The faint rumbling sound coming from the nation's warehouses and distribution centers is no cause for alarm. Quite the opposite, in fact. If the results of our annual survey on DC performance are any indication, the rumblings you've been hearing are the sound of economic recovery—or to be precise, the sound of DCs throttling up their order fulfillment operations as sales began to pick up.
While there's always the risk that a ramp-up in volume will send performance into a tailspin, it appears that most DCs avoided that trap last year. Our eighth annual survey of key warehousing and DC metrics showed that most operations made slow but steady gains in performance.
Launched in 2004, the annual study tracks the metrics DC professionals are using to monitor their operations as well as changes and trends in overall performance against those metrics from year to year. The study also provides valuable benchmarks against which managers can more accurately gauge their operations' performance within the company and against their competitors.
This year's study, which was conducted among DC Velocity's readers and members of the Warehousing Education and Research Council (WERC), was carried out via an online survey in January. In all, 602 individuals filled out the questionnaire, of which 579 provided usable responses. Respondents were asked to identify the metrics they used as well as to grade their own facilities' performance in 2010 against 44 specific operational metrics. (For purposes of analysis, the measures have been grouped into five balanced sets: customer, operational, financial, capacity/quality, and employee.)
The research, which was jointly sponsored by DC Velocity and WERC with support from Ryder, was carried out by Georgia Southern University and the consultancy Supply Chain Visions. The full results will be available online at www.werc.org after the annual WERC conference, which takes place in Orlando, Fla., from May 15-18.
Which metrics matter most?
When it comes to the performance metrics used by DC professionals, the survey showed that the most popular measures don't vary much from year to year. The metrics that received the most mentions in this year's survey—on-time shipments, average warehouse capacity used, and order picking accuracy—have appeared on the top 12 list since the study was launched.
But that's not to say the situation has remained static. As Exhibit 1 shows, there has been some change in the list of top 12 metrics compared with the 2010 survey results. Why is that? This year we changed methodologies in calculating the top 12 list. To stay consistent with the new methodology, we recalculated prior years' top 12 lists. While we found that the choice of metrics remained largely unchanged, there were some shifts in the rankings. It's important to note that decisions about which metrics an operation will use may be dictated by company policy and may not reflect the respondents' own opinions or preferences. For that reason, the survey included a question asking, "If you were the boss, what metrics would you use to run the DC or warehouse?"
|Exhibit 1: The Top 12: The most commonly used DC metrics|
Metric (by rank in 2011 survey)
|2010 rank||2009 rank|
|1. On time shipments (Customer)||1||1|
|2. Average warehouse capacity used (Capacity/Quality)||4||7|
|3. Order picking accuracy (Capacity/Quality)||2||3|
|4. Peak warehouse capacity used (Capacity/Quality)||9||*|
|5. Dock-to-stock cycle time, in hours (Operational)||6||6|
|6. Internal order cycle time (Customer)||10||8|
|7. Total order cycle time (Customer)||*||12|
|8. Lines picked and shipped per hour (Operational)||11||11|
|9. Lines received and put away per hour (Operational)||*||*|
|10. % of supplier orders received damage free (Operational)||*||10|
|11. Fill rate - line (Operational)||3||4|
|12. Annual workforce turnover (Employee)||8||*|
|* Did not appear in top 12|
As it turned out, there were some disparities between the two sets of metrics. Although "on-time shipments" and "order picking accuracy" appeared on both lists, the respondents' top five picks included three measures that did not make the list of the most widely used metrics: "inventory count accuracy, by unit;" "inventory count accuracy, by location;" and "distribution costs as a percentage of sales." The fact that respondents chose a financial metric indicates that what we do in the DC—and how we do it—affects more than customer satisfaction; it also has an impact on the organization's bottom line.
Holding their own
As for how the nation's warehouses and DCs are performing against key metrics, the news is generally good. As noted above, the upswing in volume hasn't brought a halt to the improvement trend. In fact, the latest survey found that relative to last year's findings, respondents either maintained or improved their performance against 52 percent of the 44 metrics studied.
The news was even better among the top-performing companies, the 20 percent of respondents designated "best in class." A comparison with last year's findings showed that these companies either maintained or improved their performance against nearly seven out of 10 metrics.
