When a traditional factory transitions to a smart factory, introducing new automation equipment to replace manual labor, two key questions must be considered to determine whether the machine replacement is successful:

First, it is necessary to assess whether automation improves efficiency. This can be measured by evaluating whether the overall equipment effectiveness (OEE) has increased and whether fault repairs can be resolved quickly.

Second, it must be determined whether automation reduces costs. This can be measured by whether the maintenance costs and energy consumption costs of the equipment have decreased.

Only when both indicators show a positive trend can we consider the intelligent and digital transformation successful. To understand these metrics, data on equipment operating status, unplanned downtime, and more must be collected, analyzed, and summarized.

This article distills insights and organizes perspectives from a speech by Dr. Zhang Xiaowei, Director of ALSI, titled "Key Influencing Factors and Technologies for Implementing Smart Manufacturing in Existing Factories", which also reflects ALSI’s value proposition in lean improvement and smart manufacturing.

ALSI believes that the Andon system is a highly suitable solution for addressing data collection and information collaboration. The system consists of the following four components:

• T1: Identify on-site anomalies
• T2: Transmit on-site anomalies effectively
• T3: Relevant personnel arrive on-site promptly
• T4: On-site personnel handle the anomaly

The total time from T1 to T4 is the entire business closed-loop duration. The shorter this time, the better the information collaboration.

The Andon system uses the IoT for seamless on-site data collection, easily capturing five types of data:

• Which type of anomaly occurs most frequently?
• Which production lines or workstations do these anomalies occur on?
• Which anomalies occur with high frequency or long duration?
• What does the heatmap of anomaly handling locations and production line locations look like?
• What is the anomaly handling time?

This real-time data transforms the equipment status in a smart factory from a black box to a transparent state. Through data analysis and statistics, it also provides strong support and basis for decision-making.

Case Study:

ALSI once helped a machinery manufacturing plant in Zhejiang Province deploy a smart Andon system and agile buttons on dumb equipment to capture real-time equipment status data. This factory, with over 300 employees, faced the following challenges:

• First, due to some older equipment that could not be networked, it was impossible to directly obtain equipment operating parameters, OEE data, or fault history. This prevented managers from promptly knowing the workshop equipment status and making informed decisions.

Second, the workshop mainly relied on manual operations, with situations such as calling for repairs, material shortages, and equipment abnormalities handled via phone calls, WeChat, walking, or even verbal communication, leading to long waiting times.

To address the factory’s needs, ALSI piloted an Andon system on the first floor of the workshop:

• Enabled smart networking for 50 dumb equipment units
• Customized button functions for call management, mold changes, etc., allowing operators to accurately record equipment abnormalities using agile buttons, with information synchronized via alarm lights
• Deployed visual dashboards in the workshop for real-time equipment status awareness
• Allowed managers to monitor workshop production via a web link on communication tools

These measures significantly improved the factory’s smart manufacturing level, helping save production costs and reduce downtime.

If you would like to obtain a free copy of Dr. Zhang Xiaowei’s document "Key Influencing Factors and Technologies for Implementing Smart Manufacturing in Existing Factories", please follow the public account [ALSI Intelligent Manufacturing] and reply with "Digital Factory Materials" in the backend. We will share the materials with you.