Materials Handling Meets Big Data

The supply chain sector is buzzing about big data. At any logistics association conference, the topic of what to do with the large and complex sets of data most companies are now collecting is bound to come up. And now, big data is gaining ground in warehouses and distribution centers, too—places that were once a black hole of information.

"Historically, when product moved from a trailer into a warehouse, it was as if the shipment entered a big blank box you couldn’t see into," says Jim Gaskell, director of global Crown Insite products for Crown Equipment, a materials handling equipment manufacturer with U.S. headquarters in New Bremen, Ohio. "Today, companies need to continue to monitor those products—and the equipment and labor handling them—throughout the warehouse."

Many businesses are accomplishing that feat through big data.

Warehouses and distribution centers are often as high-tech as the products they store and send out to consumers. With the increase of automated materials handling systems and equipment—as well as technology solutions that manage labor, inventory, and equipment in the distribution center—warehouses now generate a wealth of information. The abundance of data these new "smart" warehouses generate is helping companies improve facility, labor, and equipment productivity; increase safety; boost throughput and inventory accuracy; and prolong the life of key equipment such as forklifts, conveyors, sorters, totes, and racks.

Keeping Up With the Times

This evolution of warehouse data has occurred rapidly over the past few years, and mirrors the drastic advancement that has transformed the consumer landscape.

"It shouldn’t surprise us that forklifts, for instance, can now deliver actionable usage and battery life data right from their on-board technology systems," Gaskell notes, citing the connectivity and information resources that are now the norm in both smartphones and consumer vehicles. "It is natural that these technology and data enhancements should occur in distribution, too."

The trend started with over-the-road trucks, which have been equipped with telematics solutions and tracking sensors since about 2000. These solutions provide data that helps companies effectively manage fleets and drivers—a concept now being applied to materials handling fleets in warehouses around the globe.

Take, for example, Owens & Minor, a national distributor of name-brand medical and surgical supplies. In its Richmond, Va., distribution center, the company uses Crown Equipment’s wireless forklift fleet and operator management system InfoLink to manage more than 30 electric forklifts. The solution provides actionable data collected from forklift sensors that are able to track an array of factors, including battery usage, impact history, truck utilization, OSHA compliance, and service needs.

The solution helps forklifts perform like "smart" trucks, providing valuable data accessible through interactive dashboards. The setup helps fleet operators focus on exceptions and opportunities instead of digging through mounds of data to identify key trends.

Since deploying InfoLink in 2010, Owens & Minor has achieved greater visibility into its forklift fleet operation, allowing it to discover—and correct—various fleet utilization inefficiencies. Just one month after installing the system, the company determined that two of its stockpicker forklifts were not being utilized, and could be removed from the fleet.

"Another surprising fact the data revealed was the narrow window of time during which we used the lift trucks each day," explains Ron Smarsh, general manager of the Richmond distribution center. "Four months into the installation, we were able to make some minor operational adjustments to eliminate two more trucks."

In addition to right-sizing its fleet, Owens & Minor’s Richmond DC used the data InfoLink provided to track and improve workplace safety regulations compliance and impact monitoring. The company has expanded its use of InfoLink to its Baltimore, Detroit, and Louisville facilities, where it will use the data for similar purposes.

Right Data, Right Decisions

Accessing the right information to make smart decisions in the warehouse is one main reason why the demand for big data has grown so much—and so rapidly—in the distribution sector.

"In the past, warehouse operators weren’t too concerned about equipment maintenance data, how many scans were done on a line, or how many orders were changed at the last minute on a conveyor," says Greg Cronin, executive vice president of Intelligrated, a materials handling automation solutions provider based in Mason, Ohio. "But today, companies want every piece of data available. They also want the data stored for a long time so they can access and analyze it."

The type of data companies are seeking has also changed. A few years ago, most companies were focused on analyzing basic data such as the number of orders handled in a certain timeframe, and comparing the data across a network of distribution sites. This would simply help companies gauge how well each DC was faring, and whether locations were hitting their targets.

Now, companies want data that can provide real business intelligence—actionable information they can use to improve warehouse efficiency and productivity.

"We pass an array of materials handling data to companies because they want to establish trends and patterns," says Cronin. Businesses want detailed specifics such as the number of diversions on a conveyor, and conveyor motor performance data.

Drilling down to such minute details allows companies to use historical data to find key trends and patterns that can help them better understand their businesses, and make important changes. The historical data, in turn, becomes predictive, allowing companies to plan for likely events before they even happen.

"Using big data correctly helps companies allow for more flexible planning and predictive analysis in the warehouse," explains Mark Dickinson, executive sales manager, automated systems division at SSI Schaefer, a global storage and picking solutions manufacturer with U.S. headquarters in Charlotte, N.C.

