Getting the Most from a Real-Time Warehouse
Connect, Summer 2007

A combination of technology and analytics yields peak responsiveness and performance

To better manage the twin pressures of market demand and competition that face DCs, real-time technologies continue to evolve. The systems help optimize the picking, packing, replenishment and flow of SKUs throughout the supply chain with fewer inventory reserves, personnel and resources.

The real-time warehouse integrates workers and execution systems with warehouse management systems (WMS) to reduce the number of touches per product. It automates inventory management and delivers pick and replenishment information to the floor worker. It synergizes labor and automation while tracking the movement of shipments. And, it utilizes multiple scan points throughout the material path to ensure product gets to the correct shipping area.

Real-time solutions also allow users to meet the demands of their customers for order and shipment status, while simultaneously offering information for analysis and internal improvements. The key to achieving your real-time efficiency goals lies in the proper application of the technology, and meaningful analysis of the resulting data.

Technologies Yield Visibility

Real-time technologies include wired and wireless radio-frequency (RF) equipment, pick-to-light, voice, bar code and radio frequency identification (RFID) equipment.

Designed to generate improvements in productivity, accuracy, throughput and response time, these technologies increase SKU visibility from receiving to shipping. When combined with automated systems, real-time is measured in milliseconds. Scanners read boxes and/or totes moving along the conveyor, sometimes at hundreds of feet per minute.

The information collected by these systems via automatic data capture (ADC) is communicated to the facility’s WMS. By integrating and synchronizing each functional area within the DC via the material handling system, operational visibility and control is gained.

Having real-time visibility not only offers for a better understanding of current processes—it also can act as a crystal ball of sorts, allowing for timely reallocation of resources according to shifting workload and volume demands, says Alan McDonald, senior supply chain improvement consultant with FORTE.

“Using the WMS you can project different order wave scenarios to see what each will require through the different points of your DC. If you see bottlenecks, you can switch orders around, or add and reallocate personnel resources to increase productivity and capacity in certain zones,” he suggests.

Real-time technologies at the warehouse level are also being leveraged as part of the new trend toward a holistic supply chain. Today’s software packages can now correlate database information from a variety of sources beyond the WMS—including enterprise resource planning (ERP), labor management systems (LMS), transportation and yard management systems (TMS and YMS)—to generate better process visibility, higher throughput and greater labor productivity levels while reducing cycle times throughout the supply chain.

Improvement Through Benchmarking

Real-time doesn’t end with the completion of a task. The real-time warehouse provides an opportunity to anticipate and react to dynamic business conditions through data conversion and analysis. Benchmarking this information against key performance indicators (KPIs) will help continually reveal areas for processes improvements and performance optimization.

KPIs are the standard against which your real-time data is compared. Industry standards can provide a useful guideline of which metrics are important, and how others stack up against them. Resources for those standards include industry associations, material handling organizations and industrial engineering manuals, which frequently break out KPI standards by industry. Sister companies can also provide useful information. When determining KPIs, however, it’s most important to consider your own facility—not what others are doing and measuring. So select KPIs based on the following:

1. The information accurately represents a comparable situation.
2. The metrics measure achievable performance rates.
3. The data is relevant to the work performed in your facility.

Next, compare your information to the selected KPIs. For the most useful benchmarking, you need to know what your information indicates, what system it originated from and how accurate it is, says McDonald. The indicators you’re using should measure the actual work performed, not metrics that can be swayed by an outside factor.

“Take something as simple as how your system defines an order number, like ‘123.’ Different softwares within the same company may tack a suffix onto that order number—making it 123.001 and 123.002—to indicate two different shipments due to a backorder,” he notes. If your KPI is the number of orders shipped in a day, then establishing consistent criteria across systems for the definition of an order is important to understanding real-time performance.

Another example: “If you’re measuring the number of units picked in a day, and that number is 2,000, that number doesn’t necessarily mean much relative to the amount of work actually done,” continues McDonald. “One palletload could hold 2,000 units. Or, workers physically traveled throughout the facility to pick 2,000 separate units. There’s a big difference in the amount of work actually done between the two examples.”

Once you pick the right information to examine, it should be easy to see improvements, setbacks or consistency over a period of time. Analysis frequency depends on the metrics at hand. Waves of orders would naturally be monitored and evaluated throughout the day. For a longer perspective, measuring performance over different timespans (daily, weekly, or monthly) gives management a broader view of efficiencies gained from the real-time technologies.

One last thing to consider, says McDonald, is what he calls “real-enough-time” for your facility. While the real-time technologies have become more affordable and their use more common, your facility simply may not need that type of information and subsequent analysis. So make sure real-time is right for your situation before you invest.

“While it’s not very often that you see batch downloads at night anymore, maybe that’s all you need,” he notes. “You can invest in real-time technology yet not reap the benefits of it if you don’t have that sort of demand from your customers.”

Real-Life Examples of Real-Time Improvements

Putting real-time data analysis results to work is key to reaping productivity improvements. Here are three examples of actual companies who put their benchmarks into practice, as observed by McDonald:

1. A DC fulfilling fashion industry orders monitored the number of units per hour picked by workers as their KPI. Picking had been handled in two kinds of waves: efficiently picking large, carton-size quantities all grouped together, and inefficiently picking one or two item orders—typically backordered items—all grouped together. When the data was analyzed, it revealed that a better solution equally blended both large and small orders into one picking wave to increase efficiency throughout the system, eliminating sortation backups and increasing worker productivity.
   
   
2. In the party planning industry, a warehouse struggled with timely replenishment of forward pick areas, even on a daily basis. Upon examining the data for both replenishment workers and picks per hour in the picking zones against KPIs, it was determined that pick items were improperly slotted. By moving the most popular items from carton flow to pallet flow racks, and alternately removing the least popular items from pallet flow into more appropriate areas, the DC regained control of replenishment and increased picking efficiency.
   
   
3. An industrial products distribution facility experienced massive bottlenecks in just one picking zone. Although it was visually obvious where the problem was, the solution was realized through an examination of the number of cartons being processed per zone. Comparing the data to KPIs, the company realized that the most frequently picked items were all located in the same zone. By using their WMS’s predictive analysis capabilities to try a variety of potential slotting scenarios, warehouse management was able to determine the best reorganization of products per zone before implementing changes. The reslotting alleviated the back-ups and yielded smoother fulfillment activities.

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