We have previously discussed the importance of automating the management of Instructions for the Bill of Lading and how this automation can be a catalyst for efficiency in logistics.
We have previously seen how streamlining data entry, simplifying the creation of Bills of Lading from emailed Instructions, and embracing Artificial Intelligence (AI)-enhanced document automation leads to increased productivity and clear improvements in accuracy and operational efficiency. However, our journey does not stop there.
We will further investigate the adoption of document automation technologies as a crucial element in achieving operational efficiency in customer service, explore the methods to be used to streamline processes for managing Bill of Lading Instructions, and examine Wenda's proposed solution of combining document automation and Artificial Intelligence.
A more efficient customer service
That efficiency and productivity are the keys to business success in the world of logistics, and that these can be achieved and increased through automation, is now a given.
Indeed, supply chain and logistics automation is a technological response to the complexity of today's environment: it is a tool that helps companies adapt quickly to new changes, since traditional supply chain management, with its mainly manual processes, can no longer offer companies this flexibility. And above all, this efficiency.
Today, in fact, much of the information needed to execute logistics and supply chain operations-including Bill of Lading Instructions-is manually extracted from data sources such as emails or attachments.
It is therefore clear how customer service departments play a crucial role in the daily, numerous and often repetitive data entry activities for creating Bills of Lading from instructions provided by customers.
In this context, being able to automate the processing of Instructions for the Bill of Lading proves to be a crucial element in increasing back-office productivity and consequently improving customer service performance.
Thus, if conducting these activities in the traditional way-that is, by manually copying and pasting data-carries the risk of errors and is an obstacle to maximizing operational efficiency, the value of automation must be highlighted.
Indeed, it derives primarily from the efficiency it creates: for example, one of the largest global consumer goods companies reported that the use of advanced automation enabled it to solve workflow problems 30% faster and helped improve employee productivity by up to 50%1.
“Traditional supply chain management, with its mainly manual processes, can no longer offer companies flexibility and, above all, efficiency.”
Let us then look at what solutions can be adopted to streamline document management processes in general, and of Bill of Lading Instructions in particular. We will probe some automation technologies and then see the solution proposed by Wenda: among other things, in fact, Wenda AI increases staff productivity by 50% and provides a significant improvement in document management and elaboration processes to achieve a high level of operational efficiency in customer service.
How to simplify the management processes of the Instructions for the Bill of Lading?
Many companies offering transportation services receive specific instructions from their customers on what information to include in the Bill of Lading. This is often one of the most time-consuming activities on the part of those responsible for creating the bill of lading. Therefore, automating this activity can speed up the process of creating Bills of Lading as a whole.
And thus, investing in advanced technologies to automate the data entry of Bills of Lading is crucial to enable the back office to process more paperwork in less time, simplify the creation of bills of lading, and increase overall customer service productivity and customer satisfaction levels.
The world of technologies used for document automation is truly vast. These advanced technologies can be leveraged in different methods and applications depending on the scope, which usually have well-defined goals and can work either independently or in combination with each other.
Before moving on to an analysis of Wenda AI, which increases staff productivity by 50%, let us briefly consider some of the technologies that can be used to simplify the creation of bills of lading.
Natural Language Processing
Natural Language Processing (NLP) is a branch of Artificial Intelligence that deals with the interactions between computers and human language. It is the ability of a computer program to understand human language as it is spoken and written.
NLP enables computers to understand natural language as humans do. It uses Artificial Intelligence to take input from the real world, process it, and make sense of it in a way that the computer can understand. Machine Learning algorithms produce an estimate of a pattern in the data based on some input data, which may be labeled or unlabeled. In this way, the algorithms make predictions or classifications. The algorithms constantly evaluate and optimize the classification or prediction process, autonomously updating the parameters until a threshold of accuracy is reached.
Thus, one can use NLP as a technology for document automation, often or almost always in combination with other technologies-such as AI.
Optical Character Recognition
Optical Character Recognition (OCR) is the use of technology to identify printed or handwritten text characters within digital images of physical documents, such as a scanned paper document. The basic process of OCR involves examining the text of a document and translating the characters into a code that can be used for data processing. This technology is sometimes referred to as text recognition.
Usually, OCR technologies have two components: hardware for digitizing the document and software for converting documents into machine-readable text. The software can be combined with AI technologies.
Intelligent Document Processing
Intelligent Document Processing (IDP) refers to extracting information from paper and electronic documents and using it to enable end-to-end automation of document-centric processes. It leverages Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) and advanced OCR engines.
