Customer background

Yang Guofu Malatang was founded in 2003, starting from the street corner of Harbin's "Yang's Malatang." After 22 years of development, it has become a benchmark enterprise for Chinese fast-food chain brands. As of 2024, Yang Guofu has nearly 7,000 stores worldwide, spread across 31 provinces and cities nationwide and more than 20 countries overseas, with annual revenue exceeding 1.4 billion yuan, making it the leading brand in the spicy hot pot category.

With the deepening of digital transformation, Yang Guofu has built a multi-touchpoint digital service system covering mobile apps, WeChat mini-programs, and web platforms, supporting core business systems such as order management, payment processing, inventory management, and delivery scheduling. The distributed deployment of thousands of stores nationwide has led to exponential growth in system architecture complexity, posing unprecedented challenges to the stability of technical architecture and operational efficiency.

The dilemma of few but highly skilled operations staff

With nearly 7,000 stores, the operations team is relatively limited and needs efficient tools to improve average operations efficiency.

Delayed fault detection and excessively long positioning times

They can only respond passively and cannot proactively prevent issues. Often, system anomalies are discovered only after user complaints are made. As the scale and number of stores grow, feedback becomes more delayed and fault locating takes longer.

Multi-platform collaboration is difficult

Data fragmentation across multiple platforms—apps, mini-programs, web ends, POS systems—prevents forming a unified business view, which affects decision-making efficiency.

Solutions

A single platform connects all data

By Guance the global data collector DataKit and the data processing Pipeline engine, metrics, logs, and traces are uniformly collected and processed, and with preset templates, core services and infrastructure can be quickly covered.

Intelligent analysis engine

Root Cause Analysis: Automatically correlates analysis to quickly locate the root cause of problems, supporting fault analysis across regions and time zones

Performance Baseline: Establish a dynamic performance baseline to promptly detect performance degradation, supporting capacity forecasting and resource optimization

Multidimensional correlation: Link technical metrics with business metrics to achieve rapid mapping from technical issues to business impact

Ready-to-use data dashboard

100+ View templates for mainstream middleware, databases, and service frameworks can be used immediately upon integration, greatly reducing workload.

Customer outcomes

Faster fault response

System fault detection can be detected from minute to second, with most issues automatically identified and alerted before they affect users, enabling rapid remote anomaly location and effectively shortening troubleshooting time.

More accurate problem localization

Common faults have been automatically repaired, greatly freeing up the operations team's energy; Handling complex issues is also more efficient thanks to contextual data integration, ensuring stable business operations during peak periods.

Stable business performance

Platform stability steadily improved, supporting concurrent operation across multiple regions and stores, with core business system availability rising from 99.5% to 99.9%, supporting the stable operation of nearly 7,000 stores. Through a unified monitoring platform, business departments can more clearly grasp system health, proactively avoid potential risks, and significantly improve user experience.

Stronger resource efficiency

With intelligent analytics, resource scheduling becomes more rational and cost structure optimized. The scope of operations and maintenance team management has expanded, truly achieving "fewer people, fewer tasks." At the same time, through intelligent capacity forecasting, cloud resource utilization has increased by 25%, reducing IT infrastructure costs.

Yang Guofu Spicy Hot Pot Guance