Supply Chain Management: It’s IoT Time
Companies keep finding new ways to gain supply chain improvements with help from the Internet of Things.
Every day, more data than we can imagine zings across that global conduit we call the Internet. Much of that data comes directly from devices, without human intervention.
Drivers carry cell phones in their cars to feed GPS locations to a traffic information and navigation service, which uses the data to detect congestion. A home security system in Michigan alerts the resident, wintering in Florida, that someone is lingering at the front door. An electronic chair in a dentist’s office tells the manufacturer it’s time to perform preventive maintenance.
The Internet of Things (IoT) is making its way into every aspect of life. By 2021, 22.5 billion devices will be connected to the IoT, compared with 6.6 billion in 2016, according to a BI Global Intelligence survey. The world will invest $4.8 trillion in IoT technologies and products during that time, the company forecasts.
The future of IoT is intimately bound with the future of the supply chain. Already, applications are emerging to capture data from “things” equipped with sensors, barcode labels, GPS antennas, and other devices, move that data across the Internet, and use it to improve supply chain operations. IoT technology can help companies manage transportation fleets, inventory, and warehouse operations; make replenishment decisions; refine delivery routes; generate better demand forecasts; and a good deal more.
ROBOTS ON THE GRID
At Radwell International in Willingboro, N.J., the “things” that communicate over the Internet include 34 robots that swarm across a three-dimensional storage grid, putting away and picking product. The AutoStore robotic system comes from Swisslog, a Swiss firm with U.S. headquarters for its warehouse distribution systems in Newport News, Va.
Radwell International sells new and surplus equipment used in manufacturing plants and facilities maintenance. It implemented AutoStore in 2016 to gain high-density storage and fast picking, says Brian Janusz, global program manager at Radwell. AutoStore keeps product in bins, which the robots place at various locations within the grid, sharing that information with Radwell’s warehouse management system (WMS).
One of AutoStore’s big benefits is the way it continuously repositions the bins. “Over time, the fast movers rise to the top of the system, and the slow moving products sink to the bottom,” Janusz says. That shortens the time required to pick high-demand products.
Swisslog calls that strategy “prebubbling,” says A.K. Schultz, the company’s vice president, e-commerce and retail. AutoStore receives data on customer orders from an order management system or WMS, and transmits it to the robots to let them know which products they’ll be picking in the coming hours. The robots move those products into more accessible positions before the busy period arrives.
Schultz compares this process to Waze, a trip-routing service that uses GPS data from thousands of vehicles to determine real-time traffic conditions and help drivers avoid congestion. “It’s a fully integrated, real-time use of sensors to alter the destiny of what you’re doing,” he says.
“Before AutoStore, if I wanted to reorganize the warehouse physically, I had to spend months, and lots of manpower, to bring products up front into the optimum position,” Janusz says. AutoStore does this work continuously.
Because sensors on the robots send data to Radwell’s information systems, AutoStore also tells the company how often robots touch each bin. “That provides immense benefit by letting us know which items are selling and which are not,” Janusz says. “We can then stock accordingly.”
Data passing between the robots and the system also help to keep AutoStore up and running. “The robots know when they need a charge, and then they move off to be charged on their own,” Schultz says. If the workload surges, the system might command a robot to get just a top-off, rather than a full charge, so it can return to work quickly.
“We also have the ability to monitor the status of the robots remotely,” Schultz says. If a problem appears—for instance, if a slowdown in the facility’s network infrastructure affects the robots—often Swisslog can put a solution in place before the customer even knows there’s anything wrong.
LIFT TRUCK NETWORK
Collecting data from sensors to monitor activities in real time is valuable, of course. But the power of IoT doesn’t lie just in tracking a collection of discrete units, such as lift trucks in a warehouse.
“It’s the interaction between the sensors, the server, and other servers that bring the whole Internet of Things to bear,” says Neil O’Connell, senior vice president, technology, innovation and product development at TotalTrax in Newport, Del. “The network effect is greater than any one thing inside it.”
