Small and medium-sized enterprises (SMEs) in the manufacturing sector often face limited production capacity, resulting in sluggish response times and inadequate emergency preparedness during crises. Consequently, the development of smart factories is essential for these businesses.

The APS (Advanced Planning and Scheduling) system can accurately capture the entire plant's end-to-end data through a robust data foundation and sophisticated backend algorithms. It enables precise production scheduling based on orders and actual production conditions, thereby enhancing the controllability of the manufacturing process in a smart factory.

Developing a successful standardized platform demands rigorous software engineering capabilities from IT enterprises. Compared to traditional APS development and design, the APS scheduling system engineered by ALSI delivers superior performance in computational power, storage capacity, and overall system efficiency.

The dynamic scheduling capability of the APS planning and scheduling system enables real-time, precise capture of shop-floor changes and delivers accurate production directives. Configurable to adapt flexibly to actual workshop conditions, it facilitates dynamic production planning and scheduling for smart factories.

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.