The ROI of Optimization: A Case for the Board

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In a retailer’s perfect world, knowledge of what your consumer wants to buy (along with when and where they want to buy) would be instantaneous and inventory lead times would be nonexistent.

A retail utopia.

Unfortunately, this isn’t how it works. Instead, merchants are tasked with predicting enormous (sometimes erroneous) investments to: 

  • Determine the optimal assortment/mix of product to sell
  • Determine the optimal store location to allocate each product
  • Determine the optimal fulfillment path for online orders

As a merchandiser, you’re on a perpetual quest to provide the right product your consumer base demands.

So many variables come into play as you try to accomplish this.

Furthermore, it can be very difficult to make the best merchandising decision when the data required to make future decisions isn’t accessible or, best case, spread out across various systems.

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As a result, retail professionals globally are looking to invest in advanced analytics optimization technologies. Whether your organization is searching for ways to buy less inventory or improve fulfillment efficiency for online orders—the case for AI-driven inventory optimization is a no-brainer for many once they gain a clear understanding of its ROI.

How AI-Driven Inventory Optimization Impacts Your Bottom Line 

While the consumer shopping experience improved tremendously over the past decade (1-click checkout, 2-day delivery anyone?), it’s gotten really complicated for retailers. 

The reality is fluctuations in demand vary, making it extremely difficult for merchandisers to predict what’s going to sell and when. 

Solving for this uncertainty between supply and demand is a critical challenge every type of retailer struggles with, as it directly impacts quarterly performance and success. However, to combat this challenge, retailers are rolling out innovative technologies like advanced analytics to help them understand future demand and optimize inventory decisions across the board: 

“[S]pecialty retailers […] say they are working to get their merchandise mix right in order to avoid over-stacking sale racks. Much of that involves righting the supply chain, speeding up production and employing analytics to ensure that supply meets demand.” – Retail Dive 

In other words, by optimizing inventory with analytics, retailers can make more with less to make sure product is going to sell – at the right time, place, and price.

The ROI of optimizing your merchandising decisions with AI will ultimately increase full-price sell throughs (YES!), reduce markdowns (which will help improve margins), and reduce lost sales. 

Point blank: making the most of your inventory can save you a whole lot of $$ cash. 

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Benefits of Optimizing Your Merchandise Plan

Issues associated with a poorly executed merchandise mix are often tied to errors during the first step of the merchandising process – your plan. 

Merchandise planners (and buyers) traditionally relied on historical data (or any accessible data for that matter) and gut instinct to make a case for the “optimal” retail spend across departments, categories, or even vendors at any given store location. 

This approach leaves retailers prone to errors and bias (because we’re human 🤷🏻‍♀️), resulting in an unclear depiction of who our customers are and what they want. At the end of the day, this can leave you with $4.3 billion worth of unsold clothes  – which is the last thing any retailer wants to deal with (sorry H&M). 

The solution?

Better analytics to tailor your store assortments. 

Advanced analytics powered optimization can provide merchandise planners with the insights needed to capture the optimal mix of style attributes (such as color or fabric) – uncovering potential opportunities with minimal to no impact on your overall spend (say whaaat?).

Such optimization will help retailers ensure their making the right investments in the right products – providing the optimal assortment to each brick-and-mortar store. 

Benefits of Optimizing Merchandise Buys

Just as improving your plan can reap huge cost-savings, optimizing your buy sheets will also prove incredibly beneficial.  

As a complimentary phase to the planning process, how much you buy for new and reordered products can mean the difference between a profit gain and loss. 

On one hand, you don’t want to underbuy and miss out on a potential big seller.

On the other hand, overbuys can lead to major promotions and a mass email send to your customer list saying "OOPS, WE MADE TOO MANY!" – which is far from ideal (markdowns = death 🔪to margins).

 

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As most retailers work to align their supply chains to avoid discounting, getting the right quantities for the right products is going to  significantly contribute to a retailer’s success.

By optimizing your buys, the right amounts will be stocked for full-price sell through and your customer’s inboxes will thank you for it.

Benefits of Optimizing Allocations

As your plan and buys are submitted, the next hurdle costing retailers is determining which stores to allocate product to. 

By this point, you’ve already bought all your product.

Now the challenge lies in making sure you’re putting products into the right stores, while taking into consideration locations with higher demand vs. those with less. 

Incorporating advanced analytics technology to optimize your allocations leverages this localized demand on a granular level (taking into account attributes, existing store inventories, and min/max presentation constraints) across all purchase orders to help merchants allocate to the right store location. 

Margin gains are realized by preventing “overallocation” at a particular location (which would have otherwise led to markdowns – the margin 🔪 killer!) and “underallocation” at a particular location (making sure you don’t miss a sale by having the right product in stock).

Benefits of Optimizing Online Fulfillment

Lastly, we enter into the complication with online fulfillment. This is a tricky one, as getting product from point A to point B isn’t as simple as it once was – especially considering the expected time frame for delivery continues to shrink. 

At the recent Gartner Supply Chain conference, one of the main takeaways was embodied in one particularly noteworthy stat: 96% of retailers plan to incorporate buy online and ship-from-store as part of their fulfillment offering.

While it’s inevitable retailers would be leveraging their stores to provide faster and cheaper delivery options – doing so efficiently (and profitably!) can pose a real challenge. 

The struggle lies in the realization of the following:

What if the cheapest option for fulfillment isn’t the closest one? 

You don’t want to pull product from stores that would have sold at full-price. However, you also want to make use of in-store inventory that’s likely to be marked down to fulfill a full-price e-commerce purchase. 

Then you have to balance shipping costs, picking/packing, and potentially cancelled orders to figure out the best fulfillment path.

So many variables come into play, and many retailers (like Aldo) find AI-powered optimization advantageous to rolling out their fulfillment initiatives profitably:

“We’re also rolling out artificial intelligence (AI) to help allocate products between stores. At Aldo, we often use stores to ship products purchased online. To ensure stores are not penalized for shipping product (by not having the product in stock), Aldo optimizes what store is selected for shipping, so stores maintain inventory to service customers walking into the store. The AI evaluates the probability of every store’s likelihood of selling products, factoring in foot traffic and other metrics, before deciding which store should fulfill an online order.” - Forbes 

To succeed, retailers must go beyond the rules-based approach to traditional optimization to deliver the right product from the right store for each e-commerce purchase.

A Case for the Board

Clearly, the ROI is there – optimizing each stage of the merchandising process will enable retailers to focus on making more accurate and quicker decisions for the products their putting a whole lot of cash on the line for.

Better bets. Faster decision-making.

Sounds like pretty solid ROI to me. As for finding the right vendor to execute that ROI, don’t hesitate to reach out and chat!

“Celect can offer solutions to retailers’ inventory optimization challenges through using predictive analytics and big data – therefore building capabilities for retailers to ensure the most optimal product is being offered to consumers.” – Re-Think Retail: The Store Must Do More, Ahead of the Curve, Cowen Research (April 2018)

Check out some of our inventory optimization use cases in retail to see how we can impact your bottom line.

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Topics: allocation, predictive analytics, merchandise planning, fulfillment, inventory optimization, data, advanced analytics

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