The TotalTrax SX/VX Advanced Telematics Platform uses sensors on lift trucks to capture data on factors such as motion, distance and direction traveled, impacts, raising and lowering of the forklift, and whether there’s a pallet on the lift. That data crosses a wireless network to a server, which uses the data in applications for fleet management, labor management, and maintenance management.
“It gets exciting when that data is accumulated and can either trigger actions, predict actions, or prevent consequences,” O’Connell says.
For instance, by collecting data every time a truck collides with an object on the warehouse floor, the system identifies hazardous intersections. Then, tracking a truck in motion around the warehouse, the system alerts the driver to any upcoming hazards, via a monitor mounted on the truck.
Users also can configure the system to issue alarms. For example, if a truck hits an object with substantial force, it might send a text or e-mail to a supervisor.
In addition, the system can help make sure that each truck receives preventive maintenance as needed. Rather than bringing in each truck every 30 days to change the oil, check fluid levels, and perform other routine procedures, TotalTrax can monitor distance traveled, how many pallets the truck lifted, and other factors to tailor the best maintenance schedule for each vehicle.
Users also employ the system to monitor productivity. The Advanced Telematics Platform continually measures each truck’s activity, and an optional sensor indicates when the forklift is carrying a pallet.
Beyond simply calculating how much work each truck and driver performs, the Advanced Telematics Platform can help a company right size its fleet. One graph the system produces indicates how many trucks the facility uses over 24 hours, in half-hour increments.
“The system provides amalgamated statistics over one month, one year, or multiple years, which tells you quickly, for example, that your peak was 50 trucks, your average was 30, and your minimum was 15,” O’Connell says.
Using that data, managers might decide that instead of owning 50 trucks, they should own 30, and then lease an extra 20 in December to handle peak season activity, he adds.
URBAN TRAFFIC AND THE LAST MILE
At the Massachusetts Institute of Technology (MIT), the Megacity Logistics Lab at the MIT Center for Transportation and Logistics has used IoT technology to help improve last-mile delivery routes in major cities.
The lab conducted tests in 2016 in São Paolo, Brazil, with Anheuser-Busch InBev (ABI) and with B2W, Brazil’s largest e-commerce company. In each case, researchers combined location data collected from delivery trucks, and data from mobile devices carried by drivers, with company data on orders, deliveries, and delivery attempts, plus public data on factors such as population density and road infrastructure.
The goal was to learn what keeps drivers from making their deliveries according to plan, and then put that knowledge to work.
“With B2W, for instance, we designed an optimization that would help them redesign their urban distribution network,” says Matthias Winkenbach, director of the Megacity Logistics Lab. “With ABI, we took various sources of data together to identify logistics-critical areas within that city, so they would know which areas to focus on when they were piloting new delivery models or changing the way they serve customers.”
One finding the study revealed is that even the most widely used route planning solutions make imprecise assumptions when they estimate travel times in cities. That’s because they misjudge the complexity of urban roadways. “For instance, they underestimate the detours that vehicles have to make in a city’s most congested and dense areas,” Winkenbach says.
By combining GPS data from actual trips with data from Google Maps and other public sources, the MIT team was able to quantify travel times at a much higher level of accuracy. “We can tell them, for every square kilometer, the detour factor to take into account when coming up with the true distance and the true time needed,” he says.
Route planning systems also tend to discount variability. “When they plan their logistics operations, most people work with average value assumptions,” Winkenbach says. “They look at an average day with average traffic and average demand.” Then, when traffic grows extra heavy, or order volumes peak, deliveries fall behind the plan.
Using data derived from the São Paolo tests, MIT researchers hope to help companies incorporate uncertainty in their planning models. They might use different kinds of vehicles to negotiate different kinds of traffic. Or they might store inventory in satellite locations to reach customers more easily despite congested routes.
TRUCKERS CHECK IN
One of IoT’s great promises lies in using mobile devices, such as truck drivers’ smart phones, to monitor the status of freight in transit. “Almost everyone today has a smart phone,” says Greg Braun, senior vice president at C3 Solutions. “You can leverage that to the nth degree for capturing data.”
As a developer of yard management and dock scheduling software, Montreal-based C3 has focused its own IoT initiative on the interface between driver and shipper or consignee. C3 has developed a free app, downloadable on any iOS or Android phone, that a driver can use to do an advance check-in for loading or unloading at a dock, much as a traveler might check in for an airline flight.
“The distribution center is expecting the drivers, and because they have checked in on the app, DC workers see their ETA,” Braun says. “As drivers get closer to the facility, workers can pre-allocate the parking area.” That might be a door, or a parking spot in the yard.
Most likely, the drivers have also provided security information in advance. “So when they show up—similar to airport security—the security guard at the distribution center will just confirm the information already provided,” he says. Then drivers follow the instructions to the assigned parking spot.
If drivers are also scheduled to pick up an outbound load, the app allows them to go elsewhere for a while, rather than wait in the DC. The app provides load status updates, so they can return to the DC when the shipment is ready.
WHERE’S THE WASHING MACHINE?
Kenco Logistics in Chattanooga, Tenn., recently implemented two versions of an IoT technology, one for tracking product in its warehouses, and the other for tracking leased material handling equipment. The technology, developed by Locatible, a Dublin firm with offices in Chattanooga, is similar to radio frequency identification (RFID) technology but uses different technology—Bluetooth and Wi-Fi—and costs less to deploy, says Kristi Montgomery, Kenco Logistics’ vice president of innovation.
Locatible’s tracking devices are tags that come in two varieties. Passive tags transmit data only when they’re close to a reader. Active tags transmit data to a server periodically, as defined by the user. In Kenco’s case, it’s every 15 minutes.
Kenco tested the passive tags on large appliances in a 40,000-square-foot section of a Kenco warehouse. The goal was to track the location of each appliance, which Locatible can do to within 5 centimeters.
“When the material handling truck picks up a group of appliances, a reader mounted on the truck reads the tag on each unit,” Montgomery says. Comparing xyz coordinates for that location with a schematic drawing of the facility, the system calculates where the appliances are. The same thing happens when the truck places the product in a new location.
The system also notices mistaken moves. “If an operator picks up an appliance and moves it more than 5 centimeters from the location where it’s supposed to be, they’ll immediately get an alert on the tablet device mounted to the material handling equipment,” Montgomery says. “It won’t let operators do another task until they put it back in the right location.”
Although that feature helps to avert errors, Kenco’s main goal is to reduce labor costs. “I know exactly where every product is within the confines of my building, so I shouldn’t have to do cycle counts or a physical inventory regularly,” Montgomery says.
Having proven the technology in three aisles and dock staging areas, Kenco is now implementing it throughout the 750,000-square-foot facility.
Kenco Fleet Services Division is already using Locatible’s active technology to keep tabs on equipment it sends out for short-term rentals. “We’re able to track that inventory at a granular level—how often it’s coming and going, even how many times it’s moving in and out of the maintenance shop, to get an idea of how often we maintain that piece of equipment,” Montgomery says. The active tags don’t report location as precisely as the passive tags, but that isn’t crucial for this use, she says.
In another IoT development, in 2017 Kenco challenged three teams of students at the local science, technology, engineering, and math (STEM) high school to develop a way to warn supervisors visually when trucks have been parked at dock doors so long that Kenco might incur retention fines.
“The students are developing a sensor-based solution that would display red, yellow or green and a timer clock,” Montgomery says. Yellow means the truck is due to leave soon; red means it has missed its deadline. Kenco will choose one solution for further development, including integration with its warehouse and yard management systems.
