Omnichannel retailing, or using store inventory to fill e-commerce orders, is one of today's hottest trends. Here are two technologies that can help companies operate in this new environment.
Anyone in the supply chain world who's attended a conference or leafed through a supply chain journal this year is aware that the consumer goods industry is obsessed with "omnichannel fulfillment." The concept of omnichannel fulfillment encompasses many things, but at the top of the list is the ability for a retailer to use store inventory to fill e-commerce orders in certain situations.
I don't recall the industry being this focused on a single idea since the days of the Wal-Mart-driven radio frequency identification (RFID) mandates in the mid-2000s. This current trend is, of course, different from the RFID craze in a key way: RFID in its initial iterations was a technology without a business case, while for many retailers, omnichannel fulfillment initially was a business concept that lacked a supporting technology set.
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[Figure 1] Technologies that support omnichannel commerceEnlarge this image
The good news, however, is the emergence of two key technologies that are now being used by a number of pioneering retailers with success. The first, distributed order management (DOM) software, handles the complex task of determining which orders to fill from distribution center (DC) inventory versus store inventory. The second, a modified version of warehouse management software (WMS) called "in-store WMS," enables the execution of those orders, which includes picking, packing, and shipping from the store. Figure 1 outlines the capabilities of the two solutions.
Distributed order management
DOM technology itself is not new. It was developed at least a decade ago and, for a long time, was primarily used as a way to allocate in-transit inventory to customer orders before it actually arrived at the warehouse. This technology has been very successfully adapted to the task of deciding when to use store inventory to fill e-commerce orders. Manhattan Associates, one of the early pioneers of DOM, has reported that numerous large apparel retailers have rolled it out to support their omnichannel strategies. Most of these retailers declined to be mentioned in this article, as they view their use of distributed order management as a competitive differentiator, but having seen the list, I can attest that it's an impressive one. One retailer that has gone public with DOM deployment is Lilly Pulitzer, which has used that software to expand its market reach and drive online traffic to its stores.
Another early pioneer of DOM technology was Yantra, first acquired by Sterling Commerce in 2004 and then by IBM in 2010. Although IBM has not been visibly promoting DOM recently, a number of high-profile retailers use the application. One retailer, Cabela's, has been using the solution since at least 2007.
There also are examples of companies that have achieved distributed order management capabilities without using an "off-the-shelf" application. For example, Stage Stores, parent company of Bealls and Peebles department stores, deployed Oracle in 2003 to perform a number of supply chain functions. The company has configured Oracle to help make decisions about when to fill e-commerce orders from the DC versus using store employees and inventory.
Gough Grubbs, senior vice president of distribution and logistics for Stage Stores, stressed that the company's omnichannel strategy is shifting rapidly. "While there is a high level of satisfaction with the fact that our systems provide us a choice in whether to fulfill orders from the stores or the DC, the preference is changing as our online business grows and profitability track records provide better direction," he said in an interview. "Contrary to a recent article publicizing another retailer's shift to increase store fulfillment of online orders, Stage Stores is shifting more toward DC fulfillment. We don't believe there is a common one-size-fits-all solution across retailers. The answer varies by retailer based on store size, depth of product, and location."
Sears has also been leading the charge with a robust technology set to support its omnichannel distribution strategy. The company uses an internally developed system to perform distributed order management. It has actually developed one of the most advanced sets of rules that I have seen for omnichannel retailing.
Most omnichannel retailers favor using either DC- or store-based inventory and using distributed order management software to manage the exceptions to those rules. For example, Cabela's first seeks to use distribution center inventory and only goes to the stores as a last resort if the DC is out of a product. Stage Stores currently chooses to use store inventory whenever possible and only fulfills an e-commerce order from the DC if there is no other choice. But, as Gough Grubbs noted earlier, this strategy is shifting more toward the DC. Unlike these retailers, Sears uses a more nuanced approach and calculates the lowest-cost fulfillment path on an order-by-order basis to determine how to best source that particular order. There is no preference for either the DC or the stores—simply a preference for the most efficient fulfillment method.
For the time being, Manhattan Associates seems to be the dominant player in DOM technology—the only major player that is actively promoting a (relatively) mature DOM platform. However, Manhattan can expect to have some company soon. JDA (formerly RedPrairie) reports that it will roll out an integrated DOM system with interfaces to WMS in the fall. It wouldn't be surprising to see some mid-tier software providers roll out DOM platforms soon as well, providing a less costly alternative aimed at mid-market customers.
In-store WMS
Once a decision has been made to fill an order from a retail store, it becomes critical for retail workers to be able to efficiently and accurately pick, pack, and ship it. Many retailers are deploying modified versions of warehouse management software in their stores to make this possible.
