Modified: 06-01-2021 Published: 05-18-2021
As you probably know from your own experience, almost all areas related to customs are data-driven. Often there is a lack of data to complete customs declarations, supplier declarations, certificates of origin or simply the correct customs tariff number.
Therefore, a clean master data management is essential for every company. This not only guarantees the high quality of your business processes and prevents fines, but also saves you a lot of time.
It is important to be clear: where do I currently stand? Am I already in the middle of the project and have lost the overview, or is it necessary to convince management about the master data “issue” first? We have put together the most important tips for efficient and sustainable master data maintenance for each phase – before, during and after your project.
We keep finding that a lot companies lack perspective when it comes to master data management. Many people are therefore not aware of the different perspectives that master data actually encompasses. When planning a project, it is important to be clear about the relationship between time, quality and cost. You will never reach all parameters with the same intensity.
For example, if a customs inspection is pending and you have to revise a large amount of data, e.g. 70,000 articles, in two months, then it will not work to check every single article in detail.
In addition to the parameters mentioned, it is also necessary to check which systems are actually affected. So who provides which data, and where does the data end up?
If you are not sure what is actually meant by “master data”, then take a look at our blog on the topic of “master data as the foundation of digitization“.
Master data maintenance begins even before the actual project start. Good preparation is the be-all and end-all for successful project completion. Before you jump straight into the planning, you should first get an overview of the data sets that you want to check. The best thing to do is to get an excerpt of all active articles from your system.
And this is where our first tip comes into play: Less is more! It is better to concentrate on individual attributes that you want to improve, for example first the classification, leaving the recording of the original data to a later point in time. If you want to do everything at once, you run the risk of getting bogged down.
In the preparation phase, it is important to get an overview of the quality and completeness of the master data. Therefore, pay attention to which data is still missing and which may be out of date. Invalid customs tariff numbers are a typical mistake in master data! Especially regarding to annual changes in the customs tariff, we often experience that changes are not entered – you should definitely avoid that.
The best thing to do is to get an idea of the status quo in your company and find out which data sets are currently causing the biggest problems and why. Don’t forget to include the systems in which your data is stored. This may include customs systems, service providers or various ERP systems.
The aim of the preparation phase is to get a comprehensive overview of the totality of data and to determine what the desired result should be at the end of the project. The goal should be as measurable as possible. Because in the subsequent planning phase it is important to identify items that are actually required.
Try to make your project plan as realistic as possible. Many companies set a very strict schedule for large master data projects and underestimate the effort that they entail. However, saving time on master data due to a lack of time can cost you dearly later – often in the truest sense of the word if an additional payment is required due to a lack of information. So it’s best to plan enough time for your projects and keep an eye on quality and costs.
When planning, make sure to think about “tomorrow” as much as possible. Try to use the knowledge from the project for future processes. The best way to do this is to create a manual in which you can record the newly acquired knowledge.
Here are some pointers to look out for:
Which article can be used as a “reference” for others? Once you have categorised a needle, the customs tariff number may also apply to all similar items, so you only have to do this job once.
This is where IT becomes relevant: If you have a good database, you can consider how to use attributes, so-called tags, to carry out partially automated classification and evaluation of master data.
If you have created a decision tree, explaining why an aircraft part is or is not an aircraft part, this can possibly also be used as a reason for further articles. TIP: Always create justifications based on the general regulations.
Our tip: While the project is running, think about future processes and how you can optimize them and make them more efficient. Perhaps the same colleagues are already working on the project, as will process it later on. That makes sense because then the appropriate product know-how is already built up. Or should the process be outsourced after all, because there is simply not enough time internally? All of this should be taken into account during the course of the project.
After the project, it is important to determine how the process can be optimized. To do this, you should think about how much time the process for determining master data should take in the future. You should also agree who is responsible for maintaining and monitoring the master data in the company. Make sure to consider the systems in which data is maintained. Our tip is to centralize data maintenance at the beginning of the value chain, where possible.
Also try to keep an eye on which data sets are determined manually and which are determined automatically. If the data acquisition is automated or partially automated, you should pay attention to how the “machine” is checked. The systems should also be checked for corresponding field validations.
In order to guarantee the continuous quality of the master data, you should check randomly every few months (for example by means of an AQL audit) whether all data records are current.
The aim should be to develop a process, after the end of the project, that is efficient and at the same time delivers high quality.
Nothing works in foreign trade without good master data. It is the driver for all customs processes – and also for their successful digitization. Reason enough, therefore, to ensure that master data is properly maintained in every project phase.
