Automating logistics processes requires high-quality data. This is because the software can only execute standard processes automatically if the basic requirements are met. And it starts with the master data.
With the advancing tide of digitalisation, data has become the most important success factor in logistics. That means data quality management is the first step in improving efficiency with digital processes. It is a task with many individual steps:
Once you have met these objectives, you will be perfectly positioned to manage your data quality successfully.
To determine the goal of a change, logistics providers first need a definition of data quality they can work with. One of the benefits is being able to define important criteria that can be used to measure data quality. The definition begins with formal factors: The information must be complete, clear, correct, current, precise and consistent over the entire process without any redundancies – i.e., not stored more than once. Other drivers of data quality include relevance,, uniformity, reliability and understandability.
If you want to improve your data quality, you have to start with the master data. Addresses, rates and terms and conditions have to be correct for transport processes to run efficiently. In addition, it is important to constantly update actual data from ongoing processes, especially dimensions, weights, transit times and wait times. This may require a separate master database. Some well-known carriers do not maintain a "customer" data category. Their systems only contain sender and recipient addresses. The invoice is then sent to one of these addresses after delivery depending on whether it was a procurement or shipping order. Companies with multiple locations that place orders with different offices operated by a freight forwarder will thus not be recognised as a single customer. This can be a problem when striving to provide good customer service or setting up internet-based service portals for customers.
In addition to setting up a central master database, you will need to integrate all your data sources in a single point of truth in order to achieve the data quality that business processes require. It begins with the customer order, whose content must be provided over interfaces to all connected services and functions from the moment the order is entered or the data received. For this to succeed, all the applications have to be linked together via application programming interfaces and event sourcing platforms to form a digital end-to-end process. Regular plausibility checks are required to ensure data integrity and data quality. Key measures also include company-wide awareness raising of the benefits of high data quality and the allocation of clear responsibilities. Data quality will only improve once everyone understands why it is so important.
Efficient logistics processes require data to be fully available throughout the process. Logistics service providers can achieve this goal if they maintain central master data, integrate all process-relevant data sources, and invest in systematic data quality management. The benefits: trouble-free processes and a more successful business.
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