In August, the trees are adorned with autumn hues, and the mountains are bathed in the setting sun. Recently, Alps Smart Manufacturing (hereinafter referred to as "Alps") formally signed a cooperation agreement with Liaoning Tongda Axle Co., Ltd. (hereinafter referred to as "Tongda Axle"). Two major systems from the Alps Lean Factory Cloud Platform will be fully integrated into Tongda Axle's production lines, helping the company break through traditional production models, build a real-time, transparent, and informatized production environment, and usher in a new chapter of lean manufacturing.  
    
Those who persevere go far; those who think remain innovative. Entering the era of digital intelligence, while many managers of small and medium-sized factories are still "watching from the sidelines" on the edge of digital transformation, Tongda Axle, a company that has consistently ranked among the top in China's half-shaft production industry for 10 consecutive years, has begun deploying a new round of digital strategies after achieving remarkable performance. After conducting multiple market researches, Tongda Axle ultimately decided to partner with Alps, aiming for positive innovation and reaching new heights through a strategy combining lean manufacturing and digitalization.

Light Implementation of Lean Manufacturing  Building a Transparent Production Environment

With the rapid expansion of Tongda Axle's business and the continuous increase in new products, there is an urgent need to enhance the company's core capabilities in production.  

Precise Control of Production-Related Data  

Due to a high pursuit of quality, Tongda's products must undergo 26 processes, completed by personnel working on single-process operations. As the business gradually expanded, the order volume at Tongda also surged. Facing the current situation of multi-variety and small-batch orders, it was necessary to further improve product quality and production efficiency. Therefore, Tongda carried out automation upgrades for its machining lines. To understand the stability of automated movements and work efficiency, management needed to know various data, including the operating status of each equipment on the production line, downtime duration, energy consumption, and production quantity. However, traditional manual recording methods were not only inefficient but also prone to errors and omissions, failing to provide the necessary data support for leadership.  

Establishing a Rapid Response Mechanism for Anomalies  

In traditional manufacturing, anomalies such as personnel leaving their posts, material shortages, and equipment failures frequently occur during the production process. How to achieve real-time monitoring of production line anomalies and maintain the smooth operation of automated production is often a major challenge. In a noisy working environment, it is difficult for human resources to quickly transmit information, let alone handle issues promptly. How to timely notify relevant personnel to handle anomalies and ensure production proceeds as planned became one of the urgent problems for Tongda Company to solve.  

Relying on profound expertise in both lean manufacturing and digital practices, and after an in-depth investigation of Tongda Axle's specific situation, Alps tailored a "small, fast, light, and accurate" lean digital solution for the company. Addressing Tongda Axle's specific needs and aiming to maximize equipment utilization, Alps rapidly achieved a significant improvement in equipment management levels.  

24-Hour Full-Process "Auscultation"  Accurately Obtaining Equipment Information  

After the launch of the Alps Jinzhi Lean Factory Cloud Platform's Equipment Status Monitoring OEE System, it was as if a 24-hour "stethoscope" was installed on Tongda Axle's equipment. Operators can use the SS series buttons to one-click input the specific reasons for downtime. The system transmits this information to the terminal via the TS series collectors. Maintenance personnel can log in to view equipment status and usage on PC or mobile devices at any time, receive relevant information, and issue subsequent operational instructions. By statistically analyzing the actual utilization rate of equipment, it also provides a basis for deciding whether to add new equipment.  

"At this stage, we have achieved visual analysis of equipment OEE. The downtime for each equipment on a given day and the reasons for the downtime can be checked promptly in the system. This provides data support for our continuous improvement of equipment efficiency in the later stages." - Tongda Axle Equipment Management Personnel  

"One-Second Call" Rapid Response  Ensuring Smooth Production

Without changing the original working mode, the application of the Alps Lean Factory Cloud Platform's Andon System has completely built a rapid response closed-loop system for Tongda Axle, featuring automatic calls for equipment anomalies, manual calls for workstation anomalies, timely notification of responsible persons, and rapid handling of production problems. Whether it is process operations, equipment status, quality issues, or material supply, any problem that arises during operation can trigger a "one-second call," instantly transmitting information. It can also statistically analyze the causes of anomalies for later analysis, providing strong support for the entire production process.  

"Initially, when equipment had problems, we had to shout across the workshop. Now it has changed to WeChat or phone calls. Although reporting problems has become faster, there are still many cases where WeChat messages are not replied to or phone calls are not answered. Equipment might be down for half a day with no one coming to handle it, and there is a lack of data support for post-event accountability. Now, we just need to press a button, and the relevant responsible person will arrive quickly to consult and resolve the issue." - Tongda Axle Production Personnel

While continuously strengthening data collection and improving production efficiency, Tongda Axle has always adhered to its exceptional pursuit of product quality. In the future, both parties will deepen their cooperation to achieve the integration of the QMS system and the OEE system, conducting in-depth statistical analysis of product defect rates and their root causes. They will further formulate targeted strategies to genuinely improve product quality.  

The future is here, and those who ride the trend will win. Digital transformation for enterprises is no longer an optional question but a mandatory one. How to choose the most suitable transformation method for one's own enterprise while embracing technological innovation is a question that traditional enterprises must deeply consider today. Through this cooperation, Alps has assisted Tongda Axle in "running into" the new track of lean manufacturing. In the later stages, both parties will continue to join hands, maintain progress, and be brave in exploration, striving to ride the wind and waves in the era of digital intelligence and create new brilliance.  

About ALSI

Alps System Integration (Dalian) Co., Ltd. (abbreviated as "ALSI") is a wholly-owned subsidiary invested in China in 2005 by the Japanese Alps Alpine Group (founded in 1948). It is the Secretary-General unit of the Dalian Smart Manufacturing Industry Innovation Alliance and a "Specialized, Refined, Differential, and Innovative" enterprise in Liaoning Province. ALSI has been deeply engaged in smart manufacturing for over 30 years in industries such as automotive parts, electronic appliances, and mechanical processing. Relying on the manufacturing DNA of the Japanese group, ALSI delves into the "manufacturing site," combining IoT technology, big data, AI, etc., with lean principles. It has developed the "Hui Pai Supply Chain Resource Planning Platform" and the "Jinzhi Lean Factory Cloud Platform," which are small, fast, light, and accurate. ALSI is committed to helping more customers in the manufacturing industry achieve efficient workshop site management and embark on the path of digital and intelligent development.