Material Management Trends

Technology is changing at an unprecedented pace across our society. Consider that the Apple iPad was introduced in 2010—just four years ago—yet tablets are already threatening to overtake both laptop and desktop computers in combined sales.

“Big data” and analytics

One of the most common definitions of “big data” is data that exceeds the processing capacity of conventional database systems. Increasingly this definition is being broadened to describe efforts to capture data that is not currently being captured, consolidate it with other data, and analyze and use it to achieve specific objectives. In the context of warehouse management, big data includes the processes required to aggregate, inspect, clean, transform, and model data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.

In our experience, one reason for that lag in the warehouse is that managers are concerned about the complexity of collecting and analyzing data from disparate sources. Data security, privacy, integrity, and integration into existing business systems are major barriers to the rapid advancement of big data analysis in warehousing and distribution. Another issue is that data is only useful when it is integrated into daily management processes. This often requires special training to ensure employees are maximizing the data-driven decision-making opportunities.

These obstacles must be overcome because there is little doubt that the organizations that can collect and use data effectively will be the ones that are most successful in achieving consistent improvements in supply chain reliability and efficiency. Overcoming these obstacles requires a commitment by senior management to strategically implement proven, high-value analytical tools that can both deliver short-term results and serve as building blocks for the future. Once an organization begins to see success in using data to support decision making its appetite for more ambitious projects will grow.

One example of how that is already occurring is the application of forklift fleet management software, which is increasingly being used to track vehicle impacts, operator productivity, and equipment utilization across fleets of lift trucks. Until this technology was developed, there was no way for warehouse managers to track impact events by operator or location. That often prevented managers from taking corrective measures in the form of additional training or tweaks to the warehouse layout. In addition, when warehouses don’t track the number of impacts or react to events in any meaningful way, it encourages a culture in which operators don’t recognize impacts as a problem.

When warehouse managers start to track these events and react to them, they are often surprised at the number of impacts that are occurring and the reductions that can be achieved by using data to change behavior. Food services provider The Clemens Group provides an example of how powerful this can be: Managers at that company used forklift fleet management technology to achieve an 80 percent reduction in impacts simply by monitoring and investigating impact events. Similar improvements are possible when data collection is used to track equipment utilization and operator productivity.

In addition to sustainable improvements in forklift utilization, warehouse safety, and operator productivity, systems such as forklift fleet management can introduce an organization to other uses of data-based management. One particularly interesting use of big data to drive improvements in material handling is the move to aggregate supply chain data across businesses. Bringing together metrics from similar organizations that are operating similar types of facilities makes it possible to produce baseline metrics that warehouse managers can use to benchmark their operations and help them identify best practices that maximize efficiency and compress shipping times.

The Internet of Things

The Internet of Things describes the sensors and data-communication technology built into physical objects that enable them to be tracked, coordinated, or controlled across a data network or via the Internet. While a contributor to the big data trend, the Internet of Things is distinct in that it represents direct machine-to-machine communication and coordination, while big data generally encompasses data from a variety of sources that are consolidated for human analysis.

In the warehouse, the Internet of Things will support communication and coordination across conveyors, automated storage and retrieval systems, forklifts, and other systems to enable new levels of visibility and automation.

One easy-to-understand example of how the Internet of Things might evolve is the connected home or office, which senses your presence and automatically controls the lighting, temperature, and entertainment options based on your preferences. Imagine what this kind of connectivity and intelligence could accomplish in the warehouse. It would take all the disparate systems and equipment—conveyors, robots, automated storage and retrieval systems, automatic guided vehicles (AGVs), forklifts, battery charging stations, dock equipment, pick carts, voice picking systems, lighting, and heating, ventilation, and air conditioning (HVAC) systems—and tightly couple them to warehouse control, labor, transportation, order, and customer management systems. Such a connected warehouse would allow supply chain and warehouse managers to reach new levels of workflow optimization, operational efficiency, and predictability, all while providing real-time visibility into operations and predictive analytics.

Having devices that can communicate is the first step in realizing the potential of the Internet of Things. The next and more challenging step is being able to capture data from devices across the facility, aggregate that data for analysis, and enable machines to act on it. It is this aggregation, processing, and decision making that transforms the Internet of Things from a collection of isolated devices sending out data into a powerful network that can work in concert to support objectives.

In the case of the warehouse, forklifts are already doing much of the data collection. Forklifts today are equipped with wireless connectivity, data storage, and sensors that allow them to collect information from their own internal systems as well as from their environment, and then transmit this data to management systems.

With the cost of sensors going down and the amount of processing power embedded in forklifts continuing to increase, the forklift, which is the only device in the warehouse that travels to every location in the facility, will be in a position to expand on its current functionality. In addition to moving product, it will become a mobile information technology hub that collects and processes data from products, operators, the environment, and other material handling systems to support unparalleled visibility into warehouse operations as well as increased automation.

