Our annual DC metrics study has found little change in performance against the standard operational measures in the past few years. So how will facilities look to distinguish themselves in the future?
Former New York Mayor Ed Koch was well known for asking the question, "How'm I doing?" as he prowled the city. His goal was to get a street-level view of his performance.
Nine years ago, DC Velocity and its research partners set out to look at how warehouse and distribution center managers dealt with essentially the same question—that is, what steps they were taking to assess how their own operations were doing. The result was the launch of our annual metrics study, which tracks the measures DC professionals are using to monitor their operations as well as changes and trends in overall performance against those metrics from year to year. The study also provides valuable benchmarks managers can use to see how their operations stack up both within the company and against their competitors.
Since the inception of this study, our focus has been on the operational side of performance—the number of labels licked and boxes kicked per hour, or the percentage of orders sent damage-free. You name the measure, and if it's important to the industry, we have the metric.
More recently, however, we have been turning our attention to metrics of another sort—what we might call the "soft" side of logistics. But more on that later. First, we'll take a look at what we learned this year.
Which metrics matter most?
This year's study was conducted among DC Velocity's readers and members of the Warehousing Education and Research Council (WERC) by researchers from Georgia Southern University and the consulting firm Supply Chain Visions. While the number of responses to the survey, which was carried out via an online questionnaire in January, was relatively large (in excess of 225 for 2012), it did not match last year's usable responses. In order to increase the predictive power of the benchmarks, researchers combined the 2012 results with 2011's, for a total of 802 participants.
Respondents were asked to identify the metrics they used in their operations as well as to grade their own facilities' performance against 44 specific operational measures. For purposes of analysis, the measures have been grouped into five balanced sets: customer, operational, financial, capacity/quality, and employee.
As for what performance metrics are most favored by DC and warehouse professionals, the study has shown that the top measures don't vary much from year to year. For 2012, the most frequently employed metrics were on-time shipments, order picking accuracy, and average warehouse capacity used. Those same three measures also topped the previous year's list, albeit in a slightly different order.
In fact, as Exhibit 1 shows, there has been little change in the top 12 metrics used by the industry in the past three years—for example, the only change in this year's rankings from 2011 was a slight variation in the order of the top 12. (We should point out that we made a change in methodology last year in calculating the top 12 list, which caused some shifts in the rankings against previous years.)
Also worth noting here is that the three measures related to cycle time—4, 5, and 6 in 2012—have steadily increased in importance since 2010, suggesting that DC managers are recognizing that a "supply chain" type view of performance is important in addition to considering individual activities.
If we dig a bit further into the results for a handful of the metrics, we note that after several years of steady improvement, performance in general seems to have leveled off somewhat. Given the profile of the respondents—members of a professional organization and/or readers of a leading logistics industry publication—you might expect to see a continuation of the long-term trend of performance improvement. However, logic dictates that we will eventually reach a point of saturation, and we believe we are nearing this point.
In addition, we have noticed a continued tightening of the measures. In other words, the gap between best-in-class companies (the top-performing organizations) and "major opportunity" companies (the bottom performers) is growing smaller, or at least not widening.
Take on-time shipments, for example. Exhibit 2 shows the performance for companies categorized as "major opportunity" (the lowest 20 percent of respondents), "typical" (the middle 20 percent), and "best in class" (the top 20 percent), along with the median numbers for 2008, 2010, and 2012. The best-in-class performers, which had almost nowhere to go, remain near perfect. Typical performers' numbers also remain essentially unchanged, while those in the "major opportunity" category made small gains when it came to getting shipments out the door on time. The results for order picking accuracy and fill rate are similar.
In terms of performance, we believe the metric to watch over the next couple of years will be annual workforce turnover. As Exhibit 3 shows, the turnover rates among best-in-class and median performers are at or near their lowest points since 2007, when we first began to break out the data by quintiles. As economic activity heats up, we expect to see these numbers return to 2007 levels, when the median turnover rate was 14 percent.
Why? In 2006, the national unemployment rate hovered around 4.5 percent, with jobs added at the rate of about 200,000 a month. (Keep in mind that the 2007 survey report was based on data from 2006.) The low unemployment rate meant greater opportunities for workers in search of better pay or working conditions.
Contrast that to the situation in 2011, when the high rate of unemployment likely deterred workers from leaving steady jobs. It's probably no surprise that the median turnover rate dropped to 5 percent from 8 percent the previous year, an improvement of 37 percent. (The best-in-class performers, by the way, have maintained relatively low turnover rates despite wide fluctuations in the economy.)
Now, unemployment is at its lowest rate since February 2009, making the economic environment more conducive to worker mobility than it's been for some time. We suspect that as confidence in the economy grows, more employees will start to seek greener pastures, and the turnover rate will rise.
