Watson Says... |
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| In Part 2, we will now discuss best practices in using these methods and how to determine the root cause of problems. |
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1. Keep in mind how you will make money by measuring service levels. You don’t want to measure service levels for the sake of measuring. Most times the goal comes down to making more money. Use this objective to figure out which measure you want to use. For example, if one of your major customers requires that you hit 95% OTIF to keep the business, then you use that measure. If you think you can gain a competitive position by having better service, you may have flexibility to choose the metric and how you best calculate it. And, conversely, if your customer orders in large quantities and only vaguely cares about the due date on the PO, you probably don’t need to measure service levels too strictly.
2. Don’t think you have to have one measure. It can be a mistake, and lead to endless arguments to have to come down to one measure. Instead, like we discussed above with the original due date versus the adjusted due date, it might make sense to track both. Likewise, you may want to track Unit Fill Rate and OTIF to see how different the measures are with just a difference of ship date versus arrival date. Likewise, you might want to measure OTIF with giving yourself partial credit and without. In general, you will find the multiple measures can give you a richer picture of what is happening.
3. If you need a global measure to compare, keep it simple. If you need to use the measure to compare your different sites or business units, it is best to keep the measure relatively simple. All sites may not have the same ability to measure complicated service calculations (like Perfect Order). So, pick a measure or two that all sites will be able to support.
4. Balance the service metric with other metrics. This is an issue with any measurement system- improving one measure may hurt another measure. In the example here, if you are trying to maximize OTIF, it is important that you keep a complete mix of your products in inventory so you are ready to ship. If your manufacturing organization is trying to maximize utilization, they may focus on long production runs and not be willing to change the line to make another product-- even if you need it to provide good service. In another example that we’ve seen frequently, if a group is measured strictly on OTIF and not on expedited transportation cost. That is, the group focused on OTIF may hold on order for a day or two to make sure it has all the product ordered and then pay extra expedited shipping cost. This could be counterproductive for the organization if you are frequently paying expedited freight for a few more cases of product.
Tracking Root Causes
The goal of tracking the service level is to improve it and make sure you continue to meet it. Just tracking and reporting on the measure will provide some of the benefit (this is the Hawthorne effect). Knowing that it is being measured, people will be more conscious of making sure orders go out on time. This effect can be significant.
However, to dramatically improve your service level, to get last bit of improvement (to go from 93% to 99%), or to make sure your changes are long-lasting, you need to use the service measures to help understand the root cause of the problem. Then, you can fix that root cause.
For example, if you track both original data and the adjusted date and you notice a big difference, you will realize that you have a problem with the date changing. Now, you want to dig into that issue to see if the problem is with your team calling the customer to change the date, whether the customer requests an unrealistic date and it needs to be changed, whether you are changing the date based on when you schedule the truck and so on. Each one of these issues has a potentially different solution.
You can do more with the data you are collecting. You will have information on all your orders-- both the good ones and the ones where you had a service failure. You can then use new machine learning algorithms to sort through this data to look for potential patterns for problems. For example, do orders with more than 10 items tend to be late? Do orders that are right at the cut-off between one and two day shipping (~400-500 miles) tend to be late? When you pick orders too close to the cut-off time, do you you increase the chance of being late? And, so on. The algorithms can analyze the root cause of the problems and allow you to address those root causes. This is opposed to the standard way of manually going through the service failures and labeling the failures by hand. The manual method is too time consuming and introduces too much bias.
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