MEASUREMENT AND METRICS
The first step in
effectively and efficiently using metrics is to define what metrics
are. Simply stated, a metric is a verifiable measure stated in
either quantitative (e.g., 95 percent inventory accuracy) or
qualitative (e.g., as evaluated by our customers, we are providing
above average service) terms. Metrics should be consistent with how
the firm delivers value to its customers stated in meaningful terms.
The importance of
metrics has been recognized by numerous managers. For example, Tom
Malone noted, "If you don't keep score, you are only practicing."
Emery Powell noted, "A strategy without metrics is just a wish. And
metrics that are not aligned with strategic objectives are a waste
of time." Finally, some unknown but very wise manager noted, "Be
careful what you measure—you might just get it." That is, by
measuring something you are stating to your employees, managers,
stakeholders, and industry analysts that an activity is important.
important because of the functions that they provide, namely:
• Control: Metrics
enable superiors to control and evaluate the performance of the
people working under them. They also enable employees to control
their own equipment and their own performance.
• Reporting: This is the most commonly identified function of
metrics. We use metrics to report performance to ourselves, to our
superiors, and to external agencies (e.g., Wall Street, the EPA, or
• Communication: This is a critical but overlooked function of a
metric. We use metrics to tell people both internally and externally
what constitutes value and what the key success factors are. As
pointed out previously, people don't understand value but they
understand metrics. As a result, value as implemented at the firm
should influence the type of metrics developed.
• Opportunities for improvement: Metrics identify gaps (between
performance and the expectation). Intervention takes place when we
have to close undesired gaps. The size of the gap, the nature of the
gap (whether it is positive or negative) and the importance of the
activity determine the need for management to resolve these gaps.
• Expectations: Metrics frame expectations both internally (with our
personnel) and externally (with our customers). Metrics help form
what the customer expects. For example, if we say that we deliver by
9:30 a.m. next day, we have formed both a metric (i.e., did we meet
the 9:30 deadline) and an expectation. We will satisfy our customer
if the order arrives by 9:30. We will disappoint otherwise.
importance of metrics, it is important that the "appropriate" set
of metrics be developed and implemented. An appropriate set is one
without too many items (too many measures confuse the user as to the
real focus of activities), expressed in meaningful terms,
predictive in nature (they help us to foresee potential problem
situations), and consistent with the needs of the environment.
Next, consider the
different types of metrics found in most operations management
systems. One way of understanding these differences is to focus on
the various categories observed when studying metrics. We focus on
two major metrics-related categories: organizational focus and the
extent to which the metrics are predictive.
focus. Just as all firms can be viewed from different levels
(beginning with the ticket agent or the programmer and ending with
the firm as a whole), so too can we have metrics present at
different levels. In general, we have at least four different levels
of organizational focus for metrics. We can have organizational
metrics, or measures that capture and describe the performance of an
organization. This organizational level typically is the corporate
level. Performance at this level is typically described in terms of
market share, rate of return, or rate of growth. We can also have
product metrics. These metrics are usually stated in such terms as
cost per unit, contribution margin per unit, or growth in sales.
Metrics can also be functionally oriented, in that we can measure
the performance of a group such as purchasing or services or
manufacturing. Finally, we can be faced by activity/individual
metrics. These are metrics that are specific to a person or to an
activity (e.g., how long it takes to make one unit of output at a
specific machine). At each level, we have different requirements. As
a result, each level requires its own type of metrics (in other
words, one size does not fit all).
outcome metrics. We must be concerned about the extent to which a
metric is predictive (as compared to outcome-based). An
outcome-based metric (also known as an output metric) is one that is
generated only after the completion of the activities. Such a
measure tells us how we did in the end. In contrast, a predictive
metric (also referred to as a process metric) is one that we can use
to help predict our chances of achieving a certain objective or
goal. To understand the differences between these types of
measures, consider the following example. You are standing in front
of your house. It is now 9:00 in the morning. You receive a call
from someone very important telling you that you have to be in the
office of a client by no later than noon. That client is located
some 90 miles away from your house. An outcome-based measure would
involve having someone with a stopwatch standing in the client's
office. If we arrive at or before noon, we would be marked as being
"on time." Otherwise, we would be late. Outcome metrics suffer
because they are after the fact. We only know how we did after it is
too late to take any corrective action—after the activity is done.
Such a measure tends to condemn us to repeating the same
mistakes—over and over again.
take a very different approach. We would start by noting the
starting time (9:00 a.m.), the availability of appropriate resources
(a car in good working order, which is fully gassed and ready to
go), and the average speed per hour (50 miles per hour). With
thisinformation, we can predict that the chances of making the
meeting with the client are very, very good.
In many systems,
the bulk of metrics are outcome-oriented, rather than predictive.
For example, we measure on-time delivery rather than looking at
inventory accuracy or setup time or total time for a specific
operation or process to be completed. As a result, the measure
system gives the managers little information on which they can
predict their chances of meeting their objectives. However, firms
such as Texas Instruments are now turning their attention to the
development of predictive metrics. They recognize that such
measures are far more useful to the users.
As we can see from
this brief discussion, the rethinking of metrics is opening up new
areas of concern and interest for the integrated enterprise. When
the VP of operations bemoans the fact that the operating metrics
results are not being reflected in the financial results, that
person is focusing on the extent to which there is consistency
between the various levels of metrics. By focusing on metrics, we
can begin to resolve the paradox that has traditionally frustrated
manufacturing— our lack of visibility at the level of the board of
directors. Metrics are a topic whose time has come.
To Be Continued
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