Declining order intake, labor shortages and unstable supply chains are just the latest manifestations of a challenging and constantly changing business environment that many industrial companies have to deal with. These conditions mean enormous complexity for production and logistics, which is further increased by volatile customer requirements and broadening product portfolios. But how can manufacturing companies remain profitable in this environment?

In the past, the classic value stream method with value stream mapping and design has become the gold standard for optimizing value creation. In light of the challenges mentioned above, however, this method is reaching its limits and can no longer be used to achieve an overall optimum while maintaining a reasonable level of effort.

In contrast to the classic approach, the data-driven value stream method looks at the entire product portfolio and production data from a longer period of time in order to develop an exact and comprehensive model of all value streams. This model is then used to develop scenarios for the elimination of plants and lines, the restoration of profitability in production and the optimization of shift models. In addition, the method can be used to determine which systems or lines should be optimized as a priority and what potential is available, for example, in the optimization of set-up times, intermediate stock levels or throughput times.

Cost potential uncovered with the data-driven value stream method

Elimination of systems or lines from the value creation process
Restoring production stability in the face of volatile customer requirements
Adaptation of shift models in the production lines
Focus of optimizations on the most relevant systems
Reduction of setup losses

In our paper, we have shown you the possibilities for increasing profitability using the data-driven value stream method. Click here to download: