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The big picture on Big Data

It’s early in the game, but the materials handling industry is looking at new ways to use Big Data to maintain equipment, deliver services and manage processes.
By Bob Trebilcock, Executive Editor
October 01, 2013

If you think Big Data is all about Big Brother, it’s time to recalibrate your thinking. True, Big Data is all over the news thanks to the National Security Agency. The concept, however, is about more than monitoring hundreds of millions of telephone calls—or billions of status updates and tweets. For our industry, it’s also about looking for new ways to use data to maintain equipment, deliver services and manage order fulfillment processes.

After all, information is ubiquitous. As our interview this month with Richa Gupta and her colleagues at VDC Research reveals (Top 20 ADC), we are collecting more information, in more ways and from more nodes in the supply chain than ever before. Are we doing much with that data at this point? Maybe not yet, says Gupta, a senior analyst with VDC. However, she adds, “bar codes and other Auto-ID technologies are the first step toward enabling a Big Data strategy because you have to start with data collection.”

The challenge is to translate that data into information that can be used for operational decisions or to get more from processes and equipment. The ultimate point isn’t so much Big Data as it is Relevant Data.

By all accounts, the materials handling industry is at the early stages in the development of Big Data applications. But it is a topic that a number of industry leaders are discussing. Some of that discussion is around initiatives that are here today, such as smart lift trucks, and some of the discussion is around what we might do in the future with the information being collected by data capture technologies, sensors, PLCs and software systems. Here, Modern takes a look at the Big Picture on how a number of industry leaders are approaching Big Data.

Crown: The connected lift truck
Like other manufacturers, Crown has been installing sensors on its lift trucks for years. Until recently, that information was used as a fleet management tool to track the repair history of a truck or fleet of trucks for a specific customer. Today, Crown is broadening its view. “When we began our telematics program, we looked at the information in a silo,” says Jim Gaskell, director of global insight products. “A customer got a repair and we tracked that information for the dealer. No one thought to look at all of the trucks maintained by that dealer or across dealers.” Now, Gaskell adds, “we’re aggregating and analyzing data from every customer across the planet that has our trucks.”

The next step in that equation is to connect the vehicle to the technician who is going to perform service. “If the truck has a problem, it will send an e-mail to notify the service provider that there’s a fault code that needs to be looked at,” Gaskell says. “That allows the technician to bring the parts and tools that will be required for the repair.” 

By analyzing the fault codes from across Crown’s complete fleet of vehicles, the manufacturer can look for trends and patterns of wear that can be addressed in a design change in the next model. “If you think about it, we’re connecting the truck to the operator, the truck to the technician and now the truck to our design engineers,” Gaskell says. “We know so much more about the truck than we ever knew in the past, including the actual repair costs.”

Intelligrated: Big customers drive Big Data
“Conveyors and sorters have become a commodity,” says Greg Cronin, executive vice president of Intelligrated. That doesn’t mean that all conveyor and sortation providers are alike. “The information generated by those systems has become the differentiator because the value is in the data that we can collect,” Cronin says.

As with lift trucks, sensors are constantly monitoring belts, drives, motors and other key components of conveyors, sorters and other mechanical equipment for signs of wear that can predict a problem before a machine breaks down. The larger and more interesting trend to Cronin is that customers—especially retailers transitioning into e-commerce—are asking for operational data collected by conveyor and sortation systems to drive internal Big Data projects. “We have one retail customer that retains 18 months of live warehouse data,” Cronin says. “They’re using it to develop seasonality and buying patterns in their retail stores that they can then apply to the e-commerce business they’re in the process of building out.”

That retailer is not alone. “We have another customer that wants to know how many packages we’re scanning, how they’re being diverted in the warehouse, and how labor is being deployed and utilized to fill those orders,” Cronin says. While he doesn’t always know why Intelligrated’s retail customers want to capture the data, the interest is there. “These types of conversations are coming up with a number of customers. It’s a whole different world than it was just a few years ago,” Cronin says.

RMT Robotics: Distribution is all about the data
In distribution, we often talk about SKU proliferation. With an increase in the number of sensors on automated systems, we now have data proliferation. That change has been accompanied by a new ability to store and analyze data, says Doug Pickard, president of RMT Robotics. “We’ve always had sensors to detect what’s going on,” says Pickard. “What’s different is the ability to access vast amounts of data in real time while a system is operational. We can look into a piece of equipment anywhere in the world and find out what’s going on.”

What’s more, end users have become far better at figuring out the value in that data than ever before. “In distribution today, it’s all about the data,” Pickard says. “When we talk to a distribution customer with a number of DCs, we talk to the data guys before we talk to the mechanical or electrical engineers.”
At the design stage of an engagement, RMT may look at up to five years of data to understand the real problem they’re trying to solve and the situations that have arisen in the past. That same data can be used to simulate how the solution will perform based on real-world operating conditions.

Once a system goes live, real-time and historical data allows RMT to determine whether the system is operating as designed or whether the end user is operating the system differently than planned. “If a system isn’t performing as designed, we can dig deep into the data to understand what’s happening and how to get around a problem,” he says. “The ability to do analysis online and in real time is invaluable.”

Wynright: From reactive to predictive
A materials handling system is a microcosm of Big Data in a self-contained area. While a pharmaceutical company may be tracking data on thousands of patients around the globe to identify patterns and trends, a materials handling system supplier can track data from thousands of sensors, bar code scans and PLCs within the four walls of a facility. As we get smarter about analyzing that data, the industry is evolving from reactive to proactive to predictive maintenance and operations, says John Dillon, president of client care at Wynright.

In reactive maintenance, a supplier comes out once a year to lubricate the system and check the voltage on the motors. Otherwise, they are only there if there’s a problem.

