Logistics processes connect many players. Their IT systems often speak completely different languages. That, in a nutshell, is the problem that automatic data conversion solves. Its goal is to translate one data format into another without users noticing.
Logistics as we know it today would not be possible without electronic data interchange. For example, a freight forwarder can only arrange for overnight transports if it knows in advance how many goods have to be moved, and what their weights and dimensions are. That is why dispatchers need the shipment data in the transport management system (TMS) to be as complete and high-quality as possible. This data will have to be converted multiple times since all the companies involved in this process will never use the same applications, even if they belong to the same transport network. If process-related data and documents are available in different file formats, they will have to be transferred across suitable interfaces for downstream software processing. Depending on the task and area, they may not be adequately "translated" from one format to another. Plus, the contents of different files may have to be merged into a new or different data type. This is known as data integration.
Data records are converted by transferring them from one format to another format that has a different structure. Here is an example: one file lists the pickup location and date in the exact opposite order to another file. Or a data record for a time-sensitive delivery does not separate the delivery date and time into different fields. Depending on the extent of the differences between the file formats, the conversion can be done by simply mapping fields to one another.
The conversion is a lot more challenging when two different pieces of information are stored in the same data field. In that case, the content will have to extracted. To do this, a process checks the string for the known data format for each value as well as any existing separators. For example, "08:00 15/06/2021" for time and date, which in this case is 8 a.m. on 15 June. However, the values can also follow each other directly, in which case the same date reads like this: "080015062021". In this case, the information has to be derived from the sequence of digits.
The example shows that the data conversion can be arbitrarily complex depending on the content and structure. This is especially true when it comes to data integration, i.e., merging information from different files. There are also different encryption methods used when transmitting data to other companies. They are adapted for transport protocols such as SFTP (general), OFTP and OFTP2 (automotive industry) and AS2 (e-commerce).
In all areas of logistics, processes are controlled by IT applications, services are monitored with specific software, and tasks are performed using a specialised solution. Databases work in the background, providing the programs with the data records they need in the target format. This works smoothly when contained within a single solution.
The difficulties often start when data is being exchanged between applications from different providers. If the values in individual data fields do not follow the same syntax, they will have to be converted. Here is a simple example: a date is written in one order (day-month-year) in one data field and in the opposite order in another. No one single data format standard has been established in logistics even after more than four decades of digitalisation. Even the most common data formats such as Fortras Release 100 and EDIFACT have countless variants with additional data fields that cannot be simply exchanged directly with each other.
Modern software architectures with application programming interfaces (APIs) have significantly simplified the handling of different data formats. However, the formats, structures, values, and characters is far too varied to manage without conversion. That is because the pace of logistics, particularly in distribution, has continued to increase, while many freight forwarders now operate in multiple networks. The networks generally provide highly similar services but define their products and statuses very differently.
So, in order to maintain the quality of their processes, freight forwarders working in multiple alliances cannot avoid converting current production data in real time. It would simply be unmanageable for employees to decide for themselves, based on the network, which logic to follow for each individual shipment. In addition, some industries – such as the automotive industry or e-commerce – require extensive logistics services but the companies in these sectors have developed proprietary data formats for their specific needs. These companies expect fast processes and flexibility from their logistics service providers. Luckily, effective conversion can make their expectations a reality.
Data conversion provides a method for:
The ability to convert data to all available formats has become a powerful business accelerator. It eliminates the need for lengthy discussions about interfaces and data formats.
Data conversion allows logistics service providers to easily collaborate with customers and partners across company boundaries. Everyone involved in each transaction has all the information, provided in the exact same data format that they use in their own IT systems. There are no obstacles to exchanging data, and freight forwarders can focus on their core business: getting goods to their destination promptly and safely.
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