As AI takes over data aggregation and integrates into business processes, operational signals have the potential to shift from "post-event summarization" to "preemptive alerts."
Fluctuating sea freight rates each week affect not only sales quotations and booking decisions. Lingong Heavy Machinery has leveraged Feishu's multi-dimensional tables and AI to transform freight rate reporting—previously manually submitted by local agents across more than 50 countries—into an automated system that updates every Friday and actively issues warnings when fluctuations exceed 5%.
Each week, Lingong Heavy Machinery’s international marketing team must reconfirm ocean freight prices.
Sea freight costs are more than just a pricing list for the logistics department—they directly impact sales quotes, customer responses, and booking decisions. The challenge lies in the accelerating pace of market changes, while information remains scattered across different agents and shipping routes, making it difficult for teams to grasp the full picture in a timely manner.
Lingong Heavy Machinery uses Feishu multi-dimensional tables combined with AI to transform freight rate management across more than 50 countries and regions—from repetitive weekly manual work into an automated, self-updating system that proactively issues alerts.
"Let AI handle repetitive tasks and let people focus on creation—every tool upgrade represents a release of productivity," said Shang Peng,院长 of Lingong Heavy Machinery's Digital Research Institute.
Cargo rates held separately by different agents
Trapped in 'People Searching for Data'
Lingong Heavy Machinery is an engineering machinery manufacturer registered in Shandong Province. Its mining trucks have led global sales for ten consecutive years, its aerial work platforms rank consistently among the top ten worldwide, and it has been listed for many years among the world’s top 50 construction machinery manufacturers. Currently, it has established 20 overseas subsidiaries in Europe, the United States, Japan, Australia, South Korea, Hong Kong, China, and other locations, supported by 16 overseas warehousing centers and over 400 global service stations, with a business network spanning more than 100 countries and regions.
Global business expansion means that every overseas quotation and shipment must contend with a constantly changing cost variable: sea freight charges.
Lingong Heavy Machinery's products
However, sea freight rates are not a fixed, universally applicable price list. Behind them lie more than 50 countries and regions, each with different ports, shipping routes, and agents; destinations vary, so do the calculation bases for transportation costs, and prices continue to fluctuate with the market. What the international marketing team truly needs to manage is not 50 static numbers, but a globally evolving network of freight rates that changes every week.
In the past, during periodic rate updates, the logistics team had to manually collect data from agents around the world. Some sent spreadsheets, others used their own formats. After gathering all the data, staff still needed to manually transcribe, organize, calculate averages, plot trends, and then deliver the results to sales. Any missing or incorrect entry could mislead subsequent decisions; by the time sales received the information, market conditions often had already changed one or two days prior.
This matters because sea freight directly affects how overseas sales set prices and decide when to book shipments. Overestimating freight costs may result in uncompetitive quotes; underestimating them may squeeze profit margins; delayed information delivery makes both sales and logistics more reactive during the booking process. Therefore, what Lingong Heavy Machinery aims to solve goes beyond merely “reducing the effort of creating a spreadsheet”—it seeks to convert fragmented, globally dispersed price changes into actionable business intelligence that sales can use promptly and accurately.
First, Make Data Speak the Same Language
Only Then Can AI Begin Monitoring Fluctuations
The first step was not to have AI predict the market, but to standardize data. The team designed a unified template within Feishu's multi-dimensional tables, assigning input permissions to port agents to collect route and freight rate data weekly. Information previously scattered among individuals was now consolidated into a single table.
Next, the multi-dimensional table’s AI workflow took over repetitive tasks: every Friday, the system automatically aggregated data entered by agents, calculated averages, wrote them into summary tables, and updated freight trend charts for regions including Europe, the Middle East, the Americas, Russia, and Asia-Pacific—eliminating the need for teams to spend half a day on manual compilation.
The real turning point occurred at the final stage of information delivery.
The team set a 5% absolute change in freight rates as the alert threshold. Once a route crossed this line, the system would identify the anomaly, automatically generate trend analysis, and push Feishu messages to sales and relevant managers by region.
Screenshot of the backend management interface for alert notifications
What AI took over was not human judgment, but the most time-consuming and error-prone stages—data collection and organization. Humans could then redirect their efforts toward pricing, booking, and customer response—areas that truly require business experience.
Beyond Saving Half a Day, Freight Rates Have Become
Proactive Operational Signals
Data aggregation shifted from a half-day manual task to automatic completion, saving 0.5 person-days per week; rate updates moved from being delayed by one or two days to timely refreshes every Friday; market fluctuations evolved from being discovered after the fact to triggering automatic alerts when exceeding 5%.
Beyond these numbers, a more significant change is that the rhythm of operations has advanced. Sales teams no longer need to wait for logistics personnel to forward updates individually—they can now promptly view freight rate changes in their respective regions. Logistics teams no longer need to repeatedly compile the same data, freeing up more time for anomaly analysis and collaborative support. Thus, freight rate management has transitioned from reliance on experience and intuition to a rule-based, quantified operational signal system.
Regularly generated market analysis reports
By the first half of 2024, this "AI-powered sea freight rate analysis and early warning system" had been adopted daily by 50 members of Lingong Heavy Machinery’s overseas marketing team and had entered a state of stable operation. With standardized data structures, fixed update frequencies, clear alert thresholds, and automated delivery pathways, this approach also lays the foundation for replication in other logistics scenarios such as air freight and rail transport.
For other manufacturing companies expanding into global markets, this case highlights a key insight: as AI assumes responsibility for data aggregation and becomes embedded in business workflows, operational signals can shift from "post-event summarization" to "preemptive alerts."
When anomalies no longer wait to be discovered by people, and information no longer passes through multiple layers of relay, what Lingong Heavy Machinery saves is not just half a day of compilation time—it’s the waiting time before every business decision.