What's next?
As we've noted, the gap between the best- and weakest-performing DCs has narrowed or stayed the same in recent years. That raises the question of what will emerge as the next competitive differentiator. We would argue that as facilities continue to close the gap in operational performance, attention will turn to what we call the "soft" side of performance—how human beings interact with one another in the workplace. That's because skills like the ability to communicate effectively and build and maintain relationships—whether with colleagues, suppliers, or customers—have a measurable impact on both operational performance and customer satisfaction. (See Exhibit 4 for some examples.)
To put it another way, the effectiveness of an organization's relationship with customers and suppliers depends heavily upon non-operational elements of performance: employees' ability to communicate well, personally interface with counterparts at other companies, make mutually beneficial decisions, solve problems jointly, and collaborate. Ignoring these interpersonal aspects of performance can often have negative long-term effects on relationships, which is likely to have severe consequences for a company as a whole.
To develop a true picture of supply chain success, then, organizations must measure both the "what" (operational) and the "how" (interpersonal) aspects of performance. The importance of these soft skills dictates a need for a new generation of business metrics and scorecards designed to gauge both sides of performance.
In the upcoming years, research will be needed to explore how these attributes should be defined, calculated, and tracked. It is significantly different from previous metrics and benchmarking studies, as many of the measures are relatively new. The University of Tennessee and Georgia Southern University have already started researching companies that are taking a hard look at the soft side of performance.
Editor's note: The full results of the study will be available at www.werc.org after the annual WERC conference in Atlanta May 6-9. To read more about the "soft" side of metrics, see "A hard look at the soft side of performance," which appeared in the Quarter 4/2011 edition of DC Velocity's sister publication, CSCMP's Supply Chain Quarterly.
Oh, you work in logistics, too? Then you’ve probably met my friends Truedi, Lumi, and Roger.
No, you haven’t swapped business cards with those guys or eaten appetizers together at a trade-show social hour. But the chances are good that you’ve had conversations with them. That’s because they’re the online chatbots “employed” by three companies operating in the supply chain arena—TrueCommerce,Blue Yonder, and Truckstop. And there’s more where they came from. A number of other logistics-focused companies—like ChargePoint,Packsize,FedEx, and Inspectorio—have also jumped in the game.
While chatbots are actually highly technical applications, most of us know them as the small text boxes that pop up whenever you visit a company’s home page, eagerly asking questions like:
“I’m Truedi, the virtual assistant for TrueCommerce. Can I help you find what you need?”
“Hey! Want to connect with a rep from our team now?”
“Hi there. Can I ask you a quick question?”
Chatbots have proved particularly popular among retailers—an October survey by artificial intelligence (AI) specialist NLX found that a full 92% of U.S. merchants planned to have generative AI (GenAI) chatbots in place for the holiday shopping season. The companies said they planned to use those bots for both consumer-facing applications—like conversation-based product recommendations and customer service automation—and for employee-facing applications like automating business processes in buying and merchandising.
But how smart are these chatbots really? It varies. At the high end of the scale, there’s “Rufus,” Amazon’s GenAI-powered shopping assistant. Amazon says millions of consumers have used Rufus over the past year, asking it questions either by typing or speaking. The tool then searches Amazon’s product listings, customer reviews, and community Q&A forums to come up with answers. The bot can also compare different products, make product recommendations based on the weather where a consumer lives, and provide info on the latest fashion trends, according to the retailer.
Another top-shelf chatbot is “Manhattan Active Maven,” a GenAI-powered tool from supply chain software developer Manhattan Associates that was recently adopted by the Army and Air Force Exchange Service. The Exchange Service, which is the 54th-largest retailer in the U.S., is using Maven to answer inquiries from customers—largely U.S. soldiers, airmen, and their families—including requests for information related to order status, order changes, shipping, and returns.
However, not all chatbots are that sophisticated, and not all are equipped with AI, according to IBM. The earliest generation—known as “FAQ chatbots”—are only clever enough to recognize certain keywords in a list of known questions and then respond with preprogrammed answers. In contrast, modern chatbots increasingly use conversational AI techniques such as natural language processing to “understand” users’ questions, IBM said. It added that the next generation of chatbots with GenAI capabilities will be able to grasp and respond to increasingly complex queries and even adapt to a user’s style of conversation.
Given their wide range of capabilities, it’s not always easy to know just how “smart” the chatbot you’re talking to is. But come to think of it, maybe that’s also true of the live workers we come in contact with each day. Depending on who picks up the phone, you might find yourself speaking with an intern who’s still learning the ropes or a seasoned professional who can handle most any challenge. Either way, the best way to interact with our new chatbot colleagues is probably to take the same approach you would with their human counterparts: Start out simple, and be respectful; you never know what you’ll learn.
