Facebook Pinterest Search Icon SourcingJournal_horiz Tumbler Twitter Shape photo-camera graph-trend Shape latest-news icon / user

Overcoming Inventory Allocation Obstacles in an Omnichannel World

Rivet's 2020 Denim Circularity report takes a deep dive into how the global denim industry is plotting its circular future amidst a worldwide pandemic.

Having the right product, in the right place, at the right time and the right price in retail, is one of the oldest and greatest challenges retailers and brands face. The logistical complexity of this problem is magnified when we consider that retailers must address this issue in an omnichannel environment where consumers expect the physical and digital worlds to be seamlessly integrated. Omnichannel interactions are extremely tough to achieve for most retailers.

Originally developed for physical brick-and-mortar, the current retail model isn’t intended for digital commerce. But consumers expect the purchasing process to be personalized, convenient and quick. Merchants can only fulfill these expectations if inventory is properly allocated or pulled from the most optimal store location or fulfillment center. The old methods for managing stock will leave the customer unsatisfied or prove prohibitively costly for the merchant.

Non-linear purchase journey

The purchase journey for customers is no longer a linear process. Today, customers can:

  1. Purchase products directly in a local store
  2. Buy online and have products shipped from either a store or fulfillment center
  3. Buy online and pick up products in a local store
  4. Return products to either a store or fulfillment center.

All of these “convenient” options place an enormous strain on logistics and inventories while clouding future demand and allocation forecasts. Currently, retailers are not addressing these pain points in the most efficient manner.

The current state

Although retailers have systems that help them address the non-linear purchase journey, they’re not as effective as they should be when managing online orders. These systems are no match for customer expectations for fast deliveries, which can negatively impact shipping costs.

Additionally, a fast-turning product has to be readily available to maximize sales and minimize stockouts. To meet demand, some retailers try to strategically overstock their stores and fulfillment centers.

For retailers that don’t overstock products, the best location (i.e. fulfillment center or store) from which to ship a product is critical. And this need has to be met with proper physical and digital store assortments. Often, there’s a misalignment between supply and demand, and this misalignment ultimately results in excess inventory, higher inventory carrying costs, unnecessary markdowns, diminished margins and lost sales.

The rules-driven approach

Today, most retailers experience uneven levels of consumer demand across their retail network. It’s hard to keep up with this diffused demand since Order Management Systems (OMS) are often rules-based and manually driven. When employing a purely rules-driven approach, it becomes very difficult to ensure that product inventory is in the right location to fulfill demand from the most optimal location possible. As a result, retailers end up carrying excess inventory across the board, which increases the need for markdowns.

Additionally, when demand is diffused there is often a need to send split shipments to facilitate quick deliveries to the consumer. However, this increases the shipping expenses of fulfilling a given order, which in turn reduces margins. But it doesn’t end there if the consumer decides to return or exchange the goods.

Predictive analytics approach

Today, a manual approach is no match for predictive analytics, which understands the complexities of customer product demand within a season, sub-season, location, by product—down to the SKU. With this capability, four key business objectives are balanced through machine learning to determine the optimal fulfillment center or store to ship from. In addition to increasing revenue and minimizing expenses, a retailer or brand can have a clear view of true demand across the physical and digital segments of the business. The four revenue-maximizing objectives that can be balanced are:

  1. Low shipping cost
  2. High average weeks of supply impacted (AWSI)
  3. On-time delivery
  4. Maximized onesies shipped

A low shipping cost objective minimizes split shipments while adjusting for the chosen shipping mode from a particular location, whereas high average weeks of supply impacted (AWSI) allows retailers to pull from a store location that will not be able to sell its product during the full-price selling season. This can be coupled with on-time delivery, which ensures that a product arrives in the timeframe specified by the customer, allowing for positive customer reviews and word-of-mouth marketing. Maximizing onesies shipped means retailers can make inventory even more productive, by fulfilling orders with returned items not normally stocked at a particular location. Using product in this way means it won’t be marked down or sit in storage. This plays to the old retail maxim “sell what you have.”

The business objectives can be combined with any pre-existing constraints involving store locations, shipping time or the product itself.

With online sales increasing and stores using their physical locations for pick up and deliveries, the non-linear purchase is the norm and will only become more complicated. Without the right tools, it could be an operational nightmare and expense minefield. But companies that adopt predictive analytics tools have a tremendous opportunity to win through efficiency.

 

José Chan, vice president of business development for Celect, a cloud-based, predictive analytics SaaS platform that helps retailers optimize their overall inventory portfolios in stores and across the supply chain, resulting in double-digit percentage revenue increases. This groundbreaking advance in machine learning and optimization allows retailers to understand how an individual customer shopping in store or online chooses from an assortment of products, revealing true demand. The technology builds on a fundamental advance in customer choice modeling called by MIT’s Computer Science and Artificial Intelligence Laboratory one of the 50 greatest innovations it has ever produced.

Related Articles

More from our brands

Access exclusive content Become a Member Today!