NEWSLETTER
Logistics plays a fundamental role in the supply chain of companies of all sizes. It involves coordinating the movement and storage of goods, services, and information in a way that maximizes efficiency and minimizes costs.
However, managing logistics can be a complex and time-consuming task, especially for businesses that rely on manual processes. Tasks like data entry and document processing can also be prone to errors, which can lead to losses, delays, and other supply chain issues.
This is where logistics automation comes into play. By using technology to automate various tasks in the logistics process, companies can significantly improve efficiency and accuracy, reduce costs and errors, and improve customer satisfaction.
In this article, we’ll introduce you to logistics automation and how it can benefit your business. We will also delve into how Nanonets can help you automate various tasks in your logistics processes.
Logistics Automation Overview
Logistics automation refers to the use of technology in the logistics process. These tasks may include data entry, document processing, shipping label recognition, inventory management, transportation management, warehousing, shipment tracking and tracing, customs clearance, payment processing, and more. The goal of logistics automation is to improve efficiency and accuracy in logistics operations, enable data-driven decision making, reduce costs and errors, and improve customer satisfaction.
There are a number of different technologies that can help companies automate various tasks in the logistics process. These may include:
- Robotic Process Automation (RPA): RPA is a type of software that can be programmed to perform tasks such as data entry, document processing, and other repetitive tasks. RPA can help companies automate these tasks quickly and easily, without the need for complex programming.
- Artificial intelligence (AI) and machine learning – AI and machine learning can be used to automate tasks like demand forecasting and inventory management. These technologies can analyze data and make predictions or recommendations that can help companies optimize their logistics processes.
- Optical Character Recognition (OCR): OCR is a technology that uses machine learning algorithms to extract data from scanned documents and images. OCR can be used to automate tasks like data entry, document processing, shipping label recognition, and more.
Automate manual data entry using Nanonet’s AI-based IDP software. Capture document data instantly and automate data workflows. Reduce delivery times and eliminate manual effort.
Benefits of Logistics Automation
according to a study By McKinsey & Company, AI-enabled supply chain management has enabled early adopters to improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%, in compared to slower moving competitors.
Broadly speaking, the benefits of logistics automation include:
- improved efficiency: Automating tasks like data entry and document processing can significantly reduce the time, effort, and manual errors that tend to be routine in these tasks. This can free up resources that help companies focus on other important tasks.
- improved accuracy: Automating tasks can help minimize the risk of errors, such as misinterpretation of information or transposing numbers. This can improve the accuracy of the logistics process and reduce the risk of losses, delays, and other problems.
- Reduced costs: Task automation can help reduce labor costs and other expenses associated with manual processes. This can lead to cost savings for companies.
- Increased customer satisfaction: By streamlining the logistics process, companies can improve delivery times and other aspects of customer service. This can lead to greater customer satisfaction and loyalty.
OCR and Nanonets for Logistics Automation
Nanonets is a machine learning based OCR platform that can help companies automate various tasks in the logistics process. It offers an API for integrating with logistics systems, as well as an easy-to-use interface for training and deploying machine learning models.
Some specific use cases for Nanonets in logistics automation include:
- Extraction of data from invoices and purchase orders: Nanogrids can be used to automate the process of extracting data from invoices and purchase orders, such as item descriptions and quantities. This can help businesses accurately track their inventory and spending.
- Shipping Label Recognition Automation: Nanogrids can be used to automate the process of extracting data from shipping labels, such as tracking numbers and recipient information. This can help streamline the submission process and reduce the risk of errors.
- Classification and routing of incoming documents: Nanogrids can be used to classify and route incoming documents, such as invoices and purchase orders, based on predetermined criteria. This can help businesses process and organize these documents efficiently.
Carry
Technology has brought many innovations to the logistics industry, and adopting them has become the cost of doing business today. While there are several logistics automation systems to invest in, the easiest and cheapest way to start is to automate the data entry process. This in itself can help save time and reduce errors, improving customer satisfaction.
With Nanonets, you can seamlessly extract and manipulate data from documents.