Proactive maintenance involves site audits, site monitoring with camera systems and e-mail alerts when an anomaly is detected, such as a low battery level or a scanner read that is below threshold levels.

The industry has been doing reactive and proactive maintenance for years. In predictive maintenance, the system can track and analyze the performance of thousands of components of a system in real time to look for anomalies and patterns that may indicate problems in the future. “If you think of a motor-driven roller conveyor, we can track the amperage and duty cycle on every roller in a facility or across a network of facilities,” says Dillon. “With that information, we could build maintenance schedules around rollers that we believe are going to have a problem. We’re not doing this today, but it is something we plan to do.”

As more and more data becomes available, the real challenge is determining the right data to yield real operational insights and efficiencies. “In a big system, with multiple technologies, we could create hundreds of reports from the data that’s now available. The challenge to us, and our competitors, is to find the actionable data that will yield results,” Dillon says. “The company that figures that out first will separate themselves from the crowd.”

Raymond: Integrating the operator with the WMS
In recent years, lift trucks have evolved from a vehicle to move goods to a mobile information platform that can collect and send information about the operator, the truck and the battery to other systems.

Lift trucks have been sharing data with vehicle management systems for a number of years. The next step is to integrate operator data from the lift truck with labor and warehouse management systems (WMS) to drive productivity improvements. “If you put a vehicle management system on a truck, you’ll see a 5% increase in productivity by identifying bad behaviors on the part of operators,” says Scott Craver, a product manager with Raymond. “If you combine that with a labor and WMS, we believe you can see productivity improvements in the double digits.”

A vehicle management system, for instance, can provide accurate, real-time information about the travel distance, travel time, lift distance and lift time required by a lift truck operator to complete a task. That results in more accurate labor standards. What’s more, the system can better capture what Craver calls activity time. “A typical operator is moving product about 4.5 hours a shift,” Craver says. “We don’t know what they’re doing the other 3.5 hours, which could vary from sweeping floors to stretch-wrapping to putaway activity. With job coding, we can identify what they’re doing when they’re not on the truck.”

Craver has customers who are currently integrating operator information with their warehouse software systems to increase productivity in measurable ways. “Our savviest users are sharing a portion of the savings,” he says. “We’re seeing operators who get a $300 a month bonus, which is a real incentive.”

Manhattan Associates: The total cost to serve
Who is your most profitable customer? What are your most profitable products? Which are your most expensive transportation lanes? In other words, what is the total cost to serve a customer?

Those aren’t questions that warehouse, labor or transportation management systems (TMS) can answer in isolation. However, when aggregated and analyzed at a granular level, the data from those systems can provide metrics such as the cost per unit to deliver a product from the point of manufacture to a distribution center to the end customer. That cost can be apportioned in a rules-based way.

“If I’m a pharmaceutical wholesaler, a container may have crutches, latex gloves and pills,” says David Landau, vice president of product management for Manhattan Associates. “Do I apportion the cost of that shipment across those products based on weight, volume or value? I can take information about that shipment and determine the rules that are most important to my company.”

A retailer can take that information to look at the cost to serve each SKU across all of its different markets or sales channels. That can allow the retailer to identify the most profitable products, channels or geographic markets.

Finally, the data can be used to create what-if scenarios for planning purposes. A wholesaler, for instance, may compare whether it makes more sense to receive pre-paid shipments versus shipments where the wholesaler pays the freight. “If I’m a wholesaler, I may find that based on what I’m paying for freight on some lanes it makes more sense to let my vendors pay the shipping costs,” says Landau. “All of these are bigger questions than a WMS or TMS typically address on their own.”

Companies mentioned in this article
Manhattan Associates:
RMT Robotics:
VDC Research:

5 steps to leverage Big Data

For many companies, the question isn’t whether to launch a Big Data project; the tougher questions are where to start and how to create a framework for using Big Data. “In most instances, the data you need is in your system in some form, but it needs to be tamed,” says Robert Nilsson, vice president and general manager of software and supply chain intelligence for Dematic. Nilsson offers several steps to get there.

1. Use an organized process to build your analytics solution: Visualization of the data brings a quick understanding of where things stand. For that reason, Nilsson advocates for simple dashboards with targets to be achieved rather than spreadsheets.

2. Aggregate consistent data in a common spot: Information collected from multiple systems needs to be normalized so that everyone is using a common definition. For example: In one instance, a pick may be counted by the lines picked. In another instance, a pick may be counted by touches or eaches. “This could result in very different pick rates,” says Nilsson. Without information in one spot and using one definition, it’s hard to determine root causes and identify patterns or relationships.

3. Apply analytics: Nilsson urges companies to start with a statement of the problem they want to solve. Once the problem is identified, a company can apply processes, tools, a team and the relevant data to fix the problem.

4. Develop predictive analytics: “You have to understand why something is happening before you can understand what is going to happen next,” Nilsson says. Once a company is aggregating relevant data for the problem it wants to solve, that data can be run through a modeling tool that allows a team to test what-if scenarios that might predict what will happen in the future based on different decisions.

5. It may be Big, but it doesn’t have to be Complex: “Small projects can provide quick successes that create momentum to the initiative,” says Nilsson. “For that reason, it’s important to break a project down into smaller chunks using a plan so that the team is all on the same page.”

About the Author

Bob Trebilcock
Executive Editor

Bob Trebilcock, executive editor, has covered materials handling, technology and supply chain topics for Modern Materials Handling since 1984. More recently, Trebilcock became editorial director of Supply Chain Management Review. A graduate of Bowling Green State University, Trebilcock lives in Keene, NH. He can be reached at 603-357-0484.

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