strategicinsight METRICS & KPIs: RESEARCH REPORT
(The classification is indicated for each of
the top 10 metrics listed in Exhibit 1.)
What’s telling, they say, is that managers
appear to rely heavily on operational metrics (“order fill rate,” for example) or
numbers derived from operational performance (like “order picking accuracy”).
Only one of the top 10 metrics, “on time
shipments,” is a customer-facing measure,
they found.
EXHIBIT 3
GMA/FMI perfect order index
usage and performance
Measure
of cases shipped vs. cases ordered
of on-time delivery (retail)
of data synchronized SKUs
Order cycle time
of unsaleables (damaged product)
Days of supply (forward coverage)
Service at the shelf
The not-quite-perfect order
That’s not to say companies aren’t keeping a close eye on customer service, however. The fact is, the majority are indeed
tracking their operation’s performance
against the metrics most commonly associated with the “Perfect Order” and that are used to compute the Perfect Order Index (POI).
The Perfect Order Index is a widely recognized measure
that incorporates four critical customer service elements:
order completeness, timeliness, condition, and documentation. In other words, to be considered perfect, an order
must arrive complete, be delivered on
time, arrive free of damage, and be
accompanied by the correct invoice and
other documentation. To calculate a
company’s score on the index, you simply take each of the four metrics and
multiply them together. For example, a
facility that ships 95 percent of its orders
complete, 95 percent on time, 95 percent
damage-free, and with the correct documentation 95 percent of the time would
earn a score of 81.5 percent (0.95 0.95
0.95 0.95).
Exhibit 2 shows the median and best-in-class scores for each of the four POI
measures. The researchers chose to use the median score
(the exact mid point of the range—the point above which
half the values are higher and half lower) rather than the
average because it is less likely to be skewed by statistical
outliers—very high or very low numbers. “Best in class” is
Using
25.4%
36.2%
10.9%
47.5%
16.5%
23.0%
3.7%
Best practice
99.9%
99.5%
100%
13 hours
0.04%
15. 7 days
99.8%
Median
99.0%
97.9%
100%
42 hours
2.0%
30 days
95.0%
defined here as responses from the top 20 percent of companies—that is, those who are performing best against each
of the metrics.
It’s important to note that there are other ways to calculate the Perfect Order Index besides the method described
above. For example, the Grocery Manufacturers Association
and the Food Marketing Institute use a
seven-element formula to calculate the
Perfect Order Index. (The elements are
percentage of cases shipped vs. cases
ordered; percentage of on-time deliveries; percentage of data synchronized
SKUs; order cycle time; percentage of
unsaleables (damaged product); days of
supply; and service at the shelf.) As part
of their study, the researchers analyzed
the survey responses using the
GMA/FMI criteria. The results are
shown in Exhibit 3. Given the low rates
of usage for some of these metrics, however, the researchers urge readers to use
the results in this table with caution.
EXHIBIT 2
perfect order metrics
Metric
% of orders with on-time delivery
Shipped complete per customer order
Shipped damage-free (outbound)
Correct documentation
Best in class
99.3%
99.7%
99.9%
99.9%
Median
97.6%
98.0%
99.0%
99.0%
Continuous improvement?
One of the hopes of anyone conducting research over time
is that trends will begin to emerge. And when the object of
the study is business performance, the hope—if not the
expectation—is that those trends will indicate improvement. In the case of this study, the results have largely been
what the researchers had hoped—we have seen steady
improvement in DC and warehousing performance across
a wide variety of measures.
Whether this momentum can be sustained in a dismal economic climate, only time will tell. In the meantime, the
researchers invite readers’ comments, suggestions, and
insights into the research and their own use of measures. They
can be reached by e-mail: Karl Manrodt at kmanrodt@geor-giasouthern.edu, and Kate Vitasek at kate@scvisions.com.