IDP solutions capture, extract, categorize, and analyze information from different types and formats and enable users to seamlessly integrate data output into workflow automations. A common IDP solution should include the following basic capabilities:
- Ability to process unstructured and/or semi-structured data
- Data recognition and classification
- Automated data extraction
- AI and ML tools for categorization and analysis
- OCR to capture data
- Integrations with other data management software
In any case, it should always be kept in mind that these technologies do not work perfectly well in isolation, but should be brought to value as integrated solutions.
Wenda AI helps you automate your Bills of Lading
Let us now see how Wenda AI can be used to extract data and automatically create Bills of Lading, turning the creation of Bills of Lading into an automated process. This step not only dramatically improves efficiency but also offers new prospects for success in logistics.
Applying automation to the case of Instructions for the Bill of Lading allows the necessary data to be automatically extracted from the instructions received via e-mail and bills of lading to be created quickly, eliminating the need for repetitive and error-prone manual work.
This means that customer service staff can get to free up as much as 80% of their time spent on back-office activities to focus on more strategic activities and value-added services for customers.
It is often the case that Bill of Lading Instructions are accompanied by a variety of documents in different formats, as each company chooses the format best suited to its needs when sending instructions to the carrier.
Wenda AI represents a breakthrough in the management of Bill of Lading Instructions because Wenda's proprietary AI algorithms do not stop at mere text recognition, but can analyze and interpret the data. This process not only speeds up processing but also minimizes the possibility of errors.
“Customer service staff can get to free up to 80% of their time spent on back-office activities to focus on more strategic activities and value-added services for customers.”
Wenda AI offers new opportunities to further simplify the process: the Wenda platform selects the email and/or attachments received via email from the customer, downloads them, and sends them to the AI model for data reading and extraction. Wenda's proprietary Artificial Intelligence reads and understands the details, and then the specialized AI model analyzes the instructions, identifies various details such as sender, consignee, notified party, reservation number, place of receipt, ocean vessel, port of embarkation, port of discharge, final destination, freight payable, package number/type, description of goods, gross and net weight using NLP and Machine Learning.
The following is a concrete example of a workflow taken from a real case study:
- The customer sends the freight forwarder an email with Bill of Lading Instructions attached.
The shipper receives invoices, letters of credit, or simple instructions via email. In this case, Wenda uses a classifier AI model that can analyze and distinguish the type of document received, and then shares it to the specific AI model (invoices, letters of credit, customer instructions) that will extrapolate the data. - Wenda then takes in the emails received from the shipper, extracts the data from the attachments that are used to create the Bill of Lading, and sends an email to the shipper's back-office.
- This email sent by Wenda to the shipper's back-office contains text in which the sender of the customer email and the attached file name are made explicit.
The attachments to this email sent by Wenda include both the original attached file and an excel file: the latter is the end result of Wenda's automated processing. The same result could be displayed on the Wenda Platform or it could be transferred to the IT system in use.
There is thus, as we already mentioned, an understanding of the context in which the document being analyzed is moving, unlike a simple OCR software.
The most relevant information is extracted from the email or attachments, regardless of the format or layout of the document. More specifically, the fields to be extracted can be:
- Shipper
- Consignee
- Notify
- Port of embarkation (loading/loading)-num car max.
- Port of disembarkation (unloading)
- Number of containers
- Container (Container code, Container type, Number of packages, Gross weight, Seals, Any customs codes)
- Description of cargo
- Notes (agent at destination)
Using Wenda AI solution enables logistics companies to achieve remarkable results:
- 80% savings in back-office time: by reducing the time spent on document management, shipper staff can focus on more value-added, more strategic and more creative tasks;
- More efficient allocation of human resources: the freight forwarder can automate the processing of thousands of documents, greatly increasing the productivity of human resources and distributing workloads more efficiently;
- Greater scalability in customer service at the operational level: automation allows the forwarder to expand its operations. This element can be further enhanced by the potential of automation.
Conclusions
Automating the management of Instructions for Bills of Lading with Wenda AI is a powerful lever to increase efficiency and productivity in customer service in logistics companies.
By simplifying the creation of Bills of Lading through AI-powered document automation technologies, companies can achieve substantial time savings and highly visible improvements in productivity and operational efficiency, fostering faster and more profitable supply chain management.
The time has come to fully enter the automation era, and Wenda is ready for this challenge!
Note
1. IBM Institute for Business Value (2018): The Evolution of Process Automation