AWS for Industries

How fabric AI Order Cloud Spells Success with ESW on AWS

Today, your garden variety order management system (OMS) might be slipping behind the times. We’re seeing a seed change in order management, orchestration, and advanced fulfillment. That change is, quite simply, the introduction of artificial intelligence (AI) into all aspects of the order life cycle. With AI participating in the order cycle, real-time inventory updates may trigger a series of events that must happen quickly—and accurately.

Take, for example, the sell-through of a popular women’s cardigan sweater at a fashion and apparel retailer with just over 100 stores. Each location has a hodgepodge of sizes and colors. At one store, they have a stack of green cardigans, all in small, while at another store there isn’t a green cardigan in any size. Sell-through has been uneven, leaving shelf stock inconsistent and, left as-is, looks unappealing, and is destined for the clearance rack.

Poor assortment leads to poor customer experience. Imagine if you are at your favorite neighborhood store, and all they have in-stock is a pile of green cardigan sweaters in every size but yours. So, you ask a salesperson if they have any green cardigan sweaters in medium that haven’t been put out yet. The salesperson says, “let me check” and comes back with news you don’t want to hear. “Sorry, we don’t; and we’re sold out online.” To add insult to injury, the salesperson continues, “we have a bunch of those in green in our [somewhere further than you want to drive] store. Your heart sinks, and you realize you won’t be wearing that green cardigan sweater for St. Patrick’s day this year.

Contrast that experience with one where the apparel retailer has a re-imagined, modern OMS that uses real-time data to ensure inventory is right sized for each location in the chain. As the consumer, you don’t care that the retailer’s OMS uses AI to predict sell-through patterns, and you don’t care that it can recommend reshuffling lopsided location-based inventory to make the store shelves appealing in every store. Nope. What you care about is that green cardigan sweater brings out the hazel in your eyes and will be perfect for this year’s St. Patrick’s day party. And, you’ll take one in your size now, please!

Customer Experience is job #1

What we cannot forget, with any system, technology, or solution is that it must—in the end—serve the customer. And, customers want what they want, when they want it, and how they want it. While the story above centered on an in-store experience (which accounts for over 75% of customer purchasing today), it illustrates the degree to which our technology must now think ahead of traditional merchandising, fulfillment, and inventory management. This same story could happen with an online experience where the fulfillment house may be far enough away that the time to deliver the item exceeds the time the customer is willing to wait. Blurring the digital and physical lines, today’s OMS must keep-up. And, while you might say that OMS is a relatively new term in our technology compendium, it really isn’t—gaining popularity as an outgrowth of Enterprise Resource Planning (ERP) of the 1990s. Since then, order management systems have evolved significantly.

Technology’s short shelf life

The challenge today, as with any technology that doesn’t evolve, is it has a short shelf life. Those siloed OMS that grew out of yesterday’s ERP systems amounted to “home-grown extensions” meaning that they were built for the requirements of the day—and not for the requirements of today, let alone the requirements of tomorrow—and they were constrained by the original ERP system on which they were built. Composability simply was not in their vernacular at the time.

Think about it this way: the OMS of yore didn’t look at real-time inventory, fulfillment, and location stock balancing. It looked at coordinating orders from wherever they came—physical store or web. The smarter ones might have kept track of demand to feed early inventory forecasting systems; but that was about it.

And those older systems have created siloes between physical and digital stores, slowing ecommerce to a crawl while leaving physical stores on their own to sell through whatever they got, whenever they can get rid of it (think WIGIG: “When It’s Gone, It’s Gone”). Oh, what about inventory leveling across physical locations? Forget it. If a store can’t sell it during the season, to the clearance rack it goes at the start of the new season and with it goes lost revenue.

Yet, there’s another challenge. Most apparel retailers with in-store inventory can’t always respond to upticks in demand very well (remember that pile of green cardigan sweaters in small?). And then what to do for a customer who wants a sweater in a size you don’t have in your store? Should you send the customer to another location? How often does that result in a sale? Should you offer to have the item shipped? Well, that works sometimes, if the retailer can guarantee a delivery date. Essentially, it all comes down to a retailer’s capacity to respond quickly to demand; but in a world of infinite choices, brands must weigh even the tiniest margin of error with how it might affect customer loyalty.

Finally, and this is the death knell for your OMS if it can’t do it: can it keep up with a seamless shopping experience using real-time data? Tall order, yes. Doable, yes, with the right OMS. In our story, what if that salesperson had said, “Oh, the cardigan green sweater in medium? Yes, we have that in another store, but we can get it to you in two days—and if you don’t like it, or it doesn’t fit, just bring it back here for a refund or in-store credit, whatever works for you.”?

