Dynamic Pricing is a strategy that adjusts prices flexibly in real-time by analyzing various variables such as demand and supply, competition, time, and customer purchase patterns. Utilizing AI and big data, this approach enables companies to maximize revenue while offering personalized services to customers. Dynamic Pricing in Retail: The Case of TEMU TEMU, a platform known for leveraging AI instead of traditional merchandise planners (MDs), has adopted dynamic pricing tailored to individual consumers. By analyzing data such as buyers’ locations, past purchase histories, search records, and time of purchase, TEMU offers different prices and discount rates for the same product, depending on the customer. Additionally, prices may be raised or further discounts provided based on specific time periods or conditions. This approach not only delivers a personalized experience for customers but also helps companies optimize inventory management and revenue. Dynamic Pricing in Logistics Local Delivery In the local delivery sector, dynamic pricing has yet to be implemented. The industry operates on a fixed fee structure comprising intermediary usage fees, payment processing fees, and delivery charges. Intermediary usage fees are typically calculated as a percentage of the order amount, while payment processing fees vary slightly depending on the payment method. Delivery charges, on the other hand, are adjusted based on distance, location, and time of day. However, these adjustments follow pre-determined rates set by each platform and do not reflect real-time demand and supply, distinguishing them from true dynamic pricing models. While this structure provides operational stability, it limits the ability to respond swiftly to changing market conditions. Parcel Delivery In the parcel delivery market, the fee structure operates through a system where the head office connects with agencies or subcontractors before finally reaching delivery drivers. Typically, a flat fee is paid per parcel, determined through negotiations based on factors such as shipment volume, distance, and delivery convenience. However, this structure is not aligned with dynamic pricing principles. Although fees may slightly vary by agency or region, weight, distance, and volume are not systematically segmented. Discussions on community forums for delivery drivers reveal complaints about receiving the same fee regardless of whether they are delivering lightweight accessories or bulky items like toilet paper. This has raised concerns about fairness and efficiency in earnings, highlighting areas for improvement. Middle-Mile Logistics In the middle-mile segment, often regarded as the backbone of logistics, dynamic pricing has not yet been implemented. Rates are primarily determined through fixed pricing structures or pre-negotiated agreements. Various factors influence pricing in this segment, such as vehicle type (e.g., load capacity, refrigerated/frozen requirements), cargo characteristics (e.g., fragile goods, food), loading and unloading conditions (e.g., urgent delivery, round-trip requirements), regional specifics (e.g., urban vs. rural distances), transport routes and times (e.g., direct distance vs. actual travel time), and timing (e.g., weekends, holidays). However, these variables are not automated or dynamically reflected in real-time pricing models. Although some platform-based freight forwarding companies are exploring dynamic pricing, the results so far have been limited. Maritime Transport The situation is largely the same in maritime transport, where most shipping companies still adhere to traditional fixed-rate systems. However, global logistics giant Maersk is reportedly leveraging a web-based quotation system that can be considered a form of dynamic pricing. This system minimizes human intervention by automatically calculating rates based on real-time market conditions, offering a swift response to fluctuations in demand and supply. Additionally, many shipping companies are transitioning to systems where freight rates and schedules are openly available on their websites, signaling a gradual shift toward more flexible pricing mechanisms in the maritime sector. We Need to Generate Logistics Data If dynamic pricing is introduced into the logistics industry, it is likely to consider the structural characteristics of the sector. The adoption of sensor and AI-based technologies will play a crucial role by enabling the real-time collection and analysis of logistics data, leading to more precise freight rate calculations. For instance, sensors attached to the uniforms of delivery personnel could automatically measure the volume and weight of cargo during delivery, providing a foundational dataset for accurate rate determination. These sensors can collect data on basic variables such as delivery distance and frequency, as well as additional factors like seasonal trends, holidays, and late-night or early-morning deliveries. Such information could build a flexible and detailed pricing system. Volume and weight data, in particular, represent key elements that quantify the actual space occupied and