What Retailers Can Learn From H&M’s $4B Worth of Unsold Inventory

“In the world of fashion retailing, where shopping is fast moving online and stores try to keep inventories closely matched to sales, even a small stack of unsold clothes can be a bad sign.”  The New York Times 

The news circulating about H&M’s colossal pile of unsold merchandise simply reminds us of the risks fast-fashion retailers face to deliver trends at quicker-than-lightning speed consumers demand.

Although at the unfortunate expense of H&M’s affected profits, here are three invaluable lessons retailers can take away from the big, pile of unsold inventory getting so much press lately:

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Topics: convenience, inventory optimization, data, advanced analytics, fast fashion, speed, accuracy

The Challenges of Sequential Allocation

It’s almost that time of the year – Amazon Prime Day’s a-looming. The midsummer, Black Friday equivalent and annual reminder to retailers of one of the most wildly successful loyalty campaigns ever created. 

This four-year-old online shopping “holiday” is also a clear reminder to retailers of this:

It’s time to step up your game.

Beyond the Prime Day phenomenon, Amazon’s success stems from understanding what their customers want. 

In essence, understanding what your customer wants means the ability to offer the right product in the right place at the right time: the ultimate challenge retailers face today.

Perhaps not surprisingly, a huge part of this challenge can be traced back to a retailer’s allocations. You can have the best assortment plan in place, the perfect quantities purchased—and yet, you continue to see unsold product, day after day, that should have sold well at store A or out-of-stock situations at the supposedly-low-demand store D.

It’s not easy to know ahead of time that you should have stocked 30 more pairs of faux fur slide sandals in a size seven at Houston’s Galleria store. While hindsight’s 20/20, it’s also freakin’ costly. 

Here are some of biggest allocation challenges retailers need to quash ASAP:

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Topics: allocation, localization, advanced analytics, customer preference

How to Exceed Customer Delivery Expectations Through Order Fulfillment

“Consumer centricity is increasingly necessary for retailers and brands to maintain loyalty within highly saturated markets. Launching technology that not only delivers shipments in an expedited and efficient manner while subsequently optimizing current inventory levels will improve margins.” - WWD 

According to the NRF, Father’s Day spending for 2018 is expected to reach $15.3 billion dollars and more than one third of all shoppers are expected to buy something online.

I’m definitely one of those shoppers this year.

As I narrow down the search for my dad’s gift, shipping cost and delivery speed are by far one of the top deciding factors. 

While, admittedly, waiting the week before Father’s Day could have something to do with this (guilty), the data behind online delivery expectations say otherwise:

This “Amazon Effect” (so it’s been dubbed) is bringing the heat to retailers big and small, and urging many to discover ways to focus their current supply chain around the customer. 

Especially when it comes to the “last mile” as many traditional brick-and-mortar retailers realize one of the best ways to implement a “customer centric” approach is to capitalize on proximity – i.e., their physical stores.   

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Topics: localization, fulfillment optimization, cross-channel demand, advanced analytics, customer preference, order fulfillment

The Need for Retail Speed and Accuracy in an Ultrafast Fashion World

“Consumers are rabbits in today’s supply chain, and brands, retailers and manufacturers are the too-slow turtles that can’t keep up—despite knowing they need to.” – Spencer Fung, CEO of Li & Fung 

Speed is the name of the game. 

The see-now-want-now mentality isn’t going away anytime soon. While convenience and access continue to drive the consumer desire for immediacy, the gap between customers and retailers expands even more steadily.                       

The number one problem on every top retailer’s mind is this: What can we do to shrink this gap?

“What’s been made very clear in recent years is that the entire supply chain is being disrupted because of consumers’ see-now, want now mentality, because of e-commerce and the convenience it offers, and because of new business models coming from startups that aren’t bogged down by remnants of the old days and ways.” – Sourcing Journal

At the end of the day, leads times cost. The old days of showcasing designs months in advance before delivering to stores are long gone. The speed to market issue is where the problem lies – and slight improvements year over year are not enough at the rate consumers are going today. 

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Topics: convenience, inventory optimization, data, advanced analytics, fast fashion

The ROI of Optimization: A Case for the Board

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.

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.

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

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