Why traditional cost calculations reach their limits

Many industrial companies already calculate product costs, yet the results are often distributed across spreadsheets, individual expert files and different software systems. A calculation may be technically correct, but it is difficult to update, compare or reuse. The problem becomes more visible when material prices change, production locations are shifted, volumes are revised or a new design concept is introduced. Cost engineers then spend significant time collecting inputs instead of analysing alternatives and supporting decisions.

Digital Cost and Value Engineering changes this situation. The goal is not simply to replace Excel with another application. It is to create a connected knowledge environment in which product structures, materials, manufacturing processes, cost drivers, requirements and functions can be evaluated consistently. This makes cost knowledge available earlier in development and more useful for procurement, engineering, product management and management.

What digital cost intelligence looks like in practice

A useful digital model combines a transparent product structure with process-based cost logic. Each component is linked to the relevant material, weight, manufacturing route, equipment, labour content, cycle time, yield, scrap and overhead assumptions. These parameters can then be changed systematically. Instead of building a new calculation for every question, teams can run scenarios: What happens when annual volume doubles? Which production location is most competitive? How much cost is driven by an unnecessarily tight tolerance? Which concept offers the best balance between performance and cost?

The important step is to connect cost models with value-oriented methods. A lower cost is only beneficial when the required function, quality, reliability and customer value are maintained. Functional analysis, Kano models, concept evaluation and target costing therefore belong in the same decision process. Digitalisation supports the workflow, but engineering judgement remains essential.

A pragmatic implementation approach

Companies do not need to digitalise everything at once. A practical starting point is a defined product family or recurring manufacturing technology. Existing calculations are reviewed, key cost drivers are identified and the underlying assumptions are standardised. Reusable reference models can then be built for materials and processes such as casting, machining, forming, plastics processing or assembly.

The next step is to establish governance: Who owns the model? Which data sources are approved? How are market indices and supplier quotations incorporated? How are changes documented? A model that is not maintained quickly loses credibility. For this reason, roles, update cycles and plausibility checks are as important as the software itself.

The business benefit

Digital cost intelligence reduces repetitive effort and improves the speed and quality of decisions. Cost engineers can focus on interpretation and optimisation rather than data preparation. Development teams receive cost feedback while concepts are still flexible. Procurement gains fact-based negotiation arguments and target prices. Management sees the economic impact of technical choices before investments are committed.

The result is a shared language between commercial and technical functions. Costs become explainable, assumptions become visible and alternatives become comparable. That is the real value of Digital Cost and Value Engineering: not more calculations, but better decisions based on connected industrial knowledge.

Conclusion

The transition from isolated cost calculation to digital cost intelligence is an organisational and methodological development. Technology enables the change, but the decisive factors are transparent models, reliable data and interdisciplinary collaboration. Companies that build this capability can react faster to market volatility, improve product concepts and establish cost and value as continuous design parameters.

RENNET perspective

RENNET combines industrial expertise, Cost and Value Engineering methods, manufacturing knowledge and digital tools to support fact-based product decisions.

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