Automation in Logistics: 3 Ways To Automate Manual Process

What does the future hold for automation in logistics? 

The logistics industry represents around 12% of the entire world’s GDP. Though, 32% of logistics companies are still relying on manual steps for more than 50% of their manual processes. Mid-sized businesses are particularly struggling to cope with rising costs.

This guide will teach you THREE ways logistic providers are automating manual processes using automation in logistics. 

1. Intelligent Character Recognition for Paperless Invoices
2. AI for Shipment Scheduling and Tracking
3. Predictive Analytics in Supply & Demand Planning

1. Intelligent Character Recognition for Paperless Invoices

Every logistics company handles tons of documents on a daily basis. These documents can include contract agreements, freight bills, bills of lading, CMR transport documents, etc. Some of these are physical documents and many are sent by email. Collating and converting all these formats to a digital footprint can save logistic companies a lot of time and improve real-time tracking from a single place.

Going paperless is possible through automated invoice data capture .

Deciding on which software suits you will depend on your project volume. Many companies settle for Optical Character Recognition(OMR) software because of the cost factor. However, it works only with scanned images or printed text. ICR software, on the other hand, is set up to recognize handwritten and physical documents as well and converting it into machine print while also providing intelligent context.

Today, data recognition and interpretation technologies are being powered by machine learning. Manual intervention by staff is at a minimum since the algorithms are built to continually improve on itself. In a nutshell, this is how it works –

• Documents stream in from various sources and in different formats.
• The software first identifies the type of document by understanding its context.
• It locates the required data needed for each document format, extracts and stores it in relevant fields in the cloud database.
• It then sends the documents or data to business enterprise systems, globally.
• All this in a fraction of the time that a manual process would need and with no scope for human error.

Intelligent software is trained to emulate how a human thinks through a representative sample set. Once trained, it goes beyond what a human can accomplish by seeing patterns that the human mind will find it difficult to interpret. Machine learning in invoice data capture just improves accuracy as the machine trains itself. This is a time and cost-saving process in the long-run.

2. AI for Shipment Scheduling and Tracking

Artificial Intelligence comes with computing technologies that sift through the massive amount of data produced in SCM. Analyzes of this data can initiate automating the complex processes and build robust reporting tools. Smart Robotics Process Automation (RPA) can even analyze overall spend and monitor carrier performance.

Automated logistics invoice data capture begins with extracting shipment requirements from incoming emails . It logs into multiple carrier portals/systems to check availability. It then initiates a pickup request. When the pick-up time has been accepted by a carrier, it will automate the invoice creation and send the customer an email confirmation for the pickup date and time. It then keeps the customer updated on the progress of the shipment by tracking GPS enabled vehicles.

One of the biggest challenges is getting paid once the job is done. The number of freight bills can run into hundreds each week for even a mid-size shipment company. Often submitting Proof of Delivery (POD) can hold up payments. By freight audit and payment monitoring of all client and vendor accounts, any discrepancies are addressed immediately. This automating of the entire order-to-cash cycle ensures organizations can keep their cash flow running smoothly. It brings a cost-saving of 25 to 50% by speeding up the collection process.

3. Predictive Analytics in Supply & Demand Planning

In the last few years, Robotic Process Automation has come roaring out of high-tech labs and into the mainstream. While AI is designed to mimic human intelligence, Robotic Process Automation are virtual assistants that can automate repeated business processes. Teamed up with AI, they make for very accurate demand forecasting.

Forecasting requires data from multiple sources such as future sales targets, historical sales data, seasonal demand data, price trends, current inventory levels, expected shipments, etc. RPA bots are perfectly suited to collect such multivariate data. Along with AI, it can predict demands and alert procurement teams about it. RPA can circumvent this human involvement by raising purchase orders that can travel up the supply chain. Human intervention is often reduced to a supervisory role.

At the end of the day, technologies like AI and RPA bring tangible benefits to the logistics sector. If you would like to know more about how it can be customized to suit your needs, reach out to me, Jason Dodge (iTech Data Services) at 972 456 9479 or email me at info@itechdata.Ai

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