We discovered in Oracle's global research that consumers are not only hooked on deals, but they expect in-stock inventory, making accurate retail planning more important than ever. The research found that 71% of consumers think compelling promotions and offers are important to continue shopping at a retailer. Another 47% reported out-of-stock inventory is a deal-breaker, and 63% said they would try a new brand or go elsewhere instead of waiting for a restock.
In addition to using insight from consumer research, Oracle Retail's customer discussion groups are critical to solution development and evolution. Our retail community includes some of the world's largest retailers across all verticals – fashion, hardlines, specialty, grocery, etc.
Amazon is setting the convenience standard with two-day shipping for Prime members, two-hour shipping with Price Now in metro areas, unlimited music and video, and no-checkout-lines with AmazonGo. Walmart and Amazon are leading the shipping wars to drive shipping down to one-day or less. The good news for retailers is Walmart is demonstrating that its traditional brick and mortar infrastructure is giving them an advantage.
While Amazon continues to open fulfillment centers, 90 percent of Americans already live within 10 miles of a Walmart store. While the retail community is implementing strategies to expand store fulfillment, a key challenge is how to predict and incorporate fulfillment demand of digital channels into physical stores' inventory needs.
Distribution must shift from a demand-driven model to a fulfillment-driven one. Modern retailing requires a demand forecast for sales planning and a corresponding fulfillment forecast for inventory planning.
As a complement to analytical approaches to inventory optimization, retailers are also expanding their supply chain operations to support modern distribution strategies – particularly in population-dense metro areas. These modern supply chain strategies include:
Increasing localized distribution centers (DCs), using specific stores as localized DCs, and increasing replenishment frequency to intra-day. This strategy enables retailers to efficiently replenish small-format stores multiple times throughout the day to maximize fill rates with minimum space capacity.
Increasing localized fulfillment centers – dedicated locations or expanding store operations – enables retailers to fulfill orders efficiently while meeting customer shipping expectations.
Approaching inventory planning at a hyper-regional level rather than at a store level. In conjunction with more distribution and fulfillment centers and more frequent distribution, this enables retailers to focus on the right inventory levels for a particular region and place inventory where it is needed. This also minimizes unproductive inventory and increases markdown avoidance.
The roles within retail organizations have remained largely unchanged even though modern retail strategies demand an increased sense of responsiveness and a pivot to the customer in every retail process.
This introduces competing objectives within functions that are working in a silo (e.g., plan to maximize margin, replenishment to maximize fill rate, and warehouse operations to maximize truck efficiency), rather than enabling the retailer to coordinate efforts towards a single business strategy.
Merging roles into cross-functional disciplines represents a next practice trend where cross-functional teams span planning, supply chain, merchandising, and marketing functions. There is great power when all the teams work towards common objectives and leverage all available resources to achieve those objectives as a single unit.
Our customer, Best Buy shared in this presentation that their technology goals include making the customer the center of the plan, connecting the plan to eliminate costly variability, empowering their teams to drive business results, and driving results through data-driven technology solutions.
Using modern retail technology they plan to:
Develop a demand-driven lens of the supply chain and drive collaborative processes around balancing those demand signals with supply chain constraints.
Evolve supply chain centers of excellence with a focus on gaining visibility and transparency to current state process and performance through systemic and automated capabilities.
Improve forward-looking KPIs to be reviewed and integrated into not only reaction processes but planning cycles.
Collaborate with technical partners to develop a single source of supply chain data and KPIs and lay the foundation for a long term data strategy
Not all exceptions are equal, and some can be exceptionally impactful to retail supply chains. Disrupters include launch delays, product recalls, and weather. Delays in re-routing inventory have a large-scale impact on the ability to sell and fulfill.
One customer shared that although they had to close one of their regional DCs, they could re-route their distribution within hours and continued serving impacted stores with the help of flexible supply chain network capabilities supported by Oracle Retail's inventory planning solution. This contrasts days-long reactive processes that are still common across the retail industry today.
Service levels measure the demand that is fulfilled based on the desired journey of the customer (e.g., buy-in-store or buy-online-pickup-ins-store). In traditional approaches to inventory management, strategies are organized around service level targets where the demand is assumed as given.
As retailers expand their touchpoints with the customer via digital channels and devices, this introduces an opportunity to shape demand towards fulfillment experiences they can service. The next practice of inventory management can simultaneously shape supply and demand to achieve desired service levels.
Oracle Retail's solution approach for supporting next practice demand and inventory planning organizations includes:
Delivering a unified platform for strategic, assortment, demand, and inventory planning that enables end-users to collaborate in real-time towards consistent objectives throughout the process.
Augmenting inventory planning strategies with artificial intelligence that translates business objectives into operational decisions. Key examples include shaping demand towards sales objectives with promotion and markdown optimization and aligning replenishment policies to service level targets with inventory optimization.
Empowering store associates with operational decisions that are informed by machine learning and retail science. Key examples include initiating store orders informed by real-time visibility to the demand forecasts and inventory projections.