Okay, compare those scenarios. Your customer is faced with [the inconvenience of] driving across town to get a green cardigan sweater in her size (maybe—if the inventory count is correct and they still have one), or the risk-free experience of having it delivered to her home in a couple of days, knowing if it doesn’t fit she can return it with “no questions asked.” So, which would you choose?

A better choice

To meet the customer where they are in the purchasing process and to ensure you’ve done the best to predict demand, you have a clear choice: work with AWS Partner, fabric. Using a centralized platform for both orders and real-time inventory, even with multiple retail brands, increases the flexibility and adaptability to an ever-changing set of business conditions—like selling through certain colors or sizes of cardigan sweaters across your chain.

And that’s exactly what fabric AI Order Cloud does for you. You see, fabric has re-imagined the old-style OMS and infused it with AI at crucial stages of the order and fulfillment process. So, fabric AI Order Cloud knows the trends through integrating real-time data analysis, like real-time sales movement, allowing the retailer to adjust inventory levels to meet demand where it happens, whether in-store or on-line.

How fabric helps a global ecommerce leader tackle nearly 90 brands

Enter global direct to consumer (DTC) ecommerce leader, ESW, that helps the world’s best-loved brands and retailers grow their businesses worldwide while building customer loyalty through powerful, simple ecommerce solutions. As they grew, their home-grown solution had trouble keeping pace with demand; and those headwinds were getting stronger.

fabric’s approach

fabric’s AI Order Cloud offers a unified system for monitoring both order processing and live inventory levels across ESW’s brand portfolio. Implemented as a multi-tenant SaaS solution, this approach enhances infrastructure management and effectively addresses fluctuations in demand.

During a recent discussion, ESW’s Chief Product Officer, Frank Kouretas, shared his thoughts: “fabric AI Order Cloud is a game-changer for omnichannel retail.” He elaborated on fabric’s approach to addressing industry challenges, stating, “fabric is helping us create seamless order management experiences and simplifying inventory visibility across all channels. This enables the fulfillment of customer orders with greater efficiency at global scale. fabric’s composable technology enhances operational agility, helping us exceed customer expectations in today’s fast-paced retail environment.”

fabric CEO, Mike Micucci, highlighted fabric’s dedication to providing retailers with technology that can revolutionize their business saying, “fabric AI Order Cloud taps the game-changing power of AI to boost conversion by exposing shoppers to timely, accurate inventory so retailers can fulfill orders at the right margin and provide a next-level customer experience.”

And, there’s more. Umer Sadiq, fabric’s CTO, provides details into the capabilities and benefits AI Order Cloud has to offer in his blog, fabric AI Order Cloud: Built for AI-Powered Commerce, Today and Tomorrow.

Conclusion

One thing to remember is that your OMS must evolve to keep up with today’s customer demands, regardless of the channel, season, or time of day. Retailers using fabric AI Order Cloud are at an advantage. Learn more about fabric’s offerings and schedule a discussion to see how fabric AI Order Cloud can help.

AWS Partner Spotlight

fabric is a re-imagined OMS solution and key component of the modern commerce stack for enterprise retailers. By democratizing access to best-in-class digital commerce tooling, fabric provides merchants with alternatives to deliver a customer experience that builds loyalty to thrive in an ecommerce world. Employing AWS services like Amazon Bedrock and Amazon Redshift, fabric brings the full power of AI to their new fabric AI Order Cloud.

See fabric AI Order Cloud in action at NRF.

Suggested reading

fabric on AWS Marketplace
fabric AI Order Cloud press release
fabric AI Order Cloud: Built for AI-Powered Commerce, Today and Tomorrow

TAGS: ,
Cody Shive

Cody Shive

Cody Shive is the Global Partner Solutions Architect for Grocery, Drug, and Convenience at AWS, where he works with both cloud and physical store retail partners. Cody has 20+ years in Retail as an independent consultant, a technical lead for IBM/Toshiba Global Commerce Solutions, and as a Retail Transformation architect for NCR. Cody specializes in deep data analytics and keeps himself involved in self-service solutions such as Self-Checkout and Dash Cart technologies. He is passionate about the retail industry, stemming from his very first job at Albertsons in Florida. Cody is a graduate from the University of North Florida with a degree in Computer and Information Sciences and minor in Business Management.