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It is essential to have a thorough understanding of a particular business unit's business strategy and value proposition before selecting appropriate metrics. The value proposition answers the question:
The business strategy answers the question:
Different firms and different supply chains have different business strategies and value propositions, and answering those questions is often harder than one might imagine. To illustrate, let's look at some examples of metrics that are mis-aligned: cases in which a company discovered that they weren't measuring the things that really mattered to their customers.
Dell initially thought that their personal computer customers were most interested in buying the fastest processor available. But after getting to know their corporate customers better through ongoing face-to-face interactions, they discovered that the Information Technology (IT) departments in corporations really wanted a consistent and common platform across all their users. If the speed was 5% below the maximum currently available, it didn't matter - the consistency was what counted, since that would make technical support a great deal simpler.1 Dell's LatitudeTM notebook computers are an example resulting from these customer interactions; component stability, consistency, and backward-compatibility are features of that particular product line. Dell's InspironTM line pushes "bleeding edge" features like the fastest available processor speeds and high-end graphics components that corporate IT departments may not wish to support in wide-scale rollouts due to potential incompatibilities.
As another example, General Electric Aircraft Engines (GEAE) formerly measured the performance of their aircraft engine overhaul shops by "turnaround time", or "TAT". TAT is the time it takes for GEAE to complete an engine overhaul, and the faster this time, the less time their customers (the airlines) wait for that engine. From GEAE's standpoint, this was a perfectly valid metric to gauge their shops' performance, but they discovered that the airlines were more interested in a metric called W2W - standing for "Wing-to-Wing". W2W is the time from the moment an engine is removed from the wing of an airplane to the time the repaired (or a replacement) engine has been mounted, and represents the total amount of time an expensive airplane is out of service due to engine maintenance or swapping.
So far it sounds like these two measures would complement one another, as shown in the figure below:2
However, they are not the same thing; W2W time is obviously longer than TAT time. How would you improve W2W time, as opposed to simply improving TAT? Take a moment to think about this, then compare with GEAE's solution by pressing the "What GEAE did" button.
A third example is a metric for monthly output of a factory producing personal computers. The metric was on output only, with no requirement or metric on meeting due dates for specific orders! Given the metric used, the person scheduling this factory would obviously schedule it for maximum output, but that was probably not what management really wanted. For example, this could lead to on-time fulfillment of low-margin, easier-to-produce items while customers ordering higher-margin items might be forced to wait, possibly canceling their orders and finding alternate suppliers.
A final example of misaligned metrics relates to the way many companies handle technical support. Customer calls are typically handled by large "call centers"; everyone has encountered them, and sometimes the experience is less than perfect. Why might this be? Many call centers are evaluated on the basis of call center productivity, which usually translates into the number of calls "handled" per operator per hour. Note the direction of this metric; it is designed solely for the operator to "deal with" a call as quickly as possible, not necessarily in a satisfactory manner! The relative ease of quantifying this metric probably relates to its popularity, but it is too narrow and could encourage poor service. You may have been the victim of a "bad" metric like call center productivity; no one likes those situations, so you might have taken your business elsewhere. Ideally there would be a two-dimensional metric including both productivity (calls handled per hour) and quality of experience, as rated by the customer. Some companies are beginning to treat call centers more wisely. More often, representatives now ask the customer to rate the quality of service at the end of the call. Companies are even empowering operators to spend time with customers, discussing their general needs and allowing operators to sell additional services, thus changing a cost center into a revenue center.
As we've seen from the examples above, use of the wrong metrics often leads to customer dissatisfaction and consequently lost revenue. Now that we've shown the importance of aligning metrics to business strategy, it should be clear that it is impossible for us to define "perfect" metrics applicable to all business situations - the metrics you use must be tailored to your company's business strategy. Through examples, this module will describe common supply chain metrics, both "good" and "bad". In most cases you will need to do some tailoring of the metrics to meet your specific situation, because your suppliers, customers, partners, and cross-functional teams always have unique needs.
We will cover four major classes of metrics for supply chains: service metrics, inventory metrics, speed/flexibility metrics, and financial metrics:
- Service Metrics
how you meet customer needs
- Inventory Metrics
how much inventory you have
- Time / Speed / Flexibility Metrics
how quickly can you respond to new developments
- Financial Metrics
how supply chain management affects your bottom line
We'll also revisit the Bullwhip Effect (covered in module SCM102) and describe a special metric you can use to measure how effective you have been in implementing strategies to mitigate the Bullwhip. First, let's start with service metrics.
1 "The Power of Virtual Integration: an Interview with Dell Computer's Michael Dell" by Joan Magretta, Harvard Business Review, March-April 1998. (back)
2 This figure assumes an airplane "waits" for the engine that was removed from it. That is generally not the case because airlines and maintenance bases keep inventories of spare engines. But since those inventories are costly, reducing those waiting times will still be valuable to an airline, allowing for lower inventories of spare engines. (back)
Save $25 on "Fundamentals of Supply Chain Management" bundle; buy modules SCM102, SCM103, SCM104, SCM105, and SCM106 together and save $25 (see course catalog for details)
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Total Reading Time: Approx. 1 - 2 hours (for average readers)
Word Count: Approx. 11,000 words
Author: Dr. Warren H. Hausman
Professor of Management Science & Engineering, Stanford University
Certificate: Counts toward Fundamentals of Supply Chain Management
- Alignment of Metrics with Business Strategy
- Service Metrics - Build-to-Stock
- Service Metrics - Build-to-Stock (continued)
- Service Metrics - Build-to-Order
- Inventory Metrics
- Speed Metrics
- Financial Metrics
- Bullwhip Metric
- "Bad" Metrics
- Applying Metrics Across the Entire Supply Chain
- Test Your Knowledge