The Complexity of Demand and its Impact on Inventory

The Complexity of Demand and its Impact on Inventory

The importance of forecasting lift—and shift

“… the majority of retailers simply can’t forecast in any great detail as to how consumers wish to have their orders fulfilled.” –Tom Enright, Gartner

Modern consumer experiences are making accurate forecasting difficult.

For years buyers—and the advanced replenishment solutions they used—were focused on answering the question “what’s selling where.” Then it was their job to make sure the right amount of inventory was in place to maximize sales opportunities—while keeping carrying costs low.

Today, the changes to how inventory is being leveraged across the network requires a new approach to inventory optimization. “Where does demand originate?” and “Where should that demand have been fulfilled?” are now the questions of the day, introducing complexities that can’t be underestimated, nor accommodated by current solutions.

We also have a net new phenomenon at play. While the order was bought online, we need to know what happens to inventory when it’s fulfilled in the store. There needs to be a means to make sure there’s enough inventory in the stores to fulfill orders that originate via the website, as well as support foot traffic.

Amazon, Walmart and other leading retailers have acclimated consumers to omni fulfillment. Curbside pickup, same-day/next-day delivery, buy-online-return-in-store—shoppers now want to choose how, when and where they receive their goods.

What do retailers want? Their wish list is multi-faceted. They want to compete of course. They need to provide the convenience consumers demand. And they welcome the opportunity to leverage their inventory across the entire network. The challenge they face is aligning supply and demand in the current fulfillment environment using systems that weren’t designed to consider the new dimensions introduced by omni-fulfillment practices.

It’s not enough to forecast lift

Retailers are used to forecasting demand lift. Whether the bump in demand is caused by an event initiated by the retailer themselves or some external factors, today’s inventory solutions can account for this and ensure inventory levels are kept where they need to be to ensure service levels remain high.

The new complication presents itself when new fulfillment experiences are brought to bear that impact existing customers’ buying patterns. Often consumers will be compelled to take advantage of new offerings and experiences. Some portion of their “demand” may transfer from one experience to another. It’s significant and critical to the health of the retailer, but that’s not necessarily new demand for the business. We call this effect, demand shift.

Forecasting demand shift requires a new approach to inventory optimization. Omni Inventory Optimization (OIO) is a first-of-its-kind approach, advancing multi-echelon concepts to forecast lift and shift, and giving retailers full visibility into the impact of these corresponding dynamics.

Here’s an example: A retailer decides to offer buy-online-pickup-in-store. As a result, 25% of its existing customers opt for this fulfillment experience. Another 1,000 shoppers become new customers in response to the announcement of this option.

Suddenly, the DC has too much inventory and the stores have too little: Demand shift has occurred and DC inventory is needed in the stores. Inventory supply and demand are out of balance.

This scenario is difficult to model with current tools. Today’s multi-echelon-only solutions can’t forecast shift, and require manual intervention on the part of the buying team—a costly, slow and imprecise process. Alternatively, a solution designed to understand omni fulfillment (an Omni Inventory Optimization solution) forecasts both lift and shift, giving retailers full visibility into the impact resulting from the decision to introduce the new experience.

Forecasting lift alone results in an inventory surplus at some points across the network (i.e. the DC in the example above). Having the means to understand demand shift from one fulfillment experience to a new fulfillment experience keeps inventory levels optimized.

Credit for demand’s important too

The advent of omni fulfillment experiences changes the age-old question from “who gets credit for the sale?” to “who gets credit for demand?” When a transaction originates online and is fulfilled in the store the newest version of the question is not a simple one to answer. The efforts of the ecommerce team are certainly in play, including creation/maintenance of the website and the marketing driving consumers to it. The efforts at the store level also play a role, including the labor to retrieve the item, package it and ship or present to the customer. Giving both credit skews our perspective on demand and depends on the particulars of the situation (i.e. where the retailer would have preferred to fulfill the sale).

Here’s an example: A customer orders tennis shoes online for home delivery. The DC is out of stock. Store 10 has them, so a store employee packages the shoes and ships them to the customer.

Here’s another one: A customer orders tennis shoes online and wants to pick them up at the store. The store has them in stock, so a store employee retrieves the shoes and presents them to the customer.

These two examples are significantly different—one driven by available inventory levels and the other by customer preference—and would be measured accordingly. An Omni Inventory Optimization solution assures due credit for demand is assigned to the appropriate entity (per the retailer’s inventory strategy) in a way that optimizes available inventory according to forecasted demand in every situation.

Being able to predict demand shift (as well as lift) keeps inventory levels balanced in a complex omni environment, multi-echelon approaches to optimizing inventory have served retail well. Until now. New complexities in demand and fulfillment have necessitated new considerations that mandate inventory optimization techniques must evolve. In order to fully meet the needs of the digital consumer in ways that are profitable for the retailer, a new breed of solutions was required. Simply being great at multi-echelon is no longer enough.

Omni Inventory Optimization helps retailers:

  • Understand demand by fulfillment experience—forecasting both demand lift and demand shift to better align inventory.
  • Reduce inventory cannibalization across fulfillment experiences.
  • Optimize sales and increase service levels while reducing overall inventory across the enterprise.

For more information on how OIO enables retailers to forecast channel demand patterns and implement omni-fulfillment strategies to align inventory assets across the network and improve total return, download our whitepaper. Enter the Era of Omni Inventory Optimization: Why Multi-Echelon is No Longer Enough.

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