The importance of artificial intelligence for data-driven logistics management
If there’s one industry governed and overrun by data, it’s logistics! The transportation and distribution of goods have so many data points along the entire route, that supply chain management is practically drowning in data, with much of it still processed manually.
From the moment a package is picked at the warehouse to being in the hands of the customer, everything must be tracked and documented. And the process doesn’t end there – the documentation involved in processing invoices and payments is all part of the workflow before an order delivery can be considered ‘closed’.
Each manual process opens up the potential for inadvertent human error and workflow inefficiencies, all of which can lead to increased costs, potential counterfeiting (APSA cites that counterfeiting represents approx. 3.3% of world trade), and customer dissatisfaction. And with disparate systems used to track, log, and store information, the opportunities to improve accuracy and productivity are limited at best. Interestingly, in a recent Gartner report, it predicts that by 2026, more than 50% of supply chain organizations will use machine learning (ML) to augment their decision-making capability.
Now’s the time to plan for the future and boost logistics management automation!
Leveraging data-driven intelligent automation can help the supply chain optimize resources and realize financial benefits BUT this relies on having all the data available on one platform.
So, no matter what your logistics automation goals are, this is always going to be step one: aggregating all data into a single source location, like a data lake or warehouse, including invoices, scanned parcel labels, destinations and routes, real-time in-transit storage specifications, etc.
Once this crucial first step is in place, only then can data analytics help companies visualize where efficiencies can be made, where costs can be optimized, and what weaknesses can be eliminated. Fleet management software can already track resources in real-time, but with advanced automation and AI in logistics the opportunities for business growth and success are unleashed.
Technology in the Driving Seat
Exploring the opportunities of automation in transportation workflows, SourceFuse recently created a proof of concept (POC) model, demonstrating the ability to automatically aggregate data in a unified platform. The model included building dashboards for data analytics and visualization purposes, revealing where cost savings and processing times could be immediately optimized.
This empowers companies to set business benchmarks to manage customer expectations and increase satisfaction. For example, analyzing journey times, mileage, parcel tracking, warehouse processing, and regional variations can help establish guaranteed delivery times.
The POC model also recommended advanced technologies that would support the introduction of more workflow automation, including:
- AWS Textract – A machine learning tool for automatically extracting and converting printed text, handwriting, and data from any document, e.g. invoices, parcel labels, etc. It eliminates manual workflows by automatically processing and acting on the information extracted, taking minutes rather than hours or even days.
- Amazon Rekognition – Using AI to automatically analyze images or videos to extract information and insights. For example, using existing warehouse camera feeds, vehicle licence plate images can be automatically read for accurate goods in/out times, eliminating the need for any manual logging.
- AWS Quicksight – For automatically visualizing your data through interactive dashboards, converting data into business insights for improved data management and increased efficiencies. Patterns or trends can be understood, and the ability to ask questions in natural language empowers better data-driven decisions.
The Benefits of AI-Powered Logistics Management
Deploying workflow automation, data unification and analytics means business owners can study trends and forecasts, and make more informed decisions. The most common benefits that automation has brought to the transportation industry include:
- Cost optimization: Manual processes have a certain degree of error margin. Automation reduces inaccuracies, which often have a costly knock-on effect in the supply chain.
- Increased productivity: Having access to reporting dashboards enables businesses to monitor courier performance, and make strategic decisions on transportation options.
- Anti-counterfeiting: Automating track-and-trace workflows for goods documentation helps to curb anti-counterfeiting in logistics and plug loopholes in the system.
- Improved efficiencies: The ability to study trends and use predictive analysis for forecasting.
- Scalability: When deployed properly, logistics automation enables companies to scale and grow their businesses at a pace that meets today’s demands, enhancing customer service and loyalty, and keeping ahead of the competition.
See how SourceFuse has supported companies to realize these benefits, deploying automation technology to improve productivity and efficiencies:
- UrbanSDK – Modernizing Transportation: Using visual analytics to modernize transportation planning in real-time
- Automatic text analysis: Turning medical prescription data into business intelligence for a leading healthcare enterprise
- Invoice processing: Implementing machine-learning to deliver business intelligence and insights, improve efficiencies, and empower data-driven decisions, with Amazon QuickSight
