Companies can no longer think of warehouses solely as brick-and-mortar structures with an abundance of truck docks, material handling equipment, and pickers and packers. These traditional facilities are being augmented by new, nontraditional warehouses that in some cases may be difficult to think of as warehouses at all.
Nontraditional warehouses may take various forms. They may not even be buildings but still perform order assembly. They may be facilities that add a step to the supply chain while improving its overall efficiency. Or they may blur the distinctions among warehouses, retailers, and users. Many companies are finding that these unconventional warehouses fit well into flexible supply chains that have different customers, different order sizes, and different delivery paths and requirements.
The traditional supply chain flows product from manufacturers to fabricators to wholesalers/distributors to retailers and, finally, to the end user. (See Figure 1.) For this discussion, we define manufacturers as those who convert raw material, such as ore or crude oil or similar substances, into a manufacturing commodity. Fabricators turn the commodity into a product, and wholesalers/distributors buy large lots and sell in smaller quantities. Between each of these organizations lies a transportation element.
There are many variations on this structure. Often, fabricators sell to other fabricators, and wholesalers may do assembly or delayed customizations that are similar in some ways to what fabricators do. And for some products, the end user may be someone within the supply chain.
Fabricators typically have a receiving warehouse and a shipping warehouse with a production operation in between. "Lean" manufacturing, just-in-time (JIT), and similar concepts have reduced the size of fabricators' and wholesaler/distributors' warehouses, but the functions they perform are still necessary. Product is received from several sources, stored for some limited time period, and eventually delivered to a customer. This process may require some level of customization of the product for individual customers. Manufacturers, fabricators, and retailers may also have warehouses that perform the same internal functions.
If this is the traditional model for a warehouse, what does a nontraditional warehouse look like, and what will be its impact on the supply chain? The following examples provide some clues to the answer.
Music: The PC as warehouse
To the user, the music business is all about content— the songs that we want to hear. The "product," however, has always been a physical object, whether it was sheet music, a vinyl record, an 8-track tape, or a compact disc (CD).
The supply chain for the music industry traditionally has looked as shown in Figure 2. The fabricators buy the media (for example, the physical CD), the packaging material (the case and wrapper), and the items that are specific to the stock-keeping unit (SKU), such as the cover art and labels. These materials are all shipped to the component warehouse. To produce the SKU, warehouse workers pick the component pieces and bring them to the production line, where the digital content is imprinted on the media and the package is assembled. The SKU then travels to the finished-goods warehouse, where it is picked as part of an order for shipment to a distributor. The distributor receives titles from many producers and later picks the SKU as part of an order for shipment to a retailer. Finally, the end user goes to the retailer—in person, by phone, or over the Internet—and selects the CD.
The large number of steps combined with the short lifecycle of these products puts a strain on the supply chain. To be successful, the product must be in the retailers' hands on the day of release or a sale may be lost to a competitor.
One of the early attempts to streamline the supply chain for digital products was the "music kiosk." The kiosk functioned as a nontraditional warehouse that engaged in delayed customization. The supply chain was altered so that common, standard components went to many different kiosks. Rather than preprinted cover art, for example, only blank paper would be sent to the kiosk. The kiosk itself was a CD recorder that was connected via a high-speed data link to a content server, which contained a digital copy of both the music and the cover art. The end user could shop at any kiosk on the day the product was released, select the item, and have the kiosk record the product to the media while a printer created and inserted the cover art. The kiosk would then insert the recorded media and dispense it to the user. The kiosk had become, in effect, the CD producer, eliminating late deliveries and lost sales. The resultant supply chain is shown in Figure 3.
But the music kiosk was quickly relegated to a footnote in the annals of supply chain history. The very technology that made the kiosk possible—the digital recorders and printers inside—caused its death. Once every personal computer (PC) included a CD recorder, the market was ready for the next paradigm shift. This largely user-driven shift has fundamentally changed how music is delivered to customers.
Thanks to music sellers such as iTunes, the buyer no longer sees the unit of purchase as a physical object like an album or CD. The unit of purchase is now a single piece of music, or track, in digital form. The listener can now create a CD composed of tracks from several artists, and the cover art may be created from images downloaded from sources outside the music business. The new music "warehouse" is the PC, and the new supply chain must deliver all of the components to the new producer, the end user. (See Figure 4.)
More recently, the music business has been shifting even farther from the traditional model as music CDs are replaced by iPods and other MP3 players that use the PC hard drive as the archival storage method. If the CD were ever completely eliminated, the music supply chain would become entirely electronic, with no physical transportation and storage required.
Spare parts: Doing more costs less
Another example of a nontraditional warehouse can be found in capital-intensive manufacturing industries such as refining, petrochemicals, metals, and paper. The traditional raw-materials and finishedproduct warehouses in these fields are disappearing as companies employ supply chain technologies to enable JIT delivery of raw materials and to schedule production only for those goods that have already been ordered. Manufacturers in these industries are using production warehouses as temporary locations for materials that accumulate due to customers' lateterm supply chain adjustments. Build-to-stock is becoming a dying practice at these companies.
The nature of production in these capital-intensive industries has also changed. Automation has made many production workers' positions unnecessary. A glance at the organization chart of one of these companies will confirm that the maintenance staff outnumbers the production staff; that's because machinery must be running nearly 100 percent of the time to achieve the maximum return on investment.
Enter the nontraditional warehouse with nontraditional goals. Instead of storing high-turnover product, this warehouse handles "archival storage" of spare parts that management hopes will never be used.
