The Warehousing Education and Research Council's annual benchmarking study, DC Measures, shows a majority of respondents believe they are performing better than their competitors. This perception gap is common for warehouse managers, especially when using metrics to improve performance. To bridge the difference between perception and reality, we have to understand the basics and go back to school on metrics. Joe Tillman, Founder TSquared Logistics
Metrics in your warehouse should be re-evaluated every year on the following basics – math, grammar, history and science.
One common mistake that people make is forgetting to apply math to their metrics. For each metric in your arsenal, there should be a detailed calculation supporting it. A metric should provide meaningful information that is actionable; this means that it is not just a simple number, like a count of the number of packages sent or the number of fatal shark attacks. By placing a simple number within the context of something larger, the number takes on meaning and becomes a metric.
Telling someone that you shipped 150 orders yesterday doesn’t provide a good picture of reality. The percentage of orders shipped error-free -- that’s informative. If we know we sent a total of 150 packages yesterday and 50 percent were sent error-free we can develop an accurate picture of our performance. And that only 75 packages were sent yesterday without error is not so good. A metric must be the result of a calculation in order to be meaningful.
Another important characteristic of a good metric is a clear and well-documented definition. Take the percentage of orders shipped error-free; what does “error-free” mean? There are several: shipping documents, content, quantity, packaging requirements, labeling, and on-time. Error-free should be counted when ALL of these attributes of a shipment are present. What are the parts of a metric that need to be included before you turn sub-par performance into excellent performance? A documented definition of the metric is needed to make it clear to everyone involved what is expected and how to measure it.
A metric placed in the context of something larger can be meaningful on its own; however, it is even more powerful to understand how this metric relates to past performance. We recognize poor performance by knowing the error-free ship rate is 50 percent. But our information about the error-free ship rate could be so much richer if we understood its history. Is the 50 percent performance an aberration that has only occurred on this one day? Or is it a running trend indicating systemic problems across time? If you have set a goal for a metric to improve performance, you cannot know whether you are progressing towards the target if you do not track the trend of the measure over time.
Like any good scientist, you must test your data and dig more deeply to understand what is driving poor performance. What is the source of the 50 percent error-free ship rate? What is the biggest contributor: documentation, content, quantity, on-time, labeling or packaging? By conducting a root cause analysis you will be able to identify which of the core components of the definition are falling short and by how much. Also it will identify how much each of these components contributes to the overall problem, which will help you determine best course of action to improve performance.
Developing a good picture of reality takes a little time and effort. If your measures are not helping to improve your warehouse operation, then it is time to go back to school to give your metrics program a quick jolt and re-invigorate the performance of your operation.
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