Sears is an excellent example. The company has been quietly building out an omnichannel network leveraging store inventory and can now serve 81 percent of the U.S. population via one-day ground delivery service. As part of this strategy, Sears recently began a pilot program using Highjump's WMS as the execution engine for picking these orders. The company realized that the average retail store worker represents a different demographic than the average warehouse worker: probably younger, less experienced with the concept of picking orders, and more familiar with a different generation of technology. Sears recognized these differences and chose to deploy the in-store Highjump WMS on iPads, using ring-style bar-code scanners. "The technology, which uses touchscreen user interfaces, is familiar to the average store worker, which reduces training time and improves pick speed," Jeff Starecheski, vice president of logistics services at Sears Holdings, told me.
The store planogram is loaded into the WMS, and workers are directed to pick orders by department, using a "cluster pick" methodology more often seen in a warehouse than in a retail store. During the most recent holiday season, Sears was able to process hundreds of orders per day from the store network and filled greater than 99 percent of those using two-day ground service.
Other software vendors have reported successfully adapting their WMS for retail store use. Manhattan Associates released an in-store inventory and fulfillment system module two years ago, drawing heavily on its WMS heritage. The company now reports more than 4,000 store locations currently using the software, which features touchscreen interfaces tailored to younger workers.
Retailers that are developing an omnichannel fulfillment strategy have no shortage of technology solutions to provide decision-support and execution capabilities. The coming year will likely see additional software vendors enter the market with offerings, adapting their WMS systems to support store fulfillment as well as developing distributed order management capabilities, thus allowing retailers to take flexibility and service to a new level for consumers.
The launch is based on “Amazon Nova,” the company’s new generation of foundation models, the company said in a blog post. Data scientists use foundation models (FMs) to develop machine learning (ML) platforms more quickly than starting from scratch, allowing them to create artificial intelligence applications capable of performing a wide variety of general tasks, since they were trained on a broad spectrum of generalized data, Amazon says.
The new models are integrated with Amazon Bedrock, a managed service that makes FMs from AI companies and Amazon available for use through a single API. Using Amazon Bedrock, customers can experiment with and evaluate Amazon Nova models, as well as other FMs, to determine the best model for an application.
Calling the launch “the next step in our AI journey,” the company says Amazon Nova has the ability to process text, image, and video as prompts, so customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content.
“Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” Rohit Prasad, SVP of Amazon Artificial General Intelligence, said in a release. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.”
The new Amazon Nova models available in Amazon Bedrock include:
Amazon Nova Micro, a text-only model that delivers the lowest latency responses at very low cost.
Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs.
Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
Amazon Nova Premier, the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models
Amazon Nova Canvas, a state-of-the-art image generation model.
Amazon Nova Reel, a state-of-the-art video generation model that can transform a single image input into a brief video with the prompt: dolly forward.
Economic activity in the logistics industry expanded in November, continuing a steady growth pattern that began earlier this year and signaling a return to seasonality after several years of fluctuating conditions, according to the latest Logistics Managers’ Index report (LMI), released today.
The November LMI registered 58.4, down slightly from October’s reading of 58.9, which was the highest level in two years. The LMI is a monthly gauge of business conditions across warehousing and logistics markets; a reading above 50 indicates growth and a reading below 50 indicates contraction.
“The overall index has been very consistent in the past three months, with readings of 58.6, 58.9, and 58.4,” LMI analyst Zac Rogers, associate professor of supply chain management at Colorado State University, wrote in the November LMI report. “This plateau is slightly higher than a similar plateau of consistency earlier in the year when May to August saw four readings between 55.3 and 56.4. Seasonally speaking, it is consistent that this later year run of readings would be the highest all year.”
Separately, Rogers said the end-of-year growth reflects the return to a healthy holiday peak, which started when inventory levels expanded in late summer and early fall as retailers began stocking up to meet consumer demand. Pandemic-driven shifts in consumer buying behavior, inflation, and economic uncertainty contributed to volatile peak season conditions over the past four years, with the LMI swinging from record-high growth in late 2020 and 2021 to slower growth in 2022 and contraction in 2023.
“The LMI contracted at this time a year ago, so basically [there was] no peak season,” Rogers said, citing inflation as a drag on demand. “To have a normal November … [really] for the first time in five years, justifies what we’ve seen all these companies doing—building up inventory in a sustainable, seasonal way.
“Based on what we’re seeing, a lot of supply chains called it right and were ready for healthy holiday season, so far.”
The LMI has remained in the mid to high 50s range since January—with the exception of April, when the index dipped to 52.9—signaling strong and consistent demand for warehousing and transportation services.
The LMI is a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).
