ALSI has compiled the presentation material and seeks to collaborate with industry peers in exploring development strategies for the transformation and upgrading of manufacturing enterprises.

APS demands exceptionally high data precision. Beyond leveraging data from ERP and MES systems, APS refines data granularity to a more meticulous level, even down to the various parameters of each individual piece of equipment.

The algorithm of APS is grounded in mathematical models, enabling it to meet the more challenging and precise demands of smart factories. This mathematical model-based approach is recognized as one of the most powerful algorithms available.

By implementing an intelligent equipment management system, both component inspection and maintenance scheduling can be dramatically streamlined, reducing staff workload, boosting managerial efficiency, and extending the equipment’s service life.

Customers care not only about the smart product itself, but also the quality of after-sales service, which directly affects their satisfaction and repeat purchase rate.

Data serves as the cornerstone for the digital transformation and upgrading of manufacturing enterprises. However, in the current construction of smart factories, companies commonly face the challenge of dealing with redundant, disorganized, and fragmented equipment data, which hinders production efficiency and prevents the realization of cost reduction and performance enhancement.

Planned Maintenance is one of the pillars of TPM. Its philosophy is: rather than fixing problems after a machine breaks down, it is better to perform maintenance from the outset to prevent breakdowns from occurring.

The term Value Stream Mapping (VSM) originates from the 1999 bestseller *Learning to See* by Mike Rother and John Shook. The authors also coined the terms Value Stream Analysis (VSA) and Value Stream Design (VSD), and established a standardized set of symbols commonly used in value stream mapping.

Equipment failures are generally latent, and their progression adheres to the “normal‑abnormal‑failure” pattern.

Implementing predictive maintenance strategies is essential for building smart factories, as it slows the progression of minor early-stage faults into significant failures, thereby reducing equipment failure rates and optimizing production costs.