transportation load of cargo. By linking this data with delivery distances, freight rates proportional to cargo characteristics can be established. Incorporating Additional Variables Seasonal factors and time-based surcharges can also be refined. During peak seasons, higher freight rates could be applied, while discounts might be offered during off-peak periods to balance supply and demand. Holidays or late-night and early-morning hours could involve additional compensation for scarcity, and adverse weather conditions like snowstorms or heavy rain could justify special surcharges reflecting the increased complexity of transportation. Sensors for Health and Safety Sensor technology also has the potential to improve the health and safety of delivery drivers. By monitoring real-time health data such as heart rate, body temperature, and stress levels, early signs of overwork can be detected and addressed. For example, if prolonged driving raises concerns about fatigue, the system could recommend workload adjustments or urgent breaks, contributing to driver safety and preventing major accidents. Enhancing Transparency and Fairness Such sensor-based data can enhance both transparency and fairness in logistics services. By clearly presenting the basis for freight rate calculations, trust can be strengthened between shippers and drivers. Additionally, it could improve drivers’ working conditions and maximize logistics efficiency. Ultimately, this approach represents a paradigm shift, combining dynamic pricing with data-driven management across the logistics industry. Middle-Mile Logistics and Dynamic Pricing In the middle-mile segment, data-driven pricing models are gradually expanding. For example, in November 2022, Tmap Mobility and Lotte Global Logistics announced a partnership to introduce dynamic pricing and AI-based dispatch services for middle-mile transportation. This collaboration aims to provide faster and more cost-effective transportation services. While concrete progress remains to be seen, successful outcomes from data-driven dynamic pricing strategies could accelerate adoption in this segment. In maritime transport, leading players like Maersk have influenced the industry by adopting digital innovations. Maersk’s web-based quotation system, which minimizes human intervention and calculates rates based on market dynamics, exemplifies a shift toward dynamic pricing. As more shipping companies adopt transparent freight rate disclosures and flexible pricing systems, the maritime industry is likely to see broader adoption of dynamic pricing models. These changes are expected to enhance efficiency and competitiveness while providing shippers with reliable, transparent freight rates. Dynamic Pricing for ESG and Sustainability Dynamic pricing based on data not only strengthens trust between shippers and carriers but also provides a tool to respond to regulatory pressures. For example, integrating variables such as fuel price fluctuations, currency exchange rates, geopolitical risks, and weather conditions can ensure freight rates reflect real-time market conditions. Moreover, incorporating ESG factors like carbon emissions into freight calculations can promote corporate sustainability and social responsibility. Real-time tracking of carbon emissions during transportation, coupled with transparent disclosures, can reinforce environmental accountability. This approach can help logistics companies stay ahead of environmental regulations while bolstering the transparency and adaptability of global supply chains. ESG-integrated freight systems also provide a foundation for companies to address sustainability demands while contributing to the industry’s long-term resilience. Future Prospects Dynamic pricing is already widely used in the passenger aviation sector and is likely to expand its influence into cargo logistics. Similarly, rail freight, traditionally bound by rigid pricing systems, could evolve to include dynamic models as multimodal transport gains traction, such as recent successful trials connecting shipping and rail to Central Asia. Dynamic pricing in logistics is poised to move beyond reliance on indices like the Shanghai Containerized Freight Index (SCFI) or Baltic Dry Index (BDI). By incorporating real-time data, freight rates can be adjusted more precisely to reflect cargo characteristics and dynamic conditions. This transition is expected to amplify rate variability, making data-driven decisions a key differentiator and competitive advantage for logistics companies. Optimizing Logistics Space Utilization Dynamic pricing is not limited to transportation but extends to optimizing warehouse operations. Flexible pricing policies that reflect real-time vacancy rates based on time, season, or product categories can maximize both operational efficiency and profitability for logistics centers. Ultimately, dynamic pricing will drive transformative change across the logistics industry, reshaping transportation and storage systems alike. #Logistics #DynamicPricing #AI #BigData #FreightRates #SupplyChain #Innovation #LogisticsTechnology #RealTimeData #SmartLogistics