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Manufacturing Performance
Part 2 of 5

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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.

Metrics are 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 a bank).
• 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.

Given the 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.

Categorizing the Metrics

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.

Organizational 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).

Predictive versus 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.

Predictive metrics 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

For balance of this article, click on the below link:

Lean Manufacturing Articles and go to Series 1


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