Contributing Editor Toby Gooley is a writer and editor specializing in supply chain, logistics, and material handling, and a lecturer at MIT's Center for Transportation & Logistics. She previously was Senior Editor at DC VELOCITY and Editor of DCV's sister publication, CSCMP's Supply Chain Quarterly. Prior to joining AGiLE Business Media in 2007, she spent 20 years at Logistics Management magazine as Managing Editor and Senior Editor covering international trade and transportation. Prior to that she was an export traffic manager for 10 years. She holds a B.A. in Asian Studies from Cornell University.
Try as they might, they couldn't figure it out. A distribution center in Florida was experiencing an unacceptably high rate of forklift-related product damage. The lift truck fleet had installed iWarehouse, a telematics solution from The Raymond Corp., and the fleet manager had asked the forklift maker for help in using the system to learn why there was so much damage. But the traditional observations—the time of day, where impacts were happening, and who was driving—didn't turn up any obvious reasons for the impacts. Puzzled, the truck manufacturer and its customer decided to look beyond the forklift operation for possible causes, recalls John Rosenberger, product manager for iWarehouse Gateway, the system's reporting user interface. Among the things they looked at was the general environment inside the building.
Where the DC is located in Florida, high humidity levels are common, so the facility monitors humidity levels and has dehumidifiers in place. That gave the team an idea: compare the relative humidity readings with the forklift impact records in the telematics system.
"Sure enough, they aligned, and we found the root cause of the impacts," Rosenberger says. On days when thunderstorms were rolling through, the humidity rose so quickly the dehumidifiers couldn't keep up. The concrete floors became wet, and for just an hour or two, the floor would be slippery. During those times, the drivers—who are paid on piecework, which motivated them to drive fast—were prone to sliding, which led to impacts and product damage.
With that information in hand, Raymond and its customer found a way to prevent sliding accidents. Now, when relative humidity exceeds a certain threshold, the DC manager uses the iControl function in iWarehouse to reduce the maximum speed of the trucks and then to raise it after the danger has passed. For lift trucks that do not have iControl, the system alerts drivers to slow down or speed up via a message on the iWarehouse monitor display on the truck. According to Rosenberger, accidents and product damage quickly declined, and the DC still meets its throughput goals despite the periodic speed reductions.
THE WMS/LMS CONNECTION
"The Case of the Slippery Floors" is a good example of how a "big data" approach can be applied to lift truck fleet management. "Big data" refers to the analysis of data from multiple sources, often unrelated and unstructured, to find hidden correlations and unseen cause-and-effect relationships. While a true big data analysis involves sifting through huge amounts of information, the big data concept can also be applied to analyses of much smaller amounts of information. On a small scale, this is more likely to involve a comparison of two data sets, which can help companies to start down the path of using data analysis to solve problems. "This is not about gathering new data," explains Roger Tenney, senior vice president, client services, for I.D. Systems Inc., a provider of wireless vehicle management systems. "Big data is about new ways of combining, integrating, and analyzing existing information from disconnected or apparently divergent data sources."
This type of analysis requires help from technology. Although spreadsheets and basic databases are useful in collecting and sorting fleet operating and maintenance data, it can be a cumbersome, slow process to enter data from different sources, sort it, visually identify patterns, and then figure out the correlations. Fleet and battery management, maintenance tracking, and asset tracking software—not just those mentioned in this article but also the many other programs that are on the market—are designed to gather, compare, and analyze data from multiple sources. A big data analysis requires a certain degree of technological sophistication, so fleet managers shouldn't be reluctant to ask for help. The lift truck manufacturer, the software provider, and in some cases, an outside data management consultant or an in-house systems analyst can assist with identifying which data are relevant, determining how best to "harvest" it, and then conducting an analysis.
A big data analysis might look at information sources that are related but traditionally are examined independently. For example, lift truck, battery, and charger performance usually are reviewed separately. But a big data analysis that treats them as "a holistic system" will allow fleet managers to see patterns that would not be apparent otherwise, says Harold Vanasse, vice president of sales and marketing for Philadelphia Scientific, a provider of battery management technologies. Some of his customers match their battery usage and handling data with lift truck manufacturers' data collection and analysis systems, such as InfoLink from Crown Equipment and iWarehouse from Raymond, Vanasse says. "They may look at changes in run times and utilization of batteries with our system, then look at the fleet's performance. They can then match up the activity of a truck [powered by] a particular battery with that battery's performance" to find out whether one is affecting the other, he explains.
