood of fast-moving pickers grabbing the wrong product for fulfillment.
Regardless of planogram approach, it’s a good practice to reserve an area for your most-picked items at the front of the store to accelerate picking speed for your highest-demand products. These “most-picked” items may change throughout the year based on seasonal trends or major holiday events like Halloween or Christmas.
The right planning solution can automate dark store-specific shelf and inventory optimization based on localized demand forecasts.
The right planning solution can automate dark store-specific shelf and inventory optimization based on localized demand forecasts. A unified system that draws demand data into planogram creation can optimize not only the shelf space per product, ensuring efficient inbound goods flow, but also localized product positioning, ensuring high-demand items for each store are easy to pick efficiently.
Furthermore, optimizing shelf space allotments to match localized replenishment plans for each product enables direct-to-shelf replenishment. And when replenishment plans are informed by local space plans, retailers can fill center store shelves up with fewer inbound deliveries. As a result, retailers see lower goods handling costs and less risk of stock-outs from items waiting to be shelved.
Anyone working in grocery and convenience today—whether quick commerce, e-commerce, or omnichannel retail—needs a best-in-class retail planning solution if they hope to compete for their shoppers’ online business. Accurate, granular demand forecasting is, of course, the foundation for high availability, but that data must inform both replenishment and space planning to fully deliver on the promise of fast delivery that will ensure repeat business from happy customers.
