By automating documentation processes, artificial intelligence (A.I.) has the potential to completely transform the logistics and supply chain sector.
According to McKinsey, only 21% of organizations are now using AI solutions, which is a result of the high implementation costs. Small and medium-sized businesses (SMEs) frequently find it difficult to invest in expensive A.I. technology, which can make it harder for them to compete with bigger businesses.
But there’s good news. No-code and low-code solutions have emerged as game-changers in the logistics and supply chain industry, particularly for smaller companies. These tools offer affordable ways to automate documentation processes without requiring extensive knowledge of programming languages.
Teams can use platforms like monday.com, Asana, or ClickUp, along with integration tools like Zapier or Make, to run their operations more efficiently and effectively, without increasing headcount and save with A.I..
No-code and low-code tools have numerous benefits, including reducing the need for IT expertise, speeding up development time, and increasing agility in responding to changing business requirements. In fact, according to a report by Gartner, low-code application development is expected to be responsible for over 65% of application development activity by 2024.
Here are five use cases where A.I. can benefit non-technical users in the logistics and supply chain industry and help them save with A.I..
Use Case 1: Recognizing Document Types
One of the more time-consuming tasks in the supply chain is going through emails and opening documents to determine their type. This can take 5-10 minutes per document, which adds up quickly.
With A.I., automation can recognize the type of document and update the tool automatically, saving time and effort. For example, platforms like DocuWare or Abbyy can automatically classify and categorize documents, such as invoices, bills of lading, or customs forms.
Use Case 2: Automatically Extract Data from Documents
Once the document type is recognized, the next step is to automatically extract relevant data from it. This can be done manually, but automation can streamline the process and improve efficiency.
For instance, tools like UiPath or Automation Anywhere can extract data from documents such as purchase orders, delivery notes, or packing lists, and populate the information into designated fields in other applications or databases.
Use Case 3: Set up Your Own Data Extraction from Emails or Documents
Not all documents may be covered by pre-built automation solutions. Many companies have their own internal documents, such as delivery notes or reports, that they need to extract data from. A.I. can help set up custom data extraction from emails or documents, saving time and reducing errors.
Tools like Parascript or Hyperscience can be used to create custom extraction rules and automate data extraction from specific document formats.
Use Case 4: Compare Documents
In the logistics and supply chain industry, accurate documentation is crucial to ensure smooth operations. Document comparison is an essential process to avoid mistakes and maintain consistency across documents.
For example, comparing labels on packages or customs forms for accuracy is critical to comply with regulations. A.I. tools like Kofax or DeltaXML can automatically compare documents and highlight differences, saving time and minimizing errors in the comparison process.
Use Case 5: Build Standard Operating Procedures
By automating the above use cases, logistics companies can build standard operating procedures (SOPs) that are based on automated processes. This can result in a streamlined and efficient workflow, where teams can simply load documents into the system and let the automation take care of the rest.
This eliminates the need for manual sorting, document recognition, and data entry, allowing employees to focus on more productive tasks and driving business growth.
But what about accuracy?
It’s important to note that A.I. automation may have varying levels of accuracy depending on the specific technology or platform being used. While A.I. has the potential to greatly improve efficiency and reduce errors in logistics and supply chain operations, it is not always 100% accurate.
The accuracy of A.I. automation in logistics and supply chain processes, as the strive to save with A.I., depends on factors such as the quality and consistency of the data being processed, the complexity of the tasks being automated, and the accuracy of the algorithms used by the A.I. systems.
It’s crucial for logistics companies to thoroughly evaluate the accuracy and reliability of any A.I. automation solution they consider implementing. This may involve conducting pilot tests, analyzing the performance of the A.I. system in real-world scenarios, and monitoring its accuracy over time.
Additionally, it’s important to have a fallback plan or manual backup processes in place in case of any inaccuracies or errors in the A.I. automation system. Human oversight and intervention may still be necessary to verify and validate the results produced by A.I. systems to ensure accuracy and reliability.
Regular monitoring and maintenance of the A.I. automation system is also crucial to identify and rectify any issues or inaccuracies that may arise over time. This may involve updating algorithms, improving data quality, or refining workflows to enhance accuracy and performance.
Examples of Websites for A.I. Automation in Logistics and Supply Chain
monday.com – monday.com is a popular no-code platform that offers customizable workflow automation solutions. It allows logistics companies to create automated processes for document recognition, data extraction, and document comparison, among others. Teams can use monday.com to streamline their documentation processes and eliminate manual steps, saving time and reducing the chances of errors.
Asana – Asana is another widely used no-code platform that offers automation features for logistics and supply chain operations. With Asana, teams can automate tasks such as document recognition, data extraction, and setting up standard operating procedures. Asana’s user-friendly interface makes it easy for non-technical users to create and manage their automation workflows.
ClickUp – ClickUp is a comprehensive project management and automation tool that can be used in logistics and supply chain operations. It offers a wide range of automation features, including document recognition, data extraction, and document comparison. ClickUp’s intuitive interface and flexible automation options make it accessible to users with varying levels of technical expertise.
Zapier – Zapier is a popular integration platform that allows users to create automated workflows between different apps and tools. Logistics companies can use Zapier to connect their email accounts, document management systems, and other tools to automate tasks such as document recognition, data extraction, and setting up standard operating procedures. Zapier’s extensive library of pre-built integrations makes it easy to create custom automation workflows without any coding knowledge.
Bitskout – Bitskout is an A.I.-powered platform that specializes in document automation for logistics and supply chain operations. It offers a range of automation solutions, including document recognition, data extraction, and document comparison. Bitskout’s advanced A.I. capabilities enable accurate and efficient automation of documentation processes, helping logistics companies save time and reduce errors.
Conclusion
In conclusion, A.I. and automation technologies are transforming the logistics and supply chain industry, and smaller companies can benefit and save with A.I. from affordable solutions such as no-code and low-code platforms.
These tools enable non-technical users to automate documentation processes, such as document recognition, data extraction, and document comparison, resulting in significant time and cost savings.
With the availability of user-friendly platforms like monday.com, Asana, ClickUp, Zapier, and Bitskout, logistics companies can streamline their operations, increase efficiency, and gain a competitive edge in the industry, without the need for extensive IT expertise or a dedicated development team.
Embracing A.I. and automation in logistics and supply chain operations can lead to improved productivity, reduced errors, and better customer service, ultimately contributing to the growth and success of the company.