Changing markets, technologies, and customer needs increase process complexity. As a result, transparency regarding processes and their underlying structures becomes less clear. The resulting challenge is twofold: First, it is difficult to align processes with the activities that actually contribute to core value creation. Second, it is unclear where in the process the most effective reductions in complexity can be made.
Value stream analysis can restore the transparency that enables managers to shape the process landscape and make the company sustainable. Regaining this transparency then makes it possible to identify bottlenecks and inefficiencies in processes, as well as to lay the foundation for a holistic, data-driven restructuring of the company.
In R&D organizations, the V-Model serves as a suitable framework for mapping value streams. Depending on the specific organization, robust value streams are identified or defined. Afterward, depending on the project’s framework conditions, the value streams are detailed at different levels of granularity. The formulation in the form of work products is particularly suitable as the lowest level of abstraction, as these represent concrete results.
Based on the defined work products, standardized interviews are conducted at the department level to quantify the actual effort invested in creating the work products.
The results of the interviews are presented in the form of various diagrams in a final dashboard. This dashboard serves as the basis for evaluating the data and determining next steps. Anomalies in the data are identified and analyzed in collaboration with experts. Based on these anomalies, areas for action are identified, and possible solutions are described. The evaluation is conducted at different value stream levels, thereby enabling subsequent optimization at both the micro and macro levels.
Value Stream Analysis serves as the basis for further optimization steps. The starting point for optimization is the anomalies and hypotheses identified in the data from the interviews. These anomalies typically relate either to very labor-intensive work products or to work products with very low estimated effort.
Optimization requires an interdisciplinary team committed to breaking down the identified work products (whether estimated to be high or low) and developing measures to increase efficiency. Value Stream Optimization uses standardized levers to identify practical measures for the respective work products. These measures are described, evaluated by those responsible for the work products, and comprehensively integrated into existing business processes.
The four main building blocks of value stream optimization are :
- Speeding up time-to-market by eliminating low-impact and time-consuming work products
- Reducing process complexity by eliminating bottlenecks, speed bumps, and peaks in process complexity
- Increasing efficiency by optimizing the balance between in-house and external services and setting new standards in core value creation
- Improve product quality by redirecting effort toward high-value work products
The impact of value stream optimization is a significant increase in the R&D budget as a result of the efficiency measures that have been implemented.
A reduction in effort-driven costs of up to 15% is being achieved by focusing on the most valuable core results. Redesigning process operations leads to an increase in workflow efficiency of up to 10% due to reduced process complexity. Finally, transparency across business units increases, which allows for a 5% reduction in roles due to the greater use of cross-functional synergies.
Interested in more articles on value stream analysis? Click here: https://3dse.com/3dse-insights/rd-value-stream-analysis/
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