Mobile Technology

Mobile technology refers to the use of tablets, smartphones, and other handheld or wearable devices for communication and information.

Mobile technology is quickly penetrating the business realm. The global market research firm Forrester predicts that within the next two years, one-third of all tablets will be sold to businesses. Tablets are already replacing fixed-mounted terminals in law enforcement, agriculture, and aviation; they are just starting to be used in warehouses and distribution centers by managers and other personnel who don’t want to be deskbound but still need to deliver reports and information. Technicians who service forklifts and automated material handling equipment are also using them for fast, convenient access to information on troubleshooting, repairs, and work orders.

As material handling operations increase their use of analytics and automation, mobile technology will emerge as the primary platform for displaying data. Developers of process and workflow management systems have already adopted a “mobile first” approach to developing applications. Experts believe this will quickly move to a “mobile only” development philosophy as traditional, full-screen desktop displays are no longer considered useful.

With mobile technology, warehouse managers will have access to a wealth of data, including equipment status and performance reports, wherever they may be. With more warehouses operating 24 hours a day, seven days a week, this will allow managers to track performance and respond to problems around the clock.

Other warehouse personnel will encounter mobile technology through their interface with equipment such as forklifts and automated storage and retrieval systems. Wearable technology, such as “smart glasses,” is being integrated with warehouse management systems to enable hands-free mobility for workers using visualization and voice recognition to receive instructions for completing tasks.

Advanced Robotics

The technologies discussed to this point have been focused primarily on making the warehouse workforce—from senior managers to forklift operators to order pickers—more productive. The next two trends, advanced robotics and autonomous vehicles, automate manual tasks.

Robotic equipment has been used in material handling for some time. But a new generation of advanced robots incorporates enhanced levels of sensing capabilities and algorithms that allow them to better sense their environment and make decisions based on changes in that environment.

This is an important development in the use of robotics in material handling. Material handling tasks typically have been too variable to make them good candidates for robotics. Unlike manufacturing, where products move down an assembly line and can be precisely positioned for each operation, products in a warehouse typically are different sizes and shapes and may not be positioned in exactly the same way or location each time they are handled. New vision-sensing technologies are enabling robots to adjust to these variations, allowing, for example, mixed-case palletizing and depalletizing to become commonplace. As robots get smarter, more refined, and safer, they will increasingly be used to handle some of the routine yet variable tasks being performed by humans today in warehouses.

As in manufacturing, the use of robots in material handling will free humans from performing routine tasks and bring greater speed and accuracy to repetitive tasks, supporting the ultimate goal of reducing material handling costs.

Autonomous Vehicles

While sometimes used synonymously, there is an important distinction between driverless and autonomous vehicles in the warehouse. Autonomous vehicles are driverless, but not all driverless vehicles are autonomous. Autonomous vehicles are capable of making decisions in response to their environment. Driverless vehicles, such as automatic guided vehicles (AGVs), are controlled from outside the vehicle or are limited to a programmed path.

The AGVs used in warehouses today typically follow preplanned routes and can’t navigate around obstacles. When obstacles are encountered, an AGV simply stops in its tracks. Human intervention is required to remove the obstacle and restart the AGV. These situations are commonplace in warehouses and distribution centers, and they cause congestion and disruption. This shortcoming has limited the use of AGVs in material handling. They currently account for less than 1 percent of forklift sales in the United States, according to sales numbers released by the Automatic Guided Vehicle Systems Industry Group of the material handling industry association MHI.

To be truly autonomous, AGVs need decision-making capability that allows them to perform tasks with a high degree of freedom from external control. When encountering obstacles, for example, they should be able to reroute themselves to complete the task at hand without human assistance. Enabling AGVs to do that will require advances in current technology. Of the disruptive technologies discussed in this article, autonomous vehicles may be the furthest from playing a significant role in warehouse operations because of the challenges that still exist in terms of sensor capability and vehicle intelligence.

Yet much is happening outside of the material handling industry that is driving the technology forward. In the automotive industry, General Motors, Audi, Mercedes-Benz, and Nissan are testing autonomous concept cars, and Google’s driverless car has logged more than 700,000 road miles. Not too long ago driverless automobiles were thought to be in the distant future; manufacturers now expect commercialization by 2020.

With autonomous vehicles potentially on the road within the next six years, autonomous forklifts can’t be too far behind. In the meantime, just as auto manufacturers have tapped into their research on driverless vehicles to bring new collision-avoidance systems to market, forklift manufacturers have introduced semi-autonomous capabilities that work with operators to increase productivity. As technology develops and matures, these semi-autonomous vehicles will evolve into fully autonomous vehicles that will create additional opportunities for productivity improvements and cost reduction.