Carefully analyzing labor performance metrics, for instance, can help companies predict labor needs during specific periods.

"If a company runs 50,000 lines in X number of hours with X number of employees, the warehouse manager can break that down on a time scale to predict the amount of time and labor the operation will need to handle whatever volume comes in next," Dickinson explains. "The manager can call in backups or ask employees to work overtime if they are expecting higher volumes."

This has been especially important for e-commerce companies whose DCs handle multi-line orders. Complex orders with multiple items pose a challenge in how to best set up the warehouse and position inventory for maximum effectiveness, as well as what type of materials handling equipment should be used.

"In a large DC for a major e-commerce or multi-channel retailer, it’s not unusual for one order to contain two items that are slotted on opposite ends of the warehouse," Cronin explains. "Retailers must find the most cost-effective way to bring the two items together into one box to be shipped to the customer.

"A lot of the big data being accumulated now is for those purposes," he adds. "It helps companies determine how to optimally position goods that are often sold together so they can be picked in the same box, and don’t have to be brought together across thousands of square feet of warehouse space."

For industries that typically operate in a "pull" environment, collecting big data on warehouse and materials handling operations is crucial to helping make smart decisions on the fly.

The wine and spirits industry, for instance, is always at the mercy of unpredictable demand from its restaurant and bar customers, notes Chris Castaldi, manager of business development for W&H Systems, a Carlstadt, N.J.-based materials handling systems integrator.

"Companies in the wine and beverage industry are looking for a lot of materials handling data to maximize efficiency in their warehouses, because they have little control over what they ship daily," Castaldi explains.

But, he says, companies have moved past the stage of just wanting data for data’s sake—they now demand that their partners help them use that data to make smart decisions. "Warehouse operators are asking their data suppliers what their facilities need to do to meet demand, how many workers they need, and how much usage their equipment will get," Castaldi says.

Sensors, Scanners, and Systems

Sensors and detectors play a key role in gathering the data companies seek. Placed in various locations on forklifts, conveyors, sorters, and other mechanical equipment in distribution centers, sensors and detectors capture a wide range of operational data. They are crucial for turning materials handling equipment into smart systems that don’t simply perform tasks, but also help collect and disseminate crucial information about warehouse operations and productivity.

"We use sensors and data collection devices—combined with location tracking technology and sophisticated software—to provide not only various types of safety, equipment monitoring, and fleet management solutions, but also labor productivity, automated inventory tracking, and, ultimately, centralized control of manned and unmanned vehicles," explains Phil Van Wormer, executive vice president of TotalTrax, a Newport, Del.-based provider of tracking technologies for manufacturing and warehouse operations.

Equipping forklifts with data-collecting sensors is often the easiest place to start, Van Wormer notes. Impact sensors—to detect when a forklift bumps into something—and load sensors, which help to measure distance traveled with and without a load, are examples of "simpler applications that are delivering valuable insights to warehouse operators," he explains.

Usually, the forklift fleet is connected via a wireless network, and the data collected by sensors is stored either on a local or centralized server, or hosted remotely by a third party. Reporting packages from vendors such as TotalTrax allow companies to access a customized dashboard so they can view the specific forklift-generated data they are interested in, in whatever format they want.

Sensors are also crucial when it comes to gathering data from order-picking systems. Many automated warehouse systems provide data on details such as whether a tote is in transit or is stationary in front of an operator; the time it takes for an operator to perform a pick and place an item into the tote; and how long an operator is waiting for the tote to advance and the next pick to take place.

This type of data is typically time-stamped, as well. "By capturing when picks are performed, users can query that data and run reports on historical metrics," explains Dickinson. "This is helpful for determining operator and equipment productivity levels, and comparing them to forecasts and projections."

Putting the Data to Work

One popular way to use data collected by lift trucks and conveyor and sortation systems is to improve warehouse safety and efficiency.

For example, at Golden Guernsey Dairy, Wisconsin’s top milk producer, forklift data helped reign in reckless forklift driving. Although the company always emphasized safety in its 150,000-square-foot facility, accident rates among its 100 operators were higher than the company could tolerate, and the damage to its forklift fleet was generating high maintenance costs.

Golden Guernsey Dairy needed a way to track the accidents and change operator behavior. The company turned to a TotalTrax product called ImpactManager ID, a forklift impact detector that mounts on the exterior of the forklift and sounds an alarm when an impact occurs.

The device reports each incident, and the offending operator’s identity, through an internal network. This data allows the company to track when and how accidents occur, and which drivers need to be disciplined or retrained.

In addition, knowing that the equipment is monitored causes operators to drive at safer speeds, resulting in significantly fewer incidents and a safer environment for workers. In fact, four months after equipping all 11 of its forklifts with the TotalTrax monitoring system, the number of monthly impacts fell from 1,036 to just four.

