In the face of increasingly complex market demand changes and the trend of industrial chain collaboration, the automotive industry has posed unprecedented challenges to production scheduling capabilities. On the one hand, as China's auto market gradually enters the stage of stock competition, the fluctuation of customer orders directly affects the supply and production rhythm of upstream manufacturers. On the other hand, the large-scale deployment of flexible equipment and the continuous expansion of the supply network have made the planning and scheduling work more and more complex, and it is difficult to efficiently coordinate the overall situation and quickly respond to abnormal situations by relying on manual labor.
In the context of the continuous improvement of informatization, more and more car companies have begun to move from the traditional Excel manual scheduling to the digital scheduling decision-making mode with the Advanced Scheduling System (APS) as the core. As a key component of the "digital brain" of the factory, APS has become a key tool to promote intelligent production by integrating operations research, big data and artificial intelligence technology.
Based on our in-depth understanding of the automotive industry, Roland Berger has developed APS solutions for OEMs and component companies, aiming to create a "supply chain control tower" with a high level of agility, transparency and intelligence. The system revolves around a multi-level planning management system from "order to delivery", and empowers enterprises to achieve efficient collaboration from plan formulation to execution through algorithm-driven automation tools. Its core functions include:
1. Reasonable planning: layout in advance and fine preparation of goods
The system can import annual plans or sales forecasts, identify capacity bottlenecks and peak periods, predict the stocking demand of long-term materials and parts based on BOM information, and resolve potential material shortage risks in advance.
2. Reduce in-process: order-driven, full-chain linkage
With customer orders as the core node, the inventory, work-in-process and in-transit information are connected to ensure the linkage and visualization of key data, adjust procurement and production priorities in real time, and ensure delivery timeliness.
3. Reduce inventory: Accurate start-up, on-demand procurement
Combine capacity constraints and order lead times to calculate the optimal work order start time and compress the storage cycle of finished products. At the same time, by reversing the material requirements, over-purchasing is reduced.
4. Stable production: dynamic sequencing and hierarchical scheduling
Automatically arrange the order of production and procurement, and adjust the allocation of resources according to the urgency of the order. Short-term tasks are locked and controlled, and long-term tasks are flexibly adjusted according to the plan.
5. Improve efficiency: open up the system and intelligent computing
Realize seamless connection with ERP and MES systems, and automatically read and write data. With the help of the high-performance computing engine, it supports flexible configuration of parameters and editable computing logic to achieve multi-scenario adaptation.
6. Reduce errors: full coverage, timely notification
All orders are included in the production schedule, and the demand change notices of equipment, personnel, and molds are output in advance, which significantly reduces the execution deviation caused by communication lag.
Figure: Scheduling system solution
On the basis of the above functions, the APS solution further highlights three core advantages:
1. Precise matching of production resources
Combined with the front-line data collected by the Internet of Things, MES and other platforms, the system provides a scientific and reasonable optimal scheduling plan based on advanced operation research optimization algorithms, comprehensively considers multiple variables such as inventory, production capacity, delivery, and constraints, and realizes the maximum benefit of resource allocation.
2. The whole chain of production information is transparent
With orders as the main line, it connects sales, supply, manufacturing, logistics and other links, improves the visibility of orders and the accuracy of delivery predictions, and supports collaborative operation and authority control between multiple roles and factories.
3. Efficient coordination of the planning system
Realize the linkage execution of production, materials and logistics plans, drive multi-party collaborative operations with unified instructions, and strengthen the response and execution of the entire supply system.
With the continuous integration of digital technology and manufacturing management, the APS system is becoming an important entry point for enterprises to achieve digital transformation. It is worth noting that the transformation is not achieved overnight, and enterprises need to start from the overall strategy and systematically promote the construction of informatization and intelligent capabilities in stages and systems. The Roland Berger Automotive team will continue to support industry customers in their digital transformation with their expertise and practical experience to achieve a value leap in their digital upgrade and jointly move towards a more efficient and intelligent future manufacturing ecosystem.
Related:
Insights into Automotive Industry Digital Transformation White Paper Analysis (1)
Insights into Automotive Industry Digital Transformation White Paper Analysis (2)
Insights into Automotive Industry Digital Transformation White Paper Analysis (3)
Insights into Automotive Industry Digital Transformation White Paper Analysis (4)
Insights into Automotive Industry Digital Transformation White Paper Analysis (5)
Insights into Automotive Industry Digital Transformation White Paper Analysis (6)
Insights into Automotive Industry Digital Transformation White Paper Analysis (7)
Insights into Automotive Industry Digital Transformation White Paper Analysis (8)
Insights into Automotive Industry Digital Transformation White Paper Analysis (9)
Insights into Automotive Industry Digital Transformation White Paper Analysis (10)
Insights into Automotive Industry Digital Transformation White Paper Analysis (11)
Insights into Automotive Industry Digital Transformation White Paper Analysis (12)
Insights into Automotive Industry Digital Transformation White Paper Analysis (13)
