Having the right product, in the right place, at the right time and the right price is one of the oldest and greatest challenges retailers and brands face. The logistical complexity of this problem is magnified when considering retailers must address this issue in an omnichannel environment where consumers expect the physical and digital worlds to be seamlessly integrated when they interact and ultimately purchase products from a given retailer or brand. 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, which came much later. In the current state, consumers have come to expect the purchase process to be personalized, convenient and quick. This expectation exacerbates lost sales since inventory is not properly allocated or pulled from the most optimal store location or fulfillment center.
Non-Linear Purchase Journey
The purchase journey for customers is no longer a linear process, as retailers have had to adapt to customer purchasing behavior for which there are a few modalities. For example, customers can:
- Purchase products directly in a local store
- Buy online and have products shipped from either a store or fulfillment center
- Buy online and pick up products in a local store
- Return products to either a store or fulfillment center.
All of these ‘convenient’ options place enormous strains 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 address the non-linear purchase journey, existing systems are not addressing the issues as effectively as they should when managing online orders. These systems are no match for customer expectations emphasizing delivery speed, which can negatively impact shipping costs. Additionally, a fast-turning product must be readily available to maximize sales and minimize stockouts. To meet consumer demand, retailers try to strategically overstock their stores and fulfillment centers to meet promised delivery times and consumer purchase demand for products. Even if retailers are not overstocking products, they have to consider the best location (i.e. fulfillment center or store) from which to ship a product. This need has to be met with proper physical and digital store assortments. In trying to fulfill these customer expectations, assortments suffer and are not as crisp as they should be. Money is left on the table because inventories are not being efficiently aligned with consumer demand. 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 product inventory is in the right location to fulfill demand from the most optimal location possible, as described above. This is why retailers need to carry excess inventory across the board. Additionally, when demand is diffused there is often a need to send split shipments to meet the delivery requirements expected by customers. However, this increases the shipping expenses of fulfilling a given order, which in turn reduces margins. We can play this out further and assume that the size, color or design was not as advertised and an exchange or return is generated. I am sure that everyone reading has experienced some variation of this scenario.
Optimization with Predictive Analytics
With today’s technology, there are systems that leverage predictive analytics to understand 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:
(i) Low Shipping Cost
(ii) High Average Weeks of Supply Impacted (AWSI)
(iii) On-time Delivery
(iv) Maximized Onesies Shipped
A ‘Low Shipping Cost’ objective can minimize split shipments while adjusting for the chosen shipping mode from a particular location, whereas ‘High Average Weeks of Supply Impacted (AWSI)’ can facilitate a selection 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 a product arrives when a customer expects an order to arrive. It all sounds trivial, but is in fact quite important as customer reviews and word-of-mouth have an impact on a business. Lastly, by ‘Maximizing Onesies Shipped’ retailers make inventory even more productive – if a returned product is not normally stocked at that store, an order can be fulfilled using this product so that it does not become a markdown or sit in the back storage. This plays to the old retail maxim, ‘sell what you have.’ The business objectives can be combined with any pre-existing business constraints involving store locations, shipping time or the product itself.
There is Hope
Today, consumers have come to expect the purchase process to be personalized, convenient and quick. The non-linear purchase is now the norm and will only become more complicated. Although this seems like an operational nightmare and expense minefield, there is a tremendous opportunity here. Retailers can intelligently leverage their brick-and-mortar retail network with practical, predictive tools that solve many of the shortcomings of existing systems and increase revenue by making inventory productive. Where does your company stand?
About the Writer: Jose Chan is the vice president of business development at Celect, a cloud-based, predictive analytics SaaS platform helping retailers optimize their overall inventory portfolios in stores and across the supply chain. Additionally, Jose teaches retail at Parsons’ The New School for Design.
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