While some companies focus their IoT efforts on transportation and logistics, others are peering all the way down the supply chain to the end user. One of those is supply chain service provider ModusLink, which is developing new applications for Poetic, an IoT platform it acquired in 2008 along with a company called Open Channel Solutions (OCS).
Poetic was first developed to manage software licenses and entitlements. Today, ModusLink is adding applications that would, for example, track how an end consumer uses a product, generating both marketing and replenishment data.
One of the companies exploring Poetic currently sells coffee by subscription, sending new shipments at regular intervals. “But we’re working with them to track actual consumption and move to a consumption-based replenishment model,” says Neil Hampshire, chief information officer at ModusLink.
Under that model, a smart coffeemaker—one designed for the home, or for a business setting such as a company break room—monitors how much coffee the customer uses and then, when supplies run low, automatically orders more.
Opportunities for automated replenishment go far beyond coffee. “If you abstract that into the business-to-business world, any consumable items that can be tracked and measured in a device could be replenished,” Hampshire says. For example, a smart printing press might reorder its own ink.
Beyond helping to streamline replenishment—and giving more brand owners a way to sell directly to customers—a platform such as Poetic can generate a great deal of business intelligence.
“Five or 10 years ago, for large consumer products companies, the Holy Grail was increased point-of-sale data, gaining visibility into what retailers were selling to consumers, and using that to enhance replenishment,” Hampshire says. But consumer appliances equipped with smart sensors will provide even better data.
“You can drill down into Mr. Smith’s coffee machine at 240 Acacia Avenue and see exactly what he has been doing,” Hampshire adds. Aggregating the data from thousands of customers, you could, for example, find out if people who drink coffee before 9 a.m. respond differently to marketing campaigns than those who drink it between 9 and 11:30.
AN EYE ON CONSUMPTION
Coffee is also a hot topic for the IoT researchers working on “Connected Goods” solutions at software company SAP. One customer, a producer of industrial coffee machines, is working with SAP to equip those appliances with sensors to track consumption of different products.
This work has yielded some valuable insights, says Elvira Wallis, senior vice president, IoT Smart Connected Business at SAP in Palo Alto, Calif. “The company didn’t realize that in certain geographies, people consume primarily black coffee.”
This information has helped the company better hone its marketing strategy, promoting milk-based drinks only in regions where those are actually popular, she adds.
Along with coffee makers, SAP is exploring the use of sensors to amass data from many other customer-facing devices, such as beverage coolers, vending machines, and power tools. For example, the company is working with a large consumer packaged goods firm to capture data from coolers and shelves in retail stores to track consumption.
One goal of this effort is to improve inventory management. “We provided real-time replenishment by identifying when inventory falls below a certain threshold,” Wallis says. That means stores never run out of the products consumers want most.
Data from sensors in the stores also lets the company track the temperature in coolers, to make sure product stays in optimal condition. The data may also reveal how location within a store affects product sales. Do customers snatch up more cold sodas kept in a cooler near the front of the store than in the back?
In addition, sensors on doors can indicate how long a cooler stays open and, therefore, how quickly customers are finding the products they want. “If people have to search too long, they might not return,” Wallis says.
Another SAP customer, a fragrance company, wants better insight into how its food manufacturer customers use its product. For example, the company wants to change a product’s expiration date dynamically, based on the storage temperature at the customer’s site.
“They also want to monitor consumption rates, so they can get back to their customers and say, ‘You’re getting low; we need to replenish,'” Wallis says. Sensors in containers that carry the fragrance chemicals will collect data to help the manufacturer achieve both goals.
Clearly, the range of “things” that can supply data to an IoT application is vast. Like many observers, Wallis cautions that capturing the data is only the start of the process. The real key is extracting useful insights from these masses of detail.
“It’s all about managing, monitoring, and capturing value from these smart devices, then making sense out of the data,” she says. “Then the business can make intelligent decisions.”
Relevant Training: SCM Future Trend – Big Data & IoT Application