January 16, 2024

Data entry operations from TD to WMS: why are they time-consuming?

For companies involved in logistics, managing data – and particularly shipping data – requires a lot of human resources, time and accuracy.

Data entry operations from Transport Documents (TD) to Warehouse Management Systems (WMS) must be conducted with a high level of efficiency in data processing and management.
Processing the data included in transport documents and entering them into the WMS can indeed be done manually, but this can lead to huge time losses, errors and general inefficiencies.
Given the sheer volume of documents that any logistics company has to manage today, the need to speed up the processing of transport documents emerges overwhelmingly, and this urgency calls for more advanced solutions than just paper document management.
Among various technological solutions, the use of Artificial Intelligence (AI) is an increasingly popular trend in the supply chain and logistics sector: by combining process automation and AI-based technological solutions, manual data entry operations can be simplified, automated and made much more efficient.

So let's see how manual management of the data contained in the TDs incurs problems of time and resources, and let's highlight how supply chain managers or logistics managers are better off pivoting their internal processes, putting aside the manual data transfer operations from the TD to the ERP or WMS software and letting the AI do it instead of the workers engaged in this highly time-consuming activity.



The data entry process from TD to WMS: a common issue in logistics

The transport document, or delivery note, is a document that must be issued by companies to justify or prove the transfer from one place to another of goods or raw materials that are the subject of a business transaction, even in the case of two facilities of the same company.
The process of data entry of transport document information into a WMS is one of the most critical activities in logistics. Managing shipping data requires accurate recording of all cargo details in order to ensure proper traceability.
One of the most traditional methods is manual data extraction: a human operator has to look at the TD and enter or copy the data into the ERP and WMS manually.

But manual data entry of transport document data into ERPs or WMSs can cause several problems.
First, these operations require lots of human resources, which could be used in higher value-added activities. In addition, when considering the large volume of shipments that a logistics company must handle on a daily basis, most of the resources would be devoted to data entry activities, slowing down the process and locking up large portions of capital.
In addition, the manual data entry process is time-consuming. This leads to increased lead times and the possibility of errors due to employee fatigue and distraction.
Third, the manual process can cause errors due to poor readability of the TDs or weak lines of communication between the transport document suppliers and the logistics company.
For all these reasons, manual data management of TDs can lead to delays, inefficiencies and errors that damage the company's image and profitability.

Processing TDs (storing the data and then entering them into the ERP or WMS) is thus a critical process for logistics companies, which can be extremely time-consuming: the time it takes to process these documents can affect the entire logistics process and cause cascading delays in the delivery of products to customers.

“Manual data management of TDs can lead to delays, inefficiencies and errors that damage the company's image and profitability.”

It seems clear, then, the central reason why supply chain and logistics companies are increasingly choosing document automation technologies: these solutions offer a number of advantages over traditional paper-based processes, including increased accuracy and efficiency, reduced costs, greater visibility into the supply chain, and the elimination of repetitive tasks for human workers in favor of other high value-added activities.



Digitizing the manual data management of TDs

Document digitalization has become an essential process for companies in various industries.
With document digitization, logistics companies can make sure they have the information they need, when they need it, to make informed decisions and keep their supply chain running smoothly.
Many companies that need to process TDs (storing the data and then entering it into the ERP or WMS) have to copy the data and manually compare it with the data sent by the supplier, and we have already seen that these activities are time-consuming and easily prone to errors.
With the growing need to automate data extraction from paper transport documents, many organizations are turning to innovative technologies to streamline the manual data entry process.
Automated document reading and automated data entry are two of the technologies that are revolutionizing the way companies manage their documents.
There are several methods and tools for performing document reading and data extraction tasks, of which we can highlight the two most popular:

Optical Character Recognition (OCR) is a method that has gained popularity in recent years. This technology uses computer algorithms to recognize characters in a document and convert them into digital text. Although this method is faster and more accurate than manual data extraction, it still has limitations in handling unstructured or semi-structured documents.