Specifically, 48% of respondents identified rising tariffs and trade barriers as their top concern, followed by supply chain disruptions at 45% and geopolitical instability at 41%. Moreover, tariffs and trade barriers ranked as the priority issue regardless of company size, as respondents at companies with less than 250 employees, 251-500, 501-1,000, 1,001-50,000 and 50,000+ employees all cited it as the most significant issue they are currently facing.
“Evolving tariffs and trade policies are one of a number of complex issues requiring organizations to build more resilience into their supply chains through compliance, technology and strategic planning,” Jackson Wood, Director, Industry Strategy at Descartes, said in a release. “With the potential for the incoming U.S. administration to impose new and additional tariffs on a wide variety of goods and countries of origin, U.S. importers may need to significantly re-engineer their sourcing strategies to mitigate potentially higher costs.”
Grocers and retailers are struggling to get their systems back online just before the winter holiday peak, following a software hack that hit the supply chain software provider Blue Yonder this week.
The ransomware attack is snarling inventory distribution patterns because of its impact on systems such as the employee scheduling system for coffee stalwart Starbucks, according to a published report. Scottsdale, Arizona-based Blue Yonder provides a wide range of supply chain software, including warehouse management system (WMS), transportation management system (TMS), order management and commerce, network and control tower, returns management, and others.
Blue Yonder today acknowledged the disruptions, saying they were the result of a ransomware incident affecting its managed services hosted environment. The company has established a dedicated cybersecurity incident update webpage to communicate its recovery progress, but it had not been updated for nearly two days as of Tuesday afternoon. “Since learning of the incident, the Blue Yonder team has been working diligently together with external cybersecurity firms to make progress in their recovery process. We have implemented several defensive and forensic protocols,” a Blue Yonder spokesperson said in an email.
The timing of the attack suggests that hackers may have targeted Blue Yonder in a calculated attack based on the upcoming Thanksgiving break, since many U.S. organizations downsize their security staffing on holidays and weekends, according to a statement from Dan Lattimer, VP of Semperis, a New Jersey-based computer and network security firm.
“While details on the specifics of the Blue Yonder attack are scant, it is yet another reminder how damaging supply chain disruptions become when suppliers are taken offline. Kudos to Blue Yonder for dealing with this cyberattack head on but we still don’t know how far reaching the business disruptions will be in the UK, U.S. and other countries,” Lattimer said. “Now is time for organizations to fight back against threat actors. Deciding whether or not to pay a ransom is a personal decision that each company has to make, but paying emboldens threat actors and throws more fuel onto an already burning inferno. Simply, it doesn’t pay-to-pay,” he said.
The incident closely followed an unrelated cybersecurity issue at the grocery giant Ahold Delhaize, which has been recovering from impacts to the Stop & Shop chain that it across the U.S. Northeast region. In a statement apologizing to customers for the inconvenience of the cybersecurity issue, Netherlands-based Ahold Delhaize said its top priority is the security of its customers, associates and partners, and that the company’s internal IT security staff was working with external cybersecurity experts and law enforcement to speed recovery. “Our teams are taking steps to assess and mitigate the issue. This includes taking some systems offline to help protect them. This issue and subsequent mitigating actions have affected certain Ahold Delhaize USA brands and services including a number of pharmacies and certain e-commerce operations,” the company said.
Editor's note:This article was revised on November 27 to indicate that the cybersecurity issue at Ahold Delhaize was unrelated to the Blue Yonder hack.
The new funding brings Amazon's total investment in Anthropic to $8 billion, while maintaining the e-commerce giant’s position as a minority investor, according to Anthropic. The partnership was launched in 2023, when Amazon invested its first $4 billion round in the firm.
Anthropic’s “Claude” family of AI assistant models is available on AWS’s Amazon Bedrock, which is a cloud-based managed service that lets companies build specialized generative AI applications by choosing from an array of foundation models (FMs) developed by AI providers like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself.
According to Amazon, tens of thousands of customers, from startups to enterprises and government institutions, are currently running their generative AI workloads using Anthropic’s models in the AWS cloud. Those GenAI tools are powering tasks such as customer service chatbots, coding assistants, translation applications, drug discovery, engineering design, and complex business processes.
"The response from AWS customers who are developing generative AI applications powered by Anthropic in Amazon Bedrock has been remarkable," Matt Garman, AWS CEO, said in a release. "By continuing to deploy Anthropic models in Amazon Bedrock and collaborating with Anthropic on the development of our custom Trainium chips, we’ll keep pushing the boundaries of what customers can achieve with generative AI technologies. We’ve been impressed by Anthropic’s pace of innovation and commitment to responsible development of generative AI, and look forward to deepening our collaboration."