AI-powered freight procurement can help your business save time and money. It can automate many time-consuming processes and give you new insights based on incredibly complex data sets.
However, not all tasks are suitable for automation. These include arranging and receiving orders, managing purchase demand, and vendor negotiation.
Real-Time Market Insights
Real-Time Market Insights: When a business uses data to determine what product it should sell, it is crucial to access real-time data. With this kind of insight, companies can ensure they provide their customers with a valuable and relevant experiences.
Personalized Marketing and One-To-One Messaging: With real-time data, marketers can proactively respond to customer actions by offering appropriate incentives and offerings via multiple channels. This helps increase brand engagement and drive sales.
Automated Notifications: As AI processes an ever-increasing amount of data, it can immediately notify teams of anomalies or recommended activities. This means that time is well-spent chasing down errors or mishaps.
Dynamic Cost-Plus: This tool uses AI-predicted prices to help shippers negotiate freight rates and create contracts based on their shipping lanes, volume/ weight requirements and other factors. This way, they can minimize their freight costs and improve profitability.
An AI-powered technology company like Sleek Technologies provides freight procurement software. They offer minor to mid-sized carriers that can easily access large shippers and negotiate more favorable rates. They can also better understand their shipping routes and volumes, helping them build stronger relationships with their clients.
Autonomous Sourcing
Autonomous sourcing software allows procurement teams to break down the complexity of the global business environment. This will enable them to make accurate regional insights that help them make the right sourcing decision.
In addition, it helps them get a clear overview of their spending and handle all operations in correlation with their long-term strategies. This way, they can reduce risks and uncover new sources of value.
Intelligent technologies also give shippers minute-by-minute insights into actual market costs and conditions, helping them avoid wasting money by paying too much for freight. Moreover, they can use this data to check truckload costs and delivery dates.
This cloud-based solution uses intelligent automation technology fueled by AI/ML algorithms to source compliant carriers at true-market cost dynamically. It also eliminates broker fees and spot prices that add hefty hidden margins. Balancing truckload cost with service ensures that shippers remain competitive and keep their transportation budgets under control.
Automated Tendering
With automated freight tendering and intelligent market monitoring, a semi-autonomous solution allows logistics teams to turbocharge their freight procurement and optimization. It constantly scans the market for opportunities to cut freight spend, alerts them to potential savings and then, with a click of a button, runs tenders and recommends the optimal supplier according to their specific needs.
Automated freight tendering helps companies reduce costly inefficiencies like dialing for diesels and manually posting loads on load boards, thereby reducing freight spend and improving profitability. This is especially true if shippers want to expand their carrier networks or use a TMS to confirm tender acceptance with freight brokers and carriers automatically.
Procurement automation helps businesses get more value from their spending by eliminating manual processes and workflow redundancies, shaving cycle times for invoices, purchase orders, contract management and audits, and frees up human resources to focus on more strategic and innovative responsibilities. These savings can add up to significant ROI on day one.
Predictive Analytics
Predictive analytics uses machine learning algorithms and techniques to identify patterns in data. These include decision trees, linear and nonlinear regression, neural networks, support vector machines, and other methods.
Prediction models can help businesses plan for future sales and inventory requirements and can also be used to manage shipping schedules. For example, a company might use predictive analytics to reduce product shrinkage and restock products that are in stock but aren’t selling well.
The data sources for these applications can vary from transactional databases to equipment log files to images and sensors. Companies can even combine data from different sources in a single predictive model.
Freight procurement teams need an accurate view of the capacity market to negotiate more effectively with suppliers and freight brokers. AI tools can give these professionals access to data and insights from both first-party and third-party sources in real time, and they can also build more data-driven long-term strategies.