Intelligent Document Processing (IDP) is a more advanced method that combines OCR with Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate data extraction from structured and unstructured documents. IDP can accurately extract data from complex documents such as invoices, contracts and purchase orders. It can also learn from past data and improve its accuracy over time.

Ultimately, the benefits of digitizing transport documents are considerable.
First of all, the processing of TDs becomes automatic, simplifying operations and reducing the work burden on employees. Manual document compilation is eliminated and the risk of errors is reduced, saving valuable time. In addition, digitization of transport documents provides a clear and immediate view of data, increasing transparency in goods and commodity management operations.

By digitizing the manual data management of TDs, greater efficiency and accuracy in data entry activities and a significant reduction in business costs can be achieved.



How to speed up data entry operations with AI

To overcome the problems we have listed so far and, at the same time, to reap the full benefits of digitization, it is necessary to turn to the use of technologies such as Artificial Intelligence to automate and speed up the manual data entry process, revolutionizing it completely.

AI can be used to capture data from transport documents, eliminating the need to manually transcribe all the information. This can speed up the processing of TDs, and the time required to perform data entry processes in WMS can be greatly reduced.
Artificial Intelligence-based solutions such as WENDA AI Document Processing (IDP) enable supply chain managers or logistics managers in logistics companies to extract data from documents and automatically enter them into the ERP and/or WMS, regardless of how these documents are structured, formatted or digitized.

Among the benefits of implementing AI-based document and process automation solutions is the ability to reach more customers with less effort: for logistics and supply chain companies, document automation solutions will have a direct impact on the way they serve and respond to customers.
Automating time-consuming manual methods will enable customer service staff to respond quickly to inquiries and resolve problems. This will help customer service staff use their time to help more customers during working hours.
In addition, document and process automation technologies do not require breaks and rests. This ensures customers 24/7 availability and service guarantee. Even when the customer service is based in Italy but the customer himself is based, for example, in Asia or another continent with a time zone very different from that of the company. Automation and automated data entry can ensure continuity of service while staff enjoy well-deserved breaks, weekends, time off, and vacations.

Another benefit of using AI-based automated solutions is keeping pace with digital transformation.
Adoption rates of automated document generation continue to grow: by 2025, the global market for document management systems will reach $10.17 billion with a CAGR of 13%1. As software vendors launch more advanced systems, companies readily implement the proposed innovations. They move from cumbersome paper document management to fully automated document generation. In addition, companies plan to spend more on automating business processes and innovations. By 2026, global spending on digital transformation is expected to reach $3.4 trillion2.

Incidentally, operational workflows can also be set up based on data extracted from the TDs: when digital transport documents are attached as PDFs (or in other formats) to emails sent to the logistics company, workflows can be quickly configured based on AI models that are continuously trained by company users.
However, we will look at these types of process automation in another article.

It is clear, then, that with the growing implementation of document and process automation solutions, AI can become logistics companies' best ally, enabling them to handle higher shipping volumes efficiently, increase employee productivity (now shifted to higher value-added activities), reduce processing cycle times, errors and paper consumption, and increase customer satisfaction.



Conclusions

In short, data entry operations from TD to WMS are a common issue for logistics companies, as they require a lot of human resources and time, while efficiency and accuracy must be high. One solution may be document digitization, which can be optimized through the use of advanced technologies such as Artificial Intelligence.

AI can automate the processing of TDs, eliminating the need to manually transcribe the information contained in them into ERPs or WMSs, reducing the time required and the risk of errors. In addition, process automation can provide increased customer service availability, improved staff efficiency and business cost savings.

The use of advanced technologies such as AI is therefore effective and practical for logistics companies that want to improve their efficiency and the way they manage their documents.



Notes

1. See the article by Inkit titled Automated Document Generation in 2022 & Beyond: Technology, Standards, and Market
2. See the report by Statista titled Digital transformation - Statistics & Facts