These parts are being stored only because they are unique and are no longer available, or because they have a lead time of perhaps 18 months or longer. They are kept in case there is a catastrophic failure of a key part on the production line. There is no need for rapid delivery of spare parts because removal of a broken part may take hours or days.
In the past, the responsibility for ensuring that parts were available fell to the maintenance organization. Procurement decisions were based on available budgets as well as on the maintenance staff's experience of the need for specific parts in different areas of the plant. There was little communication between manufacturing and the tradespeople, such as plumbers and machinists, about the purchase of parts. There also was limited visibility of the parts that were available within a plant, and there was no contact between the various plants within the company. As a result, the number of standard parts held in stock for a single plant proliferated. At the same time, it was common for one department to "borrow" parts from another one—sometimes without informing the other department that it had taken the parts for its own use. Furthermore, because inventory management was not a core function, obsolete parts were not purged after equipment was upgraded.
Storage conditions also became an issue. The maintenance organization frequently was located adjacent to the production machines, even though such areas were usually dusty, greasy, and subject to vibration from large production equipment. Limited space within the production building often led companies to store equipment outside, where large parts, including electric motors or parts with machined surfaces or bearings, would be subjected to temperature extremes, rain, and snow. The supply chain often looked as shown in Figure 5.
The new, nontraditional spare-parts warehouse is a smart addition to any organization that has been charged with optimizing the cost of keeping machines running 100 percent of the time by increasing the availability of spare parts. While some of the tools used—racks, forklifts, and data-collection terminals—are common to traditional warehouses, the key functions performed are vastly different.
The first priority often is the development and implementation of a common parts-identification system that is based on a functional description of the parts rather than on the manufacturer's part numbers or the location where the part is used. Providing a dry, dust-free, vibration-free environment without temperature extremes is another important change for this new parts warehouse. Velocity-based slotting is superseded by the clustering of products based on which trade (plumbing, electrical, etc.) will use it; where in the plant the equipment is located; or physical characteristics of the part that limit where it can be stored, such as in a humidity-controlled environment, an area with fire protection, or an area with a high-capacity overhead crane.
Although some traditional supply chain tools (for example, economic order quantity, minimum/maximum rules, and cycle counts) may be applied in order to control inventory, "just-in-case" is a more common philosophy in these circumstances than "justin-time" is. The new spare-parts supply chain may add a warehouse and its functions, but it also streamlines the flow of spare parts and greatly enhances their visibility. (See Figure 6.)
Companies that have implemented this type of nontraditional warehouse have reduced their parts inventory. They also have increased production-line uptime by improving the visibility of spare-parts inventory and ensuring that available parts will be in working condition. These changes can improve profits by millions of dollars.
Retail: Picking in the aisles
The retail store represents the last step before the consumer. The consumer enters the store, browses, finds what he or she is looking for, takes it to the register, pays, and takes it home. That's how most of our consumer-driven world works. But some supermarkets have returned to the old-fashioned practice of picking and delivering orders directly to customers—and they're not picking from a general warehouse or a specialized consumer-order warehouse. Instead, order pickers walk the supermarket aisles with a printed pick list or a radio frequency (RF) terminal and pick orders into carts or totes, which will then be loaded onto a delivery truck for a scheduled delivery.
Turning a supermarket aisle into a pick line is about as nontraditional as a warehouse can get. It can work, but there are potential problems. The "slotting" methods used in supermarkets, for instance, are in many ways the antithesis of good pick-line design. Most supermarkets are laid out so that the first thing the customer sees is the fresh-produce section. The purpose of this type of design is to market the store as a place to buy fresh, healthy products. The consumer then must walk to the back of the store to find items that are purchased most often, such as fresh meat and milk. The store layout also purposely forces the consumer to pass as many product displays as possible, thereby encouraging spontaneous purchases. Item placement on shelves, moreover, is not driven by product velocity. Instead, product location is determined by the manufacturers' desire to promote certain wares to consumers and by the fees that the manufacturers are willing to pay to the supermarket for prime locations. These factors contradict every principle involved in designing an efficient picking operation.
Will the supermarket change into a warehouse, with velocity-based slotting and order pickers strolling the aisles with multiple-order RF pick carts? Will the supermarket disappear completely and remove one link in the supply chain? The latter is unlikely but not impossible. In either case, the supermarket will have to revamp its layout to take into account both marketing considerations and warehouse- style efficiency.
Approach with caution
As the examples in this article illustrate, nontraditional warehouses represent attempts by companies to increase revenue or reduce costs. They are manifestations of the experimentation with business models that is going on today.
Not all will succeed, however. Some may remember Kozmo.com, whose business model was to provide a free delivery service in large cities for videos, pizzas, and small consumables using bicycle messengers operating after normal work hours. Small warehouses and messenger depots were located in residential areas throughout a city. The company was supposed to generate income in two ways: by charging merchants for the deliveries and from markups on the warehoused items. There are many reasons why the company failed, but the case provides an example of how even a nontraditional warehouse that provides a market advantage over the traditional supply chain can prove unsuccessful, and why any potential paradigm shift should be approached with caution.
We will most likely see more business failures as companies pilot new types of nontraditional warehousing. One thing is certain, though: The assumption that warehouses are merely places of storage doesn't hold true any longer. Nontraditional warehouses are a new type of link in the flexible, agile supply chain that companies are fashioning to reduce inventory, improve customer service, and/or boost profits. They will force companies to rethink their supply chain structure and design them to ensure that product flow matches demand.
Benefits for Amazon's customers--who include marketplace retailers and logistics services customers, as well as companies who use its Amazon Web Services (AWS) platform and the e-commerce shoppers who buy goods on the website--will include generative AI (Gen AI) solutions that offer real-world value, the company said.
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."