By my recollection, the world of flexible fulfillment hit an inflection point about 15 years ago. It was at that point that putting the technology and operations in place to fulfill from an increasingly complex supply chain network embarked upon its next frontier.
It was no longer enough to route orders to a handful of DCs and drop ship vendors. It was time to fold the store fleet into the fulfillment network, dramatically increasingly both the network’s revenue generation potential and the sophistication required to do so effectively.
During those transition years, we watched more than a few retail CEOs fold their arms and declare: “My store associates will never spend time putting a shirt in a box.” In the end, though, the dramatic revenue lift and margin gains to be had were simply too powerful to ignore for anyone operating a store fleet.
There are myriad reasons that make running an effective and profitable store fulfillment program difficult. Store labor, inventory accuracy and the dreaded split shipment issue are each enough to kill the profitability of the program. For now, though, we’ll contain ourselves to discussing perhaps the most complex and impactful factor: how to position one’s inventory throughout the network in the age of the omni shopper.
Prior to the advent of the omni shopper, most retail businesses operated two fully siloed channels. When it came to inventory planning and optimization, stores were planned, allocated and/or replenished, and transacted with their customers entirely separately from the digital channel. The digital channel typically had one or more dedicated facilities, which ironically were often planned and managed like an individual additional brick-and-mortar location.
The easiest way to think about the disruptive effect of the omni shopper (and the consequent ship-from-store programs) is to picture a portion of the demand against that separate digital DC pulled forward into the supply chain, now manifesting itself as demand against a brick-and-mortar location instead.
The picture gets even blurrier when you try to define what exactly constitutes a digital transaction. Checking inventory in a local store but not placing an order? Doing extensive online research, and then opting for the speed of curbside pickup over ship to address?
In the end, all forms of omni consumer behavior create the need for operational excellence in two key areas: first, a much more sophisticated demand forecasting and inventory deployment strategy, and secondly, the ability to monitor the health of every unit of inventory in the network at or near real time, and to continuously adapt the decision making of the order fulfillment algorithm accordingly.
Let’s start with demand forecasting and inventory deployment. To understand why this process has required complete reinvention in the last decade, we need to understand the primary reasons most retailers have implemented a store fulfillment program. I like to sort these reasons into two categories – those driven by a desire to provide customer service and convenience, and those driven primarily by the retailer’s desire to monetize their owned inventory as fully and profitably as possible.
If we acknowledge that over the last decade consumer behavior has shifted to preferring some of its online fulfillment to be completed at a local store (classic BOPIS or curbside) and/or delivered same-day, it then logically follows that demand that historically would have been satisfied by a parcel carrier delivering inventory picked from a DC shelf must now be satisfied by store inventory and localized labor of some sort. The desire to ship even ground service from stores to deliver more orders within two business days is another customer experience-focused market driver.
The strong and growing desire for store pickup and store shipment (both same-day delivery and ground) now requires the demand forecasting algorithm to ‘shift’ some of the digital demand away from the DC and toward specific, individual stores in the fleet.
Between the consumer shifting their digital demand to a particular brick-and-mortar location, and the fulfillment algorithm in the order management system choosing the best store from which to ship an order, inventory now needs to shift away from the DC and into a store in order to capture the maximum number of sales, but also to minimize total costs to fulfill the order.
Having focused thus far solely on why solving for consumer experience necessitates that a portion of demand and supply must be shifted forward into the supply chain, into stores and away from DCs, let’s now look at factors within the retail operation that making shipping from store a necessity. The most important of these factors is the ability maximize gross margin across the network during the selling season.
In reality, no demand forecasting system can predict the future perfectly, and throughout the season there will always be units of inventory in sub-optimal locations. Connecting digital demand to store-level inventory is the single most effective way to deal with the so-called ‘distressed units’ issue.
Why? Because doing so turns the local supply/demand matching problem (i.e., store level inventory serving local demand) into a more global matching problem (i.e., digital demand-as-a-whole, sourced from the entirety of the network, including individual stores). Doing so affords not just the ability to sell marked-down items at a higher price now; more importantly, it significantly reduces the likelihood of the unit being marked down in the first place.
This last, critical element (markdown avoidance) is the least fully adopted technique across the retail landscape today. Doing so effectively requires fluid connectivity, one might even say unification, between inventory optimization algorithms and the order fulfillment algorithm that routes orders all day, every day.
Effective coordination between these two AI-based processes ensures that when it’s time to route the next order, the algorithm in the order management system goes beyond its table stakes considerations (inventory availability, parcel costs, labor availability and cost, time to deliver, etc.) and now factors the current health (or level of distress) of every potential fulfillable unit. In doing so, it can, for example, trade off a little more parcel cost or delivery time in exchange for avoiding a price markdown in the future, which effectively costs the retailer significantly more in absolute terms.
To summarize, inventory management in the omnichannel age requires unification of the upfront inventory planning process, the inventory health management process throughout the selling season and the fulfillment optimization process for digital demand. Doing so increases the ability of a retailer to capture as many sales requiring omni fulfillment as possible (BOPIS, same day, ship from store) while also lowering operating costs and maximizing gross margin on every transaction throughout the inventory’s lifecycle.
Brian Kinsella is Manhattan Associates’ SVP of Product Management. In this role, he is responsible for Manhattan’s product plans across all applications and leads Manhattan’s user experience product design organization. He has more than 20 years of experience in designing, building, selling and implementing supply chain applications.