Helping Reduce Abuse

This approach has also helped The Scotts Company—a division of Marysville, Ohio-based lawn and garden products company Scotts Miracle-Gro—to reduce truck abuse, accidents, and maintenance costs. In its Marysville manufacturing and distribution facility, The Scotts Company selected ImpactManager RF, a wireless TotalTrax system that can be used on all of Scotts’ mobile equipment, including sweepers, lift trucks, front-end loaders, and several track mobiles that move rail cars.

Data from the ImpactManager RF units helped The Scotts Company decrease damage involving its materials handling equipment by 75 percent. And, by monitoring activity for all drivers and vehicles, the company’s management has access to data and reports not only for vehicle impacts, but also for additional training needs, utilization opportunities, and OSHA compliance.

Changing operator behavior is key to making these kinds of improvements. "Telematics solutions help organizations identify inefficiencies and improve them," Gaskell says. "In the warehouse, it often comes down to changing driver behavior."

For instance, a Warehouse Management System (WMS) may help a company track how long it should take to move product from the dock to the rack via forklift, but it doesn’t explain why that time parameter is not always met.

"It may take some operators 10 minutes and others 20 minutes, but sometimes it only takes five minutes. Without the data from the forklift, it is impossible to identify the right time," Gaskell says. "The data collected by the lift truck will show that the driver who took 20 minutes drove five miles to get to the rack, while the other driver drove half the distance. Or maybe one operator was on the forklift the whole time, and the other was not."

By analyzing this type of data, companies can identify inefficiencies, then retrain employees to correct those errors.

Spotlight on Productivity

Improved productivity and asset management is another important benefit companies reap from properly utilizing big data from materials handling equipment. Crucial warehouse equipment such as sorters, conveyors, and rack systems are large—and expensive—assets that cannot be changed on the fly to flex with demand. That’s why warehouse operators constantly seek to maximize productivity and optimize the fixed physical assets in their facilities.

"Being able to properly predict and change processes and labor to maximize asset utilization is crucial in DCs," says Castaldi. "Big data is a huge help in accomplishing those goals."

Companies need to zero in on finding the appropriate data that can help them make crucial decisions and changes that maximize materials handling system efficiency. Warehouse control systems (WCS) make this task easier.

A WCS acts as a traffic cop of sorts, ensuring that operations run smoothly within the DC’s four walls. It also tracks data such as which equipment is available to work on; how many people are working and where; and the performance rate of equipment and employees, including details such as the number of diverts per hour, and whether that figure meets or exceeds the company’s specified standards.

"Companies that collect this type of data can make decisions on the fly that boost productivity and asset utilization," Castaldi notes. "For instance, if too many employees are working on one task, productivity rates decrease. Using that data, a company can quickly determine that employees can be put to better use doing something else."

The idea is not to wait and analyze this type of data after one shift, or one day, or one week, but to have instant access and visibility throughout the process.

That type of data-fueled productivity visibility has been a boon for Swanson Health Products, a Fargo, N.D.-based online and catalog retailer of vitamins and natural health products. The company embraced automation in its Fargo distribution center in 2006, installing an automated solution from SSI Schaefer that includes carousels, an A-frame picking system, a conveyor system, and miniload system used to store large numbers of small items.

Thanks to the data each of these systems puts out, Swanson is able to optimize the use of each piece of equipment, as well as warehouse labor.

"We look closely at the lines-per-hour data the equipment provides. Based on that information, we know the capacity of each system," explains Troy Ruscheinsky, project manager, Swanson Health. "The capacity information, in turn, tells us how much labor we need in the warehouse, and allows us to budget for workers throughout each season."

Budgeting for labor is crucial for the company, especially during its busy season, which occurs during the first quarter of each year, when many consumers are focused on sticking to their New Year’s resolutions to stay healthy. Because Swanson does extensive marketing during this time, it must be able to deliver on the increased demand by fulfilling orders quickly and accurately.

When managing the changing throughput, the company relies on the "mins and maxes" data from its materials handling systems to ensure the system is loaded properly.

"Loading SKUs into each system is a juggling act," Ruscheinsky says. "Each piece of equipment performs a different task, and we have fast-moving, medium-moving, and slow-moving items. If we overload, productivity suffers.

"Gathering data from our systems tells us exactly what each piece of equipment, workstation, and employee can handle, which helps us determine how to load the system appropriately for the throughput we need to manage," Ruscheinsky says.

Whether it is used for labor planning, system performance benchmarking, optimizing equipment usage and maintenance, maximizing productivity, boosting safety, or creating the best inventory flow in the warehouse, big data in materials handling is helping supply chain professionals make smarter operational decisions.