It's that time of year again. The leaves are turning, and the kids are back in school. But why should they have all the fun? It only seems fair that logistics professionals should also have the opportunity to brush up their skills or learn new ones.
For example, now's a great time to take another look at your performance measurement program. If your operation is typical, there's a good chance your metrics protocol could use a little tweaking. In most cases, all that's required is a quick review of the four fundamental subjects: math, English, history, and science. What follows is a look at how revisiting each of these areas can help you improve your performance measurement program.
When it comes to math and performance measurement programs, a common mistake is to confuse numbers with metrics. They may look alike, but they're not one and the same. A metric should provide meaningful information that is actionable; it is not just a simple number, like a count of how many packages were sent on a particular day. For the number to be meaningful, you have to place it within a larger context—i.e., by performing some calculations. Do that, and you have a metric.
Let's say you want to find out how well your shipping department is performing. You make some inquiries and learn that DC sent out 100 error-free shipments yesterday. But that tells you absolutely nothing about the quality of your performance. What you really need to know is what percentage of your total shipments were error-free. If it was 100 out of 100 (or 101), you're doing well. But if it was 100 out of 200, you have a serious performance problem. The point is, knowing the total package count for a given day doesn't help you much. But knowing the percentage of error-free shipments on that same day tells you a lot. It's a meaningful metric—one that offers information you can use.
You can use the same approach to zero in on the source of a problem. For instance, if you're trying to determine what's causing your shipping errors, you might decide to calculate the percentage of packages sent with bad labels. Again, that's information you can use. If you find it's 80 percent, you know labeling is something you'll have to address right away. But if it's just 1 or 2 percent, you know you'll have to look elsewhere for the source of the problem.
What does English have to do with metrics? A good deal, it turns out. A good metric must be clearly defined, free of ambiguities and leaving no room for interpretation. If a metric doesn't mean exactly the same thing to every party in the supply chain, you're inviting all kinds of miscommunication.
Take "on time shipments," for example. "On time" means many different things to different people. The guy in shipping might interpret it as meaning the shipment left the DC on schedule. The truck driver might see it as delivery on the agreed-upon day. But to the customer, "on time" might mean the shipment was delivered not just on the appointed day but within a 15-minute to 1-hour window of a specific time. Those differences might sound trivial, but they could lead to all kinds of misunderstandings. For example, consider the customer service implications if the supplier thinks it's meeting expectations by delivering the order on the right day, but the customer is counting on having its order arrive between noon and 2 p.m.
It's the same with the "percentage shipped error-free" measure. What exactly does "error-free" mean? What elements of a shipment need to be correct for it to be considered free of errors? There are a lot of elements to consider here: shipping documentation, content, quantity, packaging requirements, labeling requirements, and on-time delivery, to name a few. A well drafted metric should make it clear exactly what's involved and how it should be measured.
At first glance, it might seem that history has little to do with metrics programs. But actually there's a connection. After all, even the best metric doesn't mean very much in isolation. To get a full understanding of your performance against a metric, you need a sense of its history to see where it's trending.
For example, say you've just found out your operation's error-free shipment rate yesterday was only 50 percent. While we can agree that this is a clear indicator that something's gone awry, our understanding could be so much richer if we understood its history. Is the 50 percent performance an aberration, something that has only occurred once on one particular day? Or is it a long-term trend? The answer will undoubtedly shape your response.
And if you still need convincing, consider this: If your goal is to improve performance against a specific metric, you'll have no way of knowing whether you're on track to hit that target unless you're monitoring performance over time.
Once you have a clear understanding of how a measure is calculated, what it means, and how your operation has been performing against it over time, what do you do with that information? Like any good scientist, you must test your data and delve more deeply into the findings. For example, if you're trying to reduce shipping errors, you need to determine the source of the problem. Is it documentation, content, quantity, timeliness, labeling, or packaging? A root cause analysis will help you figure out where to focus your attention.
For example, you might find that 50 percent of your problems come from shipping the wrong item, while 30 percent come from shipping the wrong quantity. The remaining 20 percent might be split more or less evenly among the other components, documentation, timeliness, and labeling and packaging. That tells you the underlying problem isn't shipping; it's order fulfillment at the warehouse.
Solve that problem and you eliminate the majority of the shipment errors. It's important to have the curiosity of a scientist and the willingness to test data down to the root cause in order to drive improvement from the metrics.
Now that you've brushed up on the basics of math, English, history, and science, it's time to re-evaluate your metrics program. Chances are, you'll discover one or more opportunities to give your program a quick jolt and reinvigorate the performance of your operation.