Exhibit 2 identifies the metrics that saw the most improvement over last year across the entire respondent base. (When making comparisons from year to year, we have continued to use the median—the midpoint of all the responses—rather than the mean, or average, because it's less likely to be skewed by very high or low numbers.)
|Exhibit 2: Going up! Where DC performance improved|
|Metric||Major opportunity||Typical||Best in class||Median 2011||Median 2010|
|Internal order cycle time||> 36 hours||>= 8 and< 23.4 hours||< 2.2 hours||12 hours||24 hours|
|Dock-to-stock cycle time||> 18.7 hours||>= 4 and < 8.2 hours||< 2 hours||6 hours||9.1 hours|
|Pallets picked and shipped per person hour||< 7 per hour||>= 14.5 and < 20 per hour||>= 26.5 per hour||18.5 pallets||15 pallets|
|Supplier orders received per hour||< 1.5 orders||>= 3 and < 5 orders||>= 10 orders||4 orders||3 orders|
|Total order cycle time||> 72 hours||>= 15 and < 48 hours||< 4.5 hours||36 hours||48 hours|
|Days on hand - raw materials||> 66 days||>= 29 and < 45 days||< 15 days||30 days||39 days|
|Distribution costs as a % of sales||> 10.2%||>= 3.3 and < 6%||< 1.7%||4%||5%|
|Note: Survey responses have been divided into quintiles to make it easier for companies to see where they stand in comparison with other warehouses and DCs. For example, the "best in class" category represents the top 20 percent of respondents, while "major opportunity" represents the lowest 20 percent of respondents—or those who have the most to gain from performance improvements.|
Of particular note are the improvements in average internal order cycle time and total order cycle time, both of which dropped by a whopping 12 hours compared with the two previous years. We believe these results speak to a greater sense of urgency among warehouse and DC managers to keep up with orders as activity picks up.
Another interesting finding is the shift in the status of the "dock-to-stock cycle time" metric, a measure of receiving and put-away efficiency. Last year, "dock to stock" performance was identified as one of the major pain points, with median performance slipping to 9.1 hours from eight hours the year before. This year, however, "dock-to-stock time" ranked among the "most improved" metrics, with the median cycle time shrinking to just six hours. It's not much of a stretch to conclude that the "dock to stock" improvement (which presumably helped ensure product was available to be picked) contributed to the impressive gains seen in both internal and total order cycle times.
Where are the points of pain?
Of course, every coin has its flip side, and this year's survey was no exception. Just as performance against several of the metrics showed noteworthy improvement over the previous year, performance in other areas deteriorated.
Exhibit 3 identifies the major points of pain—the metrics that saw the biggest performance declines. It's worth noting that three of the five "pain points" centered on internal operations, notably the pick and pack functions. Although we can only speculate as to the cause, one possibility is that the typical order profile has changed, with orders getting larger. If so, that might explain why performance dropped against those particular metrics, which focus largely on speed.
|Exhibit 3: Points of pain: Where DC performance declined|
|Metric||Major opportunity||Typical||Best in class||Median 2011||Median 2010|
|Honeycomb %||< 14%||>= 39 and < 69.8%||>= 85%||50%||72%|
|Orders picked and shipped per hour||< 2 orders||>= 4.2 and < 9.5 orders||>= 29.8 orders||6 orders||8.5 orders|
|Lines picked and shipped per hour||< 13.6 lines||>= 25 and < 40.6 lines||>= 77.4 lines||30 lines||36.0 lines|
|Cases picked and shipped per hour||< 34.8 cases||>= 85.2 and < 144 cases||>= 280 cases||120 cases||142.5 cases|
|Days on hand finished-goods inventory||> 75.2 days||>= 30 and < 45 days||< 14.4 days||36.7 days||32 days|
It's also worth pointing out that in some cases, performance slippage may not be a bad thing. Take the "honeycomb percentage" metric, which showed the biggest drop in performance relative to last year's survey.
Like "average warehouse capacity" and "peak warehouse capacity" (whose performance declined as well), "honeycomb percentage" is a measure of how fully space is being used within the warehouse or DC. And while it might appear that the objective here would be to get as close to 100 percent as possible, that's not necessarily the case. In fact, research has shown that the ideal "average warehouse capacity used" number may be closer to 80 percent, because it gives facilities the flexibility to respond quickly to changing economic conditions.
In any event, it appears that while there's been some slippage, performance in most warehouses and DCs could be fairly characterized as getting better all the time. The big question now is, can the momentum be sustained—especially if, as expected, orders grow faster than employment?
About the authors: Karl Manrodt is a professor at Georgia Southern University. Joseph Tillman is senior researcher and consultant for Supply Chain Visions. Kate Vitasek is founder of Supply Chain Visions.
The authors welcome readers' comments, suggestions, and insights into the research and their own use of metrics. They can be reached by e-mail: Karl Manrodt at email@example.com, Joseph Tillman at firstname.lastname@example.org, and Kate Vitasek at Kate@SCVisions.com.