Or, like the humidity example above, it may involve analyzing data sources that appear to be unrelated. Another example: An analysis of a Raymond customer's maintenance and repair data showed that some trucks were suffering damage to drive wheels and tires, while others were not. A look at the damaged trucks' daily activities found that they all had been driving over a malfunctioning dock plate. The DC's managers were aware of the faulty plate and had planned to replace it when the next year's facility-maintenance budget was released. But because building maintenance and fleet maintenance had separate budgets, nobody knew until it was revealed by the analysis that driving over the dock plate was directly responsible for some $1,000 a month in truck repairs, Rosenberger says. Immediately replacing the dock plate would be more cost-effective than waiting for the following year's budget to kick in.
In that particular case, the customer was able to track down the problem because it assigned drivers and trucks to specific dock areas. But a company that does not follow that approach could use information from its warehouse management system (WMS) to see which jobs directed operators through a particular dock or other section of a warehouse, Rosenberger notes.
A WMS can be an invaluable source of information for this type of analysis. One of Philadelphia Scientific's customers, for instance, was experiencing a reduction in the number of picks per hour. Around the same time, managers noticed that drivers were changing batteries more frequently than would have been expected. Using its WMS, the company saw a correlation between the frequency of battery changes and reduction in hourly picks. The problem, it turned out, was that operators, who were paid by the piece, wanted to make the quickest possible change and get back out on the floor. As a result, some would grab the closest battery rather than ones that were fully charged and fully cooled down. The batteries did not last a full shift, and drivers lost time in the changing room. After getting rid of the older batteries and putting in a battery-tracking system, the DC achieved a 35-percent reduction in battery changes while order picks per hour quickly rose, Vanasse relates.
Tenney says some of I.D. Systems' customers have analyzed fleet telematics and maintenance data in concert with information from their labor management systems (LMS) and timekeeping modules like a payroll log to track down productivity-busters. One grocery distributor used that approach to identify the source of performance variances among lift truck drivers. "Big data can be used very effectively to identify who's falling behind, including looking at what are the four or five attributes that define an operator. Then you can break that down into what he or she is good or bad at," he says. The point is not to punish, but to "be able to look at productivity from all viewpoints and angles within how a job is done." That analysis allowed the customer to identify training program enhancements that helped operators become more effective. Before long, the grocery distributor increased throughput by 15 percent with the same operators and vehicles, according to Tenney.
PREVENTIVE ACTION
Big data analysis and correlation is not always about solving problems. It can also be an effective tool for improving current practices. For example, previously established time standards may suggest that a certain number of order pickers are needed for a particular shift. But correlating WMS data (what needed to be accomplished) with lift truck telematics (how long it actually took) over time may show that the standards in a labor management system (LMS) are no longer accurate, Tenney says.
Integrating data from different data sources can be useful for predicting the future, too. One I.D. Systems customer, a large consumer products supplier to a Fortune 10 company, worked backward from significant repair events to identify patterns in the types of activities that occurred prior to those repairs. "It allows you to say, for example, that when these four things happen, three months later, this problem happens," Tenney explains. Because the customer was able to identify the common thread among unrelated events, it is now able take action before a major failure occurs.
Applying big data analysis to lift truck fleet management is neither easy nor simple. It also takes time, since any analysis must consider large quantities of data over a lengthy period to find and validate patterns. But as the examples in this article show, the payoff in terms of problem solving or prevention could make it well worth the effort.
Supply chain planning (SCP) leaders working on transformation efforts are focused on two major high-impact technology trends, including composite AI and supply chain data governance, according to a study from Gartner, Inc.
"SCP leaders are in the process of developing transformation roadmaps that will prioritize delivering on advanced decision intelligence and automated decision making," Eva Dawkins, Director Analyst in Gartner’s Supply Chain practice, said in a release. "Composite AI, which is the combined application of different AI techniques to improve learning efficiency, will drive the optimization and automation of many planning activities at scale, while supply chain data governance is the foundational key for digital transformation.”
Their pursuit of those roadmaps is often complicated by frequent disruptions and the rapid pace of technological innovation. But Gartner says those leaders can accelerate the realized value of technology investments by facilitating a shift from IT-led to business-led digital leadership, with SCP leaders taking ownership of multidisciplinary teams to advance business operations, channels and products.
“A sound data governance strategy supports advanced technologies, such as composite AI, while also facilitating collaboration throughout the supply chain technology ecosystem,” said Dawkins. “Without attention to data governance, SCP leaders will likely struggle to achieve their expected ROI on key technology investments.”
The British logistics robot vendor Dexory this week said it has raised $80 million in venture funding to support an expansion of its artificial intelligence (AI) powered features, grow its global team, and accelerate the deployment of its autonomous robots.
A “significant focus” continues to be on expanding across the U.S. market, where Dexory is live with customers in seven states and last month opened a U.S. headquarters in Nashville. The Series B will also enhance development and production facilities at its UK headquarters, the firm said.
