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. According to the company, 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 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.
What the Case Reports: From Manual Compilation to Scheduled Updates and Alerts
The source article attributes a statement about assigning repetitive work to AI to Shang Peng, described there as head of Lingong Heavy Machinery’s Digital Research Institute. The role, quotation and publication context have not been independently verified.
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. According to the company, its mining trucks and aerial work platforms hold strong global market positions in their categories, and it ranks among the world’s established construction machinery manufacturers. The company states that it has established 20 overseas subsidiaries in locations including Europe, the United States, Japan, Australia, South Korea, Hong Kong, China, supported by 16 overseas warehousing centers and over 400 global service stations, with a business network spanning more than 100 countries and regions—figures buyers can verify against corporate disclosures.
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.
According to the case, during periodic rate updates the logistics team manually collected data from agents around the world. Some sent spreadsheets while others used their own formats. Staff then transcribed and organized the data, calculated averages, plotted trends and delivered the results to sales. The article says missing entries, transcription errors and delays of one or two days were possible; the frequency and delay should be checked against source records.
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 reportedly 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—replacing what the team describes as half a day of manual compilation.
The real turning point occurred at the final stage of information delivery.
According to the case, the team set a 5% absolute change in freight rates as its alert threshold. When 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. Buyers should treat 5% as this team’s configuration, not a recommended default.
Screenshot of the backend management interface for alert notifications
In the case narrative, the workflow supports data collection and organization rather than replacing human judgment. Pricing, booking and customer response remain business decisions. Buyers should verify exception handling, permissions and the review step in a live environment.
Beyond Saving Half a Day, Freight Rates Have Become
Proactive Operational Signals
Data aggregation shifted from a half-day manual task to automatic completion, with the team tracking the time saved against its own pre-automation baseline; rate updates moved from delayed relays to scheduled Friday refreshes; market fluctuations evolved from being discovered after the fact to triggering automatic alerts when exceeding 5%.
In the company’s account, sales teams can view regional freight-rate changes without waiting for individual forwarding, while logistics teams spend less time compiling the same data. These are case-study claims. Whether the workflow improves decision speed should be tested against pre- and post-rollout records for labour time, data errors, alert accuracy and response time.
Regularly generated market analysis reports
The source article says that by the first half of 2024 the workflow was used by 50 members of Lingong Heavy Machinery’s overseas marketing team and was operating on a continuing basis. It does not provide independently verified usage logs or reliability data. The article also suggests that the same structure could be tested for air and rail freight; any reuse would require separate data, threshold and exception design.
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."
The useful test is not whether automation always saves half a day, but whether the live workflow reduces compilation time and information delay without adding missed submissions, false alerts or unreviewed pricing decisions.