With the hourglass dwindling before steep tariffs threatened by the new Trump Administration will impose new taxes on U.S. companies importing goods from abroad, organizations need to deploy strategies to handle those spiraling costs.
American companies with far-flung supply chains have been hanging for weeks in a “wait-and-see” situation to learn if they will have to pay increased fees to U.S. Customs and Border Enforcement agents for every container they import from certain nations. After paying those levies, companies face the stark choice of either cutting their own profit margins or passing the increased cost on to U.S. consumers in the form of higher prices.
The impact could be particularly harsh for American manufacturers, according to Kerrie Jordan, Group Vice President, Product Management at supply chain software vendor Epicor. “If higher tariffs go into effect, imported goods will cost more,” Jordan said in a statement. “Companies must assess the impact of higher prices and create resilient strategies to absorb, offset, or reduce the impact of higher costs. For companies that import foreign goods, they will have to find alternatives or pay the tariffs and somehow offset the cost to the business. This can take the form of building up inventory before tariffs go into effect or finding an equivalent domestic alternative if they don’t want to pay the tariff.”
Tariffs could be particularly painful for U.S. manufacturers that import raw materials—such as steel, aluminum, or rare earth minerals—since the impact would have a domino effect throughout their operations, according to a statement from Matt Lekstutis, Director at consulting firm Efficio. “Based on the industry, there could be a large detrimental impact on a company's operations. If there is an increase in raw materials or a delay in those shipments, as being the first step in materials / supply chain process, there is the possibility of a ripple down effect into the rest of the supply chain operations,” Lekstutis said.
New tariffs could also hurt consumer packaged goods (CPG) retailers, which are already being hit by the mere threat of tariffs in the form of inventory fluctuations seen as companies have rushed many imports into the country before the new administration began, according to a report from Iowa-based third party logistics provider (3PL) JT Logistics. That jump in imported goods has quickly led to escalating demands for expanded warehousing, since CPG companies need a place to store all that material, Jamie Cord, president and CEO of JT Logistics, said in a release
Immediate strategies to cope with that disruption include adopting strategies that prioritize agility, including capacity planning and risk diversification by leveraging multiple fulfillment partners, and strategic inventory positioning across regional warehouses to bypass bottlenecks caused by trade restrictions, JT Logistics said. And long-term resilience recommendations include scenario-based planning, expanded supplier networks, inventory buffering, multimodal transportation solutions, and investment in automation and AI for insights and smarter operations, the firm said.
“Navigating the complexities of tariff-driven disruptions requires forward-thinking strategies,” Cord said. “By leveraging predictive modeling, diversifying warehouse networks, and strategically positioning inventory, JT Logistics is empowering CPG brands to remain adaptive, minimize risks, and remain competitive in the current dynamic market."
With so many variables at play, no company can predict the final impact of the potential Trump tariffs, so American companies should start planning for all potential outcomes at once, according to a statement from Nari Viswanathan, senior director of supply chain strategy at Coupa Software. Faced with layers of disruption—with the possible tariffs coming on top of pre-existing geopolitical conflicts and security risks—logistics hubs and businesses must prepare for any what-if scenario. In fact, the strongest companies will have scenarios planned as far out as the next three to five years, Viswanathan said.
Grocery shoppers at select IGA, Price Less, and Food Giant stores will soon be able to use an upgraded in-store digital commerce experience, since store chain operator Houchens Food Group said it would deploy technology from eGrowcery, provider of a retail food industry white-label digital commerce platform.
Kentucky-based Houchens Food Group, which owns and operates more than 400 grocery, convenience, hardware/DIY, and foodservice locations in 15 states, said the move would empower retailers to rethink how and when to engage their shoppers best.
“At HFG we are focused on technology vendors that allow for highly targeted and personalized customer experiences, data-driven decision making, and e-commerce capabilities that do not interrupt day to day customer service at store level. We are thrilled to partner with eGrowcery to assist us in targeting the right audience with the right message at the right time,” Craig Knies, Chief Marketing Officer of Houchens Food Group, said in a release.
Michigan-based eGrowcery, which operates both in the United States and abroad, says it gives retail groups like Houchens Food Group the ability to provide a white-label e-commerce platform to the retailers it supplies, and integrate the program into the company’s overall technology offering. “Houchens Food Group is a great example of an organization that is working hard to simultaneously enhance its technology offering, engage shoppers through more channels and alleviate some of the administrative burden for its staff,” Patrick Hughes, CEO of eGrowcery, said.
