Air freight forwarding is a complex and time-sensitive process that involves multiple parties and logistical challenges. One of the biggest challenges in air freight forwarding is optimizing the routing and scheduling of air freight to ensure timely delivery and minimize costs. Traditional methods of routing and scheduling can be time-consuming and prone to errors, leading to inefficiencies and increased costs. However, generative AI has the potential to revolutionize air freight routing and scheduling by automating and optimizing these processes. In this white paper, we will explore how generative AI can help reduce costs and improve efficiency in air freight forwarding, with real-world examples and implementation approaches.
Painpoints
Increasing demands for faster delivery times: According to a survey by the International Air Transport Association (IATA), 62% of air freight shippers cited "time sensitivity" as the main reason for using air freight. This demand for faster delivery times can put pressure on air freight forwarders to optimize routing and scheduling to meet customer expectations.
Complex and time-consuming routing and scheduling processes: Traditional methods of routing and scheduling can be complex and time-consuming, requiring manual analysis of multiple factors such as flight schedules, cargo volume, and destination. According to a study by a prestige research body, air freight forwarders spend up to 60% of their time on operational tasks such as routing and scheduling.
Constantly changing nature of air freight demand and flight schedules: The air freight industry is subject to fluctuations in demand and flight schedules, which can make it difficult to optimize routing and scheduling in real-time. According to a report by DHL Global Forwarding, air freight demand is expected to increase by 3.5% annually between 2020 and 2024, making it essential for air freight forwarders to have agile and adaptable routing and scheduling processes.
Inefficiencies and increased costs: Inefficient routing and scheduling processes can lead to increased costs for air freight forwarders, which can ultimately impact customer satisfaction and competitiveness. Inefficiencies in air freight operations can lead to up to 30% higher costs for shippers.
These pain points highlight the need for more efficient and cost-effective routing and scheduling processes in the air freight industry. Generative AI has emerged as a solution to these challenges by providing automated and optimized routing and scheduling based on machine learning algorithms that can analyze large datasets and predict demand patterns. By using generative AI, air freight forwarders can reduce costs, improve delivery times, and stay competitive in a rapidly evolving industry. 

How generative AI can help? Generative AI can help address the pain points of the air freight industry by automating and optimizing the routing and scheduling process. By using machine learning algorithms to analyze large datasets and predict demand patterns, generative AI can identify the most efficient and cost-effective routes and schedules for air freight. This can lead to reduced costs, improved delivery times, and increased customer satisfaction. Additionally, generative AI can automate the routing and scheduling process, freeing up time for human operators to focus on other aspects of air freight forwarding.
Benefit and use cases
Real-world examples have shown that generative AI can lead to significant cost savings and efficiency improvements in air freight forwarding. For example, a German Global Forwarding has developed an AI-powered platform that uses generative AI to optimize air freight routing and scheduling. The platform analyzes a variety of factors such as flight schedules, cargo volume, and destination to identify the most efficient and cost-effective routes. According to the forwarder, the solution has reduced air freight costs by up to 20% and improved delivery times by up to 40%.
Another example is a German air carrier, which has implemented a generative AI-based platform to optimize air freight routing and scheduling. The platform uses machine learning algorithms to analyze historical data and predict future demand patterns. It then uses this information to optimize routing and scheduling, taking into account factors such as flight schedules, cargo volume, and destination. According to the air carrier, the solution has reduced air freight costs by up to 10% and improved delivery times by up to 20%.

Implementation approach
Data Collection and Preparation: The first step in implementing generative AI in air freight forwarding is to collect and prepare high-quality data for analysis. This data should include information such as flight schedules, cargo volume, weight, size, and destination. The data can be collected from a variety of sources, such as internal systems, external databases, and third-party providers. Once the data has been collected, it must be prepared for analysis. This may involve cleaning the data, removing duplicates, and ensuring that all data is in a consistent format.
Machine Learning Model Development: The next step is to develop a machine learning model that can analyze the data and predict demand patterns. This may involve selecting an appropriate machine learning algorithm, such as linear regression, decision trees, or neural networks, and training the algorithm on historical data. The training process may involve dividing the data into training and validation sets, selecting appropriate features, and tuning hyperparameters to optimize the performance of the model.
AI Platform Development: Once the machine learning model has been developed, it must be integrated into an AI platform that can be used to optimize routing and scheduling. The platform may be developed in-house or using a third-party provider.The platform should be designed to take input data such as flight schedules, cargo volume, and destination, and output an optimized routing and scheduling plan. The platform may also include features such as real-time monitoring and alerts for unexpected events or delays.
Human Oversight and Intervention: While generative AI can automate many aspects of routing and scheduling, human oversight and intervention are still necessary to ensure optimal performance. This may involve monitoring the output of the AI platform, verifying the accuracy of the routing and scheduling plan, and intervening in cases of unexpected delays or cancellations.
Continuous Improvement: Finally, the implementation of generative AI in air freight forwarding should be an iterative process that involves continuous improvement. This may involve collecting feedback from stakeholders, monitoring performance metrics, and updating the machine learning model and AI platform as needed.
Overall, implementing generative AI in air freight forwarding requires expertise in data science, machine learning, and software development. Companies may choose to partner with third-party providers or hire specialized talent to develop and implement this technology. By implementing generative AI in an effective and efficient manner, air freight forwarders can optimize routing and scheduling, reduce costs, and improve customer satisfaction.
How to implement in an economical way Implementing generative AI in air freight forwarding can be costly, requiring significant investments in technology and training. However, there are ways to implement this technology in an economical way. One approach is to partner with a third-party logistics provider that has already developed a generative AI-powered platform. This can reduce the upfront costs of developing and integrating the technology into existing systems and processes. Another approach is to start small and gradually scale up. For example, companies can begin by implementing generative AI in a specific region or for a specific type of cargo, and then gradually expand to other regions or types of cargo as the technology proves its efficacy and return on investment.
Wrap up Generative AI has the potential to revolutionize air freight forwarding by automating and optimizing the routing and scheduling process. By analyzing large datasets and predicting demand patterns, generative AI can identify the most efficient and cost-effective routes and schedules for air freight, leading to reduced costs and improved delivery times. Real-world examples have shown that generative AI can deliver significant benefits in terms of cost savings and efficiency improvements. While implementing generative AI in air freight forwarding requires significant investments in technology and training, partnering with third-party logistics providers or starting small and scaling up can help make it more economical. As the air freight industry continues to evolve and face new challenges, generative AI can be a powerful tool to stay competitive and meet customer demands. 

About KY & Company Full-service digital transformation partner that integrates Strategy, Design, Engineering and Managed-services for Corporates & Government 
 

Mike Kwok Managing Director KY & Company Hong Kong Office
Kelvin Tsang Business Analyst KY & Company Hong Kong Office 

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