Current supply monitoring faces challenges that typically lead to reactive bottleneck management. These challenges include:
- Manual range determination: manually calculating ranges for transportation assets and locations leads to time delays and potential errors, resulting in late detection of bottlenecks.
- Manual adjustment of system parameters: Manual adjustment of system parameters to control the supply chain requires complex expertise. Due to the shortage of skilled workers, these adjustments cannot always be made optimally, leading to suboptimal results.
- Insufficient consideration of organizational and network risks: Potential risks along the supply chain are not sufficiently identified and assessed, which can lead to unforeseen bottlenecks.
- Lack of direct interaction between system parameters and material call-offs: An inefficient link between system parameters and material call-offs makes optimal resource utilization difficult and leads to bottlenecks or unprofitable inventory levels.
- Skilled labor shortage: The lack of skilled labor for complex manual setting of system parameters and delivery monitoring makes effective bottleneck management difficult.
In summary, these factors mean that delivery monitoring in current practice is often reactive and bottlenecks are only identified and dealt with after they have already occurred.
The project aims to achieve the following objectives:
- Real-time transparency through digital real-time material flow and transport data.
- Automated decision support through a central logistics system.
- Development of a self-learning system that is continuously optimized through experience and data.
These measures are intended to improve delivery monitoring and control, reduce manual effort and create robust supply chains.