September 26, 2024
How to Maximise Margins with Differential Pricing and Client Clustering
Unlock revenue potential with differential pricing. Learn how to optimise profits and boost customer satisfaction with real-world examples.
Blog /
AI Forecast demand
2024-05-13
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minutes reading
2024-07-30
The daily challenges faced by road freight operations departments are numerous, multifaceted and fast-changing. In an environment characterised by the unexpected, the reliability of forecasts can be make-or-break for overall fleet efficiency.
Short-term resource management is a critical but often neglected component of fleet operations. Operations managers have to oversee the dynamic allocation and reallocation of fleet resources to meet immediate demands, make agile and effective decisions to respond to last-minute changes and manage any disruptions in the logistics chain.
Integrating machine learning (ML) and artificial intelligence (AI) into short-term demand forecasts can significantly enhance a logistics service provider’s (LSP) ability to adapt to dynamic conditions. These technological advancements boost fleet productivity and efficiency via various practical applications.
Here, we’ll discuss some real-life use cases of ML and AI-powered short-term demand forecasting, demonstrating their tangible impact on fleet efficiency.
Fleet efficiency isn’t a two-dimensional concept but a combination of several critical components that collectively enhance the performance and cost-effectiveness of transport operations. Before jumping into the examples, we’ll quickly outline the specific metrics that drive overall fleet efficiency:
The image below shows insights from Ontruck’s AI/ML tool, followed by practical examples of what can be done with such insights. This demonstrates how AI and ML solutions can impact the four quantifiable factors mentioned above, generating accurate short-term forecasts and driving fleet efficiency.
Real-time monitoring systems that integrate forecasts enable continuous visibility of forecast, demand and capacity, allowing operations teams to respond to deviations between predicted and actual demand immediately. This capability ensures that service levels are consistently met and operational efficiency is maintained.
Constant access to a real-time forecast is transformative, especially when paired with demand and capacity data. This continuous stream of information ensures ops managers can maintain a clear and current view of their fleet, allowing for more informed and strategic decision-making throughout the day.
For example, in a traditional working model, shipment requests are received in the morning. In the afternoon, the ops team will plan routes and assign shipments to vehicles. It’s at that moment that the team might notice there are too many or too few vehicles. This leads to one of two scenarios:
Traditionally, the road freight industry is very reactive: solutions are sought when it's too late. ML-enabled forecast data provides insights into deviations from previous days’ shipment volumes by generating forecasts and assessing capacity. If forecasted volumes exceed capacity, external fleet assistance can be sought proactively. Conversely, if forecasted volumes are below capacity, external carriers can be informed of reduced need, thus saving costs.
Forecast data allows ops teams to see where they stand and which customers are increasing their shipment demand. Armed with this intelligence, the team can formulate a proactive action plan. This approach enhances customer service levels while optimising operational expenses.
Deploying ML and AI tools for real-time fleet management allows for the automation of complex decision-making processes. These tools can analyse vast amounts of data, enabling logistics operators to make informed decisions quickly, thus improving daily operational management and supporting agile responses to unexpected changes.
Algorithms can analyse large amounts of data regarding shipments, locations, and cargo characteristics. With this information, they can forecast the types of vehicles needed each day, tailored to specific cities, route types and various freight operations (like full truckload, less than truckload, international, domestic, and last mile).
This forecasting helps identify changes in vehicle requirements and assesses the number of assigned shipments and potential shortages. Simultaneously, it demonstrates how the market is evolving and the net revenue achieved. It enables real-time monitoring of profit margins based on vehicles and routes, facilitating timely actions to maximise profitability.
Integrating AI and ML forecast results with existing management systems can greatly increase fleet efficiency. This integration facilitates more seamless operations, reduces the need for manual interventions, and enhances overall operational responsiveness.
For example, we can automatically input the anticipated number of vehicles into a given system. Specifically, our forecast module can seamlessly integrate with our routing tool, enabling the pre-booking functionality. Within our operations platform, the pre-booking tab presents recommendations for reserving slots, guided by the forecast and driver availability.
To provide a straightforward illustration, picture a forecast indicating a requirement for 10 trailers; consequently, the system triggers 10 pre-bookings automatically. The team can then assess and accept these recommendations, guaranteeing ample capacity to manage demand efficiently. This functionality empowers fleet managers to swiftly respond to forecast analysis, thereby ensuring streamlined operations.
The strategic implementation of ML and AI forecasting methods represents a transformative leap forward in fleet management. AI and ML forecasting can significantly improve fleet efficiency by enabling more accurate and timely decision-making in both short-term resource management, as we’ve seen here, and medium-to-long-term capacity planning.
In this article, we've explored practical approaches to achieving greater fleet efficiency based on firsthand experience. It's important to highlight that our solutions have been developed through real-world challenges encountered within our freight business division, Ontruck Transport Services.
Strategies and techniques that harness the power of AI and ML in short-term fleet management not only streamline operations but also enable you to navigate unexpected challenges adeptly. Schedule your complimentary demo today and discover how our cutting-edge solutions can optimise your fleet management process.
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