What if you could travel back in time?
Back to the exact moment you determined which inventory needed to be allocated to each store for the 2017 holiday season.
The only difference this time around is you’d know exactly what sold at each location because your ‘future self’ would know every single purchase made. Then, you'd be able to make the best use of inventory by knowing exactly where each product was going to sell — prior to the hectic holiday season plagued by unpredictable spikes in demand.
Why, wouldn’t that be nice?
Unfortunately, the ability to travel back in time just isn’t an option. We’re stuck making do with historical sales performance, trends, or manual-based analyses to distribute inventory across stores.
To put it plainly: we’re placing huge bets on our inventory.
Since time travel isn’t a thing (*sigh*), retailers must infuse advanced analytics into their allocation processes to improve inventory bets across each store location. Here are three reasons why:
Deliver Allocations to Reflect Customer Preference
While it’s not nearly as alluring as time travel, the ability to understand customer preference across stores is pretty darn appealing to retailers.
An understanding of customer preference comes from a solid understanding of future demand—which is where predictive technologies have proven successful across retail today. From a merchandise allocator’s perspective, this means having a highly accurate picture of precisely where units of inventory will do best.
The average retailer already understands what their customers bought – you have all the transaction data to tell you that. However, the uniqueness around advanced analytics lies in its ability to layer in all other types of contextual data (i.e., SKU, location, browse, etc.) to fill in the gaps of what could have sold in a given assortment, if a customer were given the choice.
This forward-looking view of demand helps inform retailers of an individual customer’s propensity to buy products in a given assortment at a given location, so they can allocate the right product at the right place.
Optimize Allocation Decisions at Scale
In a perfect world, knowledge of customer preference and future demand at the store level would be enough to inform any sort of merchandising decision.
However, there’s also the challenge of balancing inventory decisions around existing constraints, including:
- Existing inventory in stores
- Constraints on min/max style-colors
- Constraints on min/max presentation
That being said, the scale at which retailers operate make it incredibly difficult to deliver the optimal allocation decision.
Because they have to consider thousands of purchase orders (PO’s) coming in across hundreds of store locations AND account for all the added constraints associated with each store.
Doing this manually is out of the question – it’s just not feasible.
Many rely on traditional allocation tools, which approach PO’s sequentially as each order comes in. This is the status quo. However, with advanced analytics, there’s an opportunity for high-scale optimization to provide retailers with the ability to take a joint approach to allocation (as opposed to sequential).
This means considering multiple PO’s as a single pool of inventory to allocate simultaneously across stores.
The result? A joint allocation approach that yields a better overall solution than the typical process of sequential PO optimization in meeting demand needs at the product-store level in the face of constraints.
When Every Variable Matters
It’s a very precarious time for many in retail.
The market is consumed by relentless competition, unpredictable demand, disruptive technologies and business models. As a merchant, every inventory decision you make has an impact on your bottom line.
Where there is room for improvement presents an opportunity.
Especially at a time when every variable matters. Improvements on how to best distribute inventory across each store are one area where merchants can make better decisions to impact customer experience, sell-through, and overall margins.
Get allocation right by considering how to intelligently leverage your data to help place the right product in the right place at the right time.