Automate Monthly Order Reports with Chengwu Low-code Platform and DeepSeek

Background and Goal

An operations employee must collect and consolidate monthly order data from branch companies, including order volume, total amount, return volume, return amount, and top return reason. Manual collection from disparate systems is time-consuming. The Chengwu low-code platform combined with DeepSeek enables automated data collection, processing, analysis, and report generation without developer assistance.

Data Collection: Creating Data Tables and Forms

2.1 Data Table Design

Two tables are created in the Chengwu platform:

Orders Table ( orders )

CREATE TABLE `orders` (
  `order_id` VARCHAR(100) COMMENT 'Order ID',
  `company_name` VARCHAR(100) COMMENT 'Branch Company Name',
  `order_amount` DOUBLE COMMENT 'Order Amount',
  `order_date` DATE COMMENT 'Order Date',
  `status` VARCHAR(50) COMMENT 'Order Status (Completed, Returned)'
);

Returns Table ( returns )

CREATE TABLE `returns` (
  `order_id` VARCHAR(100) COMMENT 'Order ID',
  `return_amount` DOUBLE COMMENT 'Return Amount',
  `return_reason` VARCHAR(100) COMMENT 'Return Reason',
  `return_date` DATE COMMENT 'Return Date'
);

2.2 Form Design and Data Collection

Using the platform's form designer, two forms are created to match the tables:

New Order Form fields: Order ID, Branch Company Name, Order Amount, Order Date, Order Status.

New Return Form fields: Order ID, Return Amount, Return Reason, Return Date.

Submitting these forms saves data directly to the database in real time.

Data Analysis and Report Generation

3.1 Using DeepSeek for Data Analysis

3.1.1 Analysis Metrics

DeepSeek analyzes the data to produce:

Monthly order volume per branch.

Monthly total order amount per branch.

Monthly return volume per branch.

Monthly return amount per branch.

Top return reason per branch per month.

3.1.2 Generating Analysis Report

The user prompts DeepSeek with:

Analyze each branch's order data for the past month, calculating monthly order volume, total order amount, return volume, return amount, and most common return reason. Present as a table and flag branches with abnormal fluctuations.

DeepSeek queries the tables and returns a result table (example):

Branch | Monthly Orders | Total Amount | Returns | Return Amount | Top Return Reason
A公司  | 120            | 35000        | 15      | 3000          | Product Quality Issue
B公司  | 80             | 24000        | 10      | 2000          | Shipping Damage
C公司  | 95             | 28000        | 8       | 1600          | Customer Rejection

The table highlights issues such as Company A's high return volume due to product quality, signaling a need for supply chain review.

3.1.3 Anomaly Detection

DeepSeek also identifies anomalies, e.g.:

Company A's order volume dropped 20% compared to last month. Check for inventory issues or market changes.

3.2 Auto-generating Report Narrative

Based on the analysis, DeepSeek produces a ready-to-use report:

This month Company A processed 120 orders totaling 35,000 yuan. Returns: 15 orders, 3,000 yuan. Top reason: product quality issue. Recommend quality improvement or market research.

Company B and C show normal order and return volumes, but Company B's shipping damage rate increased, suggesting stronger logistics quality control.

The narrative is concise and requires minimal editing before submission.

Filtering and Data Visualization

4.1 Multi-dimensional Filtering

The platform supports filtering by:

Time range: view orders within a date range.

Branch company: isolate a single branch's data.

Return reason: examine all returns for a specific cause.

These filters enable flexible scenario analysis.

4.2 Data Visualization

Built-in charts include:

Order Volume Trend Chart : shows three-month order volume trends per branch.

Return Reason Pie Chart : displays proportion of return reasons across branches.

Visualizations help leadership grasp operational status quickly.

Value of Low-code Platforms in Clerical Work

5.1 Rapid Data Collection and Automation

Clerical workers can create forms, manage data, and configure automation tasks without coding, eliminating manual entry and aggregation.

5.2 Efficient Analysis and Intelligent Reporting

DeepSeek extracts insights from large datasets, automating analysis and report generation so staff can produce real-time, adjustable reports and improve decision speed.

5.3 Improved Report Quality and Decision Support

Auto-generated narratives and analyses ensure objectivity and accuracy, enabling data-driven decisions without reliance on other teams or external tools.

Summary

The combination of Chengwu low-code platform and DeepSeek significantly boosts efficiency for clerical staff in data collection, statistics, analysis, and report writing. No code is required to automate the full pipeline, reducing repetitive labor and enhancing decision-making. As platform capabilities expand, clerical workers will further leverage low-code tools to drive organizational digital transformation.