APS and Digital Twin

How APS Systems Are Revolutionizing Value Chain Optimization

The term “Digital Twin” refers to the virtual representation of a real object, process, or system. These digital twins are used to simulate, analyze, and optimize the performance of their physical counterparts. Advanced Planning and Scheduling (APS) systems play a central role in making these digital twins a reality.

APS systems are software solutions that use methods like heuristic algorithms to optimize production scheduling along a company’s value chain. They enable manufacturers to simulate and improve their production processes, allocate resources efficiently, meet delivery deadlines, and reduce lead times, inventory, and production costs.

By using master data from the ERP system, an APS system creates a virtual representation of the future production schedule—weeks, months, years ahead—essentially functioning as a digital twin. By simulating various production scenarios, APS systems can identify potential bottlenecks, delays, or production constraints and adjust plans accordingly for optimal results.

By implementing an APS system, manufacturers can reduce the stagnation of semi-finished goods between processes, increase production efficiency, and maximize overall factory performance without increasing resource capacity. Simulating different scenarios allows these systems to predict the impact of changes before they are implemented—be it new shift schedules, supplier changes, technological upgrades, changes in production technology, or investments in new machinery. This capability minimizes the risk of costly mistakes while improving overall performance.

A critical factor for effective scheduling is the ability of an APS system to accurately mirror reality, as the term “twin” implies. This includes:

• Product characteristics
• Process rules and constraints
• Resource capabilities
• Order dispatching rules
• Scheduling logic, both product- and process-specific, as well as general

However, most APS systems on the market fail to meet this foundational requirement. Additionally, there are several other reasons why some APS software might fall short in realizing a digital twin:

• Lack of Data Precision: A digital twin relies on accurate and up-to-date data from sources like ERP systems and MES. Often, the necessary level of detail to replicate real-world process rules and constraints is missing. Most APS systems lack the ability to compensate for these gaps.
• Limited Scope: Traditional APS systems often focus on optimizing production schedules within a specific plant or department. In contrast, a robust digital twin created by a powerful APS system provides a holistic view, enabling process synchronization across the entire value chain.
• Complex Algorithms: Digital twin technology requires sophisticated algorithms to accurately model and simulate manufacturing processes.
• Lack of Expertise: Developing and maintaining an APS/Digital Twin requires expertise in areas such as data science, process modeling, and optimization. Many APS providers lack the necessary resources and know-how to deliver this level of sophistication.

The successful implementation of an APS/Digital Twin requires collaboration across multiple teams and departments, a deep understanding of manufacturing processes and the supply chain, and a reliable APS consultant.