The “series B” funding round was led by DTCP, with participation from Latitude Ventures, Wave-X and Bootstrap Europe, along with existing investors Atomico, Lakestar, Capnamic, and several angels from the logistics industry. With the close of the round, Dexory has now raised $120 million over the past three years.
Dexory says its product, DexoryView, provides real-time visibility across warehouses of any size through its autonomous mobile robots and AI. The rolling bots use sensor and image data and continuous data collection to perform rapid warehouse scans and create digital twins of warehouse spaces, allowing for optimized performance and future scenario simulations.
Originally announced in September, the move will allow Deutsche Bahn to “fully focus on restructuring the rail infrastructure in Germany and providing climate-friendly passenger and freight transport operations in Germany and Europe,” Werner Gatzer, Chairman of the DB Supervisory Board, said in a release.
For its purchase price, DSV gains an organization with around 72,700 employees at over 1,850 locations. The new owner says it plans to investment around one billion euros in coming years to promote additional growth in German operations. Together, DSV and Schenker will have a combined workforce of approximately 147,000 employees in more than 90 countries, earning pro forma revenue of approximately $43.3 billion (based on 2023 numbers), DSV said.
After removing that unit, Deutsche Bahn retains its core business called the “Systemverbund Bahn,” which includes passenger transport activities in Germany, rail freight activities, operational service units, and railroad infrastructure companies. The DB Group, headquartered in Berlin, employs around 340,000 people.
“We have set clear goals to structurally modernize Deutsche Bahn in the areas of infrastructure, operations and profitability and focus on the core business. The proceeds from the sale will significantly reduce DB’s debt and thus make an important contribution to the financial stability of the DB Group. At the same time, DB Schenker will gain a strong strategic owner in DSV,” Deutsche Bahn CEO Richard Lutz said in a release.
Transportation industry veteran Anne Reinke will become president & CEO of trade group the Intermodal Association of North America (IANA) at the end of the year, stepping into the position from her previous post leading third party logistics (3PL) trade group the Transportation Intermediaries Association (TIA), both organizations said today.
Meanwhile, TIA today announced that insider Christopher Burroughs would fill Reinke’s shoes as president & CEO. Burroughs has been with TIA for 13 years, most recently as its vice president of Government Affairs for the past six years, during which time he oversaw all legislative and regulatory efforts before Congress and the federal agencies.
Before her four years leading TIA, Reinke spent two years as Deputy Assistant Secretary with the U.S. Department of Transportation and 16 years with CSX Corporation.
Serious inland flooding and widespread power outages are likely to sweep across Florida and other Southeast states in coming days with the arrival of Hurricane Helene, which is now predicted to make landfall Thursday evening along Florida’s northwest coast as a major hurricane, according to the National Oceanic and Atmospheric Administration (NOAA).
While the most catastrophic landfall impact is expected in the sparsely-population Big Bend area of Florida, it’s not only sea-front cities that are at risk. Since Helene is an “unusually large storm,” its flooding, rainfall, and high winds won’t be limited only to the Gulf Coast, but are expected to travel hundreds of miles inland, the weather service said. Heavy rainfall is expected to begin in the region even before the storm comes ashore, and the wet conditions will continue to move northward into the southern Appalachians region through Friday, dumping storm total rainfall amounts of up to 18 inches. Specifically, the major flood risk includes the urban areas around Tallahassee, metro Atlanta, and western North Carolina.
In addition to its human toll, the storm could exert serious business impacts, according to the supply chain mapping and monitoring firm Resilinc. Those will be largely triggered by significant flooding, which could halt oil operations, force mandatory evacuations, restrict ports, and disrupt air traffic.
While the storm’s track is currently forecast to miss the critical ports of Miami and New Orleans, it could still hurt operations throughout the Southeast agricultural belt, which produces products like soybeans, cotton, peanuts, corn, and tobacco, according to Everstream Analytics.
That widespread footprint could also hinder supply chain and logistics flows along stretches of interstate highways I-10 and I-75 and on regional rail lines operated by Norfolk Southern and CSX. And Hurricane Helene could also likely impact business operations by unleashing power outages, deep flooding, and wind damage in northern Florida portions of Georgia, Everstream Analytics said.
Before the storm had even touched Florida soil, recovery efforts were already being launched by humanitarian aid group the American Logistics Aid Network (ALAN). In a statement on Wednesday, the group said it is urging residents in the storm's path across the Southeast to heed evacuation notices and safety advisories, and reminding members of the logistics community that their post-storm help could be needed soon. The group will continue to update its Disaster Micro-Site with Hurricane Helene resources and with requests for donated logistics assistance, most of which will start arriving within 24 to 72 hours after the storm’s initial landfall, ALAN said.