The Journal of Business Logistics (JBL), published by the Council of Supply Chain Management Professionals (CSCMP), is recognized as one of the world's leading academic supply chain journals. But sometimes it may be hard for practitioners to see how the research presented in its pages applies to what they do on a day-to-day basis. To help bridge that gap, CSCMP's Supply Chain Quarterly challenges the authors of selected JBL articles to explain the real-world implications of their academic research.
THE ARTICLE "Just-in-time retail distribution: A systems perspective on cross-docking," by Paul Buijs of the University of Groningen, Hans W. Danhof of the Dutch retailer Blokker, and J. (Hans) C. Wortmann of the University of Groningen. Published in the September 2016 issue of the Journal of Business Logistics.
THE UPSHOT
Cross-docking—the process of moving goods through the distribution network without placing them in stored inventory at a distribution center—typically involves moving products from an inbound trailer directly to an outbound trailer or temporarily storing them on the floor before shipping them out. Cross-docking improves the processing speed of a distribution network while also reducing the amount of inventory it needs to hold.
Many companies, however, struggle to effectively implement cross-docking strategies. One of the main reasons is that most have implemented cross-docking without changing their organizational structure or metrics. Most supply chain literature agrees that the best approach is a holistic one, where cross-docking operations are not only synchronized with inbound and outbound logistics processes but also are managed by the same people with the same or similar performance metrics.
But this does not often happen. Managers who oversee cross-docking operations typically are not also involved in external logistics processes, and the metrics they use focus only on internal efficiencies, such as the distance traveled by material handling equipment in the distribution center.
In this article, the authors sought to prove the extent to which a holistic approach to cross-docking provides a significant advantage over a more localized approach focused only on the distribution center. To accomplish this, they worked with a major retailer in the Netherlands to identify cross-docking improvement opportunities. The two possibilities they studied were 1) whether to change the dock-door assignment policy; and 2) whether the retailer should cluster and sort loads bound for the same store at the cross-dock itself or at a facility farther upstream in the distribution network. They also used simulation software to determine which would create a bigger impact: focusing on local optimization, or focusing on networkwide optimization. As part of that process, the authors discovered that the retailer's current metrics were not fully communicating the benefits of a holistic approach. To address that shortcoming, they developed new metrics, borrowing from concepts used in lean manufacturing.
Dr. Paul Buijs, the lead author, spoke with Supply Chain Quarterly about what these findings could mean for companies that are currently using cross-docking or are thinking of implementing the technique.
What issues were you seeking to explore through this research?
We saw that most of the benefits of cross-docking are lower inventory levels. But the lower inventory levels also form the main challenge of cross-docking. Due to the low inventory levels, a much tighter coupling arises between the logistics inside the distribution center and the inbound and outbound logistics networks. We also saw that both the academic research as well as practitioners' own strategies were geared mostly toward optimizing the operations that take place locally—in other words, how to improve cross-docking operations at the distribution center itself. But due to the tight coupling between the local cross-docking operations and the network logistics, there is only so much [benefit] you can [achieve] when you have this localized focus.
Our main message is that future efforts should be geared toward addressing cross-docking from a holistic perspective where we consider local and network considerations together. But first we wanted to empirically verify this claim. Then we also set out to provide a detailed example of what firms could actually do with this holistic approach, what it generally implies for managers, and how they can adopt it.
Your paper presents a case study of a large retailer in the Netherlands. Why did you choose to focus on this particular retailer?
First of all, this retailer is one of the largest international grocery retailers, and it's considered a leader in how it's organizing its distribution. Cross-docking forms a central part of the company's distribution strategy. And at the time we started this research, the retailer was planning a major change in its distribution network. Although that change did not actually relate to cross-docking, it offered a very good opportunity to propose and test some distribution network changes that we thought could improve cross-docking from a systemic or networkwide perspective.
On top of that, one of the warehouse managers at the retailer was willing to cooperate with us in proposing and testing a change for a local cross-dock improvement effort. This provided a rather unique opportunity to gather empirical data to support the call for a more holistic approach to cross-docking.
What makes the performance metrics you propose in the paper different from the traditional metrics used by cross-docking distribution centers?
Metrics was one of the key things that drove this research. We believed that because cross-docking is all about reducing inventory and improving flow, it has a close analogy with lean manufacturing. It therefore also makes sense to make this link with performance metrics.
