Fantasy warehousing

How good is your warehouse, really? How can you tell?
Traditional measurements — order fill rates, cycle times, lines picked per labor-hour or cost per order shipped — can provide a good benchmark against a warehouse's own past performance, as long as the overall situation remains unchanged. In other words, these traditional measurements are fine as long as the world hasn't changed and products, customers, order profiles, technologies and costs have remained the same.But we live in a Heraclitean universe: Nobody walks through the same streamor the same warehousetwice. Furthermore, no two warehouses are exactly the same. Each has different space, equipment, employees, customers, order profiles, value-added services and cost structures.Looking outside
The good old traditional measurements are even less useful in making direct comparisons to other warehouses, even in the same company or same industry. The reason, as we all know, is "My warehouse is different!" No two warehouses have the same space, equipment or people or the same customers or the same order profiles, or the same value-adding services or the same cost structure. In fact, every warehouse really is unique. So how does one compare internal operational efficiency to the best warehouses?One approach is a conventional benchmarking study, in which partners share detailed technical analyses of operations looking for performance differences and, in particular, differences in technology, policy or practice. This approach can be effective, but it's also labor-intensive and expensive, especially if outside professional services are required.Use the Web
Georgia Tech has been developing an alternative to these individual, labor-intensive benchmarking studies. By exploiting Web-based information technology and a new approach to system performance assessment, any warehouse, anywhere, can benchmark itself quickly and inexpensively. The process provides both a ratingcompared to the best possible performanceand an indication of areas to investigate for improvement.The assessment methodology, data envelopment analysis (DEA), is based on a database of other warehouses. The version posted online is the Internet-based Data Envelopment Analysis system, or iDEAs. For every warehouse in the database, information is recorded about the resources used (space, equipment, labor, inventory) and the services produced (items and lines picked — broken case, full case, and pallet). The resource and service information is used in a system-based efficiency analysis. The identities of and data about participating companies, however, are treated as proprietary and not shared outside of the research team.Playing to winIn essence, the DEA analysis compares your warehouse to a "fantasy league" warehouse. You can compare warehouses two ways. In an output-oriented analysis, the fantasy warehouse uses the same resources as your warehouse, but produces the maximum possible output. In an input-oriented analysis, the fantasy warehouse has the same output as your real warehouse, but uses the least possible resources.For an input-oriented analysis, the efficiency score indicates how many resources, relative to you, the fantasy league warehouse used. For example, scoring only 80 percent means the fantasy league warehouse produced the same output as yours, but used only 80 percent of the space, equipment and labor. The closer a score is to 1.0, the closer you are to the fantasy league warehouse in terms of system efficiency. The score on the output-oriented version indicates how much output, relative to the fantasy warehouse, your warehouse produced. What's important is that this isn't an "average" performance comparison and isn't based on a single, partial productivity index. It's an assessment that considers every resource and service simultaneously. It determines from real warehouse data what the "best possible" performance should be.Early findings
So far, nearly 200 warehouses rely on this Internet-based tool. Comparisons among these early adopters confirm previous studies that indicate:

• Larger warehouses are not necessarily more efficient, at least with regard to equipment and labor.
• Labor-hours alone aren't a good predictor of system efficiency.
• While highly-capitalized warehouses tend not to be the most efficient, they are even less efficient if they also have a lot of space.
•  No matter how the warehouse database gets segmented and sampled, a large fraction of the population is operating at a system efficiency of 50 percent or less.

In addition, statistical trends about warehouse efficiency are beginning to emerge. For example, within a particular category of warehouse, partial productivity metrics aren't constant. Instead, they exhibit a performance distribution. Figure 1 illustrates such a distribution for lines picked per labor-hour. Acknowledging the performance differences might justify a higher or lower warehouse productivity.