The 40-acre solar facility in Gentry, Arkansas, includes nearly 18,000 solar panels and 10,000-plus bi-facial solar modules to capture sunlight, which is then converted to electricity and transmitted to a nearby electric grid for Carroll County Electric. The facility will produce approximately 9.3M kWh annually and utilize net metering, which helps transfer surplus power onto the power grid.
Construction of the facility began in 2024. The project was managed by NextEra Energy and completed by Verogy. Both Trio (formerly Edison Energy) and Carroll Electric Cooperative Corporation provided ongoing consultation throughout planning and development.
“By commissioning this solar facility, J.B. Hunt is demonstrating our commitment to enhancing the communities we serve and to investing in economically viable practices aimed at creating a more sustainable supply chain,” Greer Woodruff, executive vice president of safety, sustainability and maintenance at J.B. Hunt, said in a release. “The annual amount of clean energy generated by the J.B. Hunt Solar Facility will be equivalent to that used by nearly 1,200 homes. And, by drawing power from the sun and not a carbon-based source, the carbon dioxide kept from entering the atmosphere will be equivalent to eliminating 1,400 passenger vehicles from the road each year.”
As a contract provider of warehousing, logistics, and supply chain solutions, Geodis often has to provide customized services for clients.
That was the case recently when one of its customers asked Geodis to up its inventory monitoring game—specifically, to begin conducting quarterly cycle counts of the goods it stored at a Geodis site. Trouble was, performing more frequent counts would be something of a burden for the facility, which still conducted inventory counts manually—a process that was tedious and, depending on what else the team needed to accomplish, sometimes required overtime.
So Levallois, France-based Geodis launched a search for a technology solution that would both meet the customer’s demand and make its inventory monitoring more efficient overall, hoping to save time, labor, and money in the process.
SCAN AND DELIVER
Geodis found a solution with Gather AI, a Pittsburgh-based firm that automates inventory monitoring by deploying small drones to fly through a warehouse autonomously scanning pallets and cases. The system’s machine learning (ML) algorithm analyzes the resulting inventory pictures to identify barcodes, lot codes, text, and expiration dates; count boxes; and estimate occupancy, gathering information that warehouse operators need and comparing it with what’s in the warehouse management system (WMS).
Among other benefits, this means employees no longer have to spend long hours doing manual inventory counts with order-picker forklifts. On top of that, the warehouse manager is able to view inventory data in real time from a web dashboard and identify and address inventory exceptions.
But perhaps the biggest benefit of all is the speed at which it all happens. Gather AI’s drones perform those scans up to 15 times faster than traditional methods, the company says. To that point, it notes that before the drones were deployed at the Geodis site, four manual counters could complete approximately 800 counts in a day. By contrast, the drones are able to scan 1,200 locations per day.
FLEXIBLE FLYERS
Although Geodis had a number of options when it came to tech vendors, there were a couple of factors that tipped the odds in Gather AI’s favor, the partners said. One was its close cultural fit with Geodis. “Probably most important during that vetting process was understanding the cultural fit between Geodis and that vendor. We truly wanted to form a relationship with the company we selected,” Geodis Senior Director of Innovation Andy Johnston said in a release.
Speaking to this cultural fit, Johnston added, “Gather AI understood our business, our challenges, and the course of business throughout our day. They trained our personnel to get them comfortable with the technology and provided them with a tool that would truly make their job easier. This is pretty advanced technology, but the Gather AI user interface allowed our staff to see inventory variances intuitively, and they picked it up quickly. This shows me that Gather AI understood what we needed.”
Another factor in Gather AI’s favor was the prospect of a quick and easy deployment: Because the drones can conduct their missions without GPS or Wi-Fi, the supplier would be able to get its solution up and running quickly. In the words of Geodis Industrial Engineer Trent McDermott, “The Gather AI implementation process was efficient. There were no IT infrastructure or layout changes needed, and Gather AI was flexible with the installation to not disrupt peak hours for the operations team.”
QUICK RESULTS
Once the drones were in the air, Geodis saw immediate improvements in cycle counting speed, according to Gather AI. But that wasn’t the only benefit: Geodis was also able to more easily find misplaced pallets.
“Previously, we would research the inventory’s systemic license plate number (LPN),” McDermott explained. “We could narrow it down to a portion or a section of the warehouse where we thought that LPN was, but there was still a lot of ambiguity. So we would send an operator out on a mission to go hunt and find that LPN,” a process that could take a day or two to complete. But the days of scouring the facility for lost pallets are over. With Gather AI, the team can simply search in the dashboard to find the last location where the pallet was scanned.
And about that customer who wanted more frequent inventory counts? Geodis reports that it completed its first quarterly count for the client in half the time it had previously taken, with no overtime needed. “It’s a huge win for us to trim that time down,” McDermott said. “Just two weeks into the new quarter, we were able to have 40% of the warehouse completed.”