What emerged during our research is that there was a lack of performance metrics that would trigger management to look at cross-docking more holistically. Cross-docking operations are managed according to traditional warehousing principles, where one manager would be responsible for operations inside the distribution center and another one for the transportation or other logistics at the network level. On top of that, each of those managers would have his or her own set of metrics geared toward either localized performance or networkwide performance.
With these existing metrics, it was very hard for us to convey the need for the changes that we were proposing to the retailer. So we added some performance metrics inspired by "lean" and just-in-time manufacturing that focused on the flow of the loads and work-in-progress throughout the distribution network. An example would be that we kept track of the number of in-process loads that were on-site at the cross-dock, which translates into the work-in-progress metric from lean manufacturing. We also tracked the life span of loads throughout the cross-docking systems [how long it takes a load to go through the distribution network as a whole], which gives an indication more or less of the flow.
We also used more traditional metrics because we felt we could relate more easily to the managers using their own metrics. An example of a traditional metric that we incorporated was the travel distance covered by the material handlers inside the cross-dock. The less time material handlers have to travel, the more efficient the cross-dock operations are considered to be by managers.
How can companies use this information to improve their own cross-docking operations?
Our study shows that while local improvement efforts for cross-docking can be very effective at making the operations inside the distribution center more efficient, the impact of these improvements from a systemwide performance perspective can actually be quite limited.
On the other end, our paper shows that even minor changes in the network design could result in considerable systemwide performance improvements. The network design change studied in our paper involved changing the location at which loads are clustered and sorted for store delivery; that is, from the distribution center to one facility farther upstream in the distribution network. This is just one example of just one kind of networkwide change that could seriously benefit cross-docking performance.
The paper also shows that changes at the network level affect another type of performance metric—metrics that not many firms currently use in cross-docking. Without such metrics, many opportunities to improve operations may go unnoticed. We provide just a few examples of metrics that would reveal these opportunities, such as the number of load carriers [wheeled equipment for moving cases] on-site or how long it takes for the load carriers to move through the distribution network. The data for these measures are typically already available in a firm's existing warehouse management system.
What is the key takeaway from your research for practitioners?
First and foremost, we empirically verified, and therefore stressed, the importance of taking a holistic approach to cross-docking. But in order to take this approach, cross-dock management needs to be organized differently. Firms could consider changing the way management responsibilities around cross-docking are organized. They could also consider adopting new performance metrics that better reflect the holistic approach to cross-docking.
When you have functional silos, with managers who are responsible for internal operations and managers who are responsible for network operations, and they each have their own metrics, it's quite hard to make cross-docking happen efficiently. So a key takeaway is that you need to organize cross-docking differently to see the opportunities and then to seize them.
TO READ THE FULL ARTICLE ...
As a member benefit, CSCMP members can access articles in the Journal of Business Logistics at no charge. To request access to this and other JBL articles, send a request via e-mail to cscmppublications@cscmp.org.
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."
A growing number of organizations are identifying ways to use GenAI to streamline their operations and accelerate innovation, using that new automation and efficiency to cut costs, carry out tasks faster and more accurately, and foster the creation of new products and services for additional revenue streams. That was the conclusion from ISG’s “2024 ISG Provider Lens global Generative AI Services” report.
The most rapid development of enterprise GenAI projects today is happening on text-based applications, primarily due to relatively simple interfaces, rapid ROI, and broad usefulness. Companies have been especially aggressive in implementing chatbots powered by large language models (LLMs), which can provide personalized assistance, customer support, and automated communication on a massive scale, ISG said.
However, most organizations have yet to tap GenAI’s potential for applications based on images, audio, video and data, the report says. Multimodal GenAI is still evolving toward mainstream adoption, but use cases are rapidly emerging, and with ongoing advances in neural networks and deep learning, they are expected to become highly integrated and sophisticated soon.
Future GenAI projects will also be more customized, as the sector sees a major shift from fine-tuning of LLMs to smaller models that serve specific industries, such as healthcare, finance, and manufacturing, ISG says. Enterprises and service providers increasingly recognize that customized, domain-specific AI models offer significant advantages in terms of cost, scalability, and performance. Customized GenAI can also deliver on demands like the need for privacy and security, specialization of tasks, and integration of AI into existing operations.