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.

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 maintenance and repair of equipment in a smart factory, along with equipment care, rely heavily on the stocking of spare parts.

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.

Conventional equipment data acquisition solutions often suffer from functional limitations and insufficient monitoring comprehensiveness.

Addressing the challenges in spare parts management, the ALSI Smart Maintenance System incorporates a dedicated spare parts management module that enables real-time online inventory management.

Jidoka, a Japanese term coined by Toyota, refers to the ability of machines to make decisions like humans—sometimes called human-triggered automation. In a lean environment, Jidoka must be employed to identify and halt problems from escaping the process (eliminating waste).