The hidden dependency on individual experts

Industrial cost analysis depends on detailed knowledge: achievable cycle times, machine rates, material yields, tooling concepts, labour allocation, reject rates and the influence of design features on manufacturing effort. In many organisations, this knowledge exists mainly in the experience of a small number of specialists. Their expertise is valuable, but it is difficult to scale and vulnerable when people change roles or leave the company.

A manufacturing knowledge base turns this experience into a structured company asset. It captures not only numerical values, but also the logic behind them: which process is suitable for which geometry, which equipment is required, how capacity and utilisation influence the machine rate, and which assumptions are appropriate at a given maturity level.

What should be stored

A useful knowledge base combines several layers. Material data includes designation, density, market price, typical purchase form, yield and scrap value. Process data includes routing steps, equipment, labour, cycle-time drivers, batch sizes, parts per cycle and quality losses. Equipment data includes investment, depreciation, energy demand, maintenance and utilisation. Reference projects and validated calculations show how these elements interact in real products.

The structure should be detailed enough to support engineering decisions, but not so complex that maintenance becomes impossible. Standard taxonomies and naming conventions are essential. Without them, the same process may appear under several names and comparisons become unreliable.

From database to decision support

The knowledge base creates value when it is integrated into day-to-day workflows. A cost engineer should be able to select a manufacturing process and receive a credible starting model that can be adapted to the specific component. A designer should receive early feedback on cost-sensitive features. A buyer should be able to compare a supplier quotation with a transparent process model and relevant market indices.

Digital tools and AI can improve search, classification and parameter suggestions. For example, a system can propose likely manufacturing routes based on component attributes or identify similar historical parts. However, recommendations must remain explainable. Users need to understand which data and assumptions led to the result.

How to build the knowledge base step by step

The best starting point is usually a high-impact product or process family. Existing models, supplier data and expert interviews are consolidated. The team defines a minimum data standard and validates a limited number of reference calculations. Only after the structure has proven useful should it be expanded.

Ownership is crucial. Process experts should review technical parameters, procurement should contribute market information, and cost engineering should govern the calculation logic. Version control, approval status and source quality should be visible. A value entered ten years ago without context should not carry the same weight as a recently validated production benchmark.

The benefit for the organisation

A manufacturing knowledge base improves consistency, speed and resilience. New team members learn faster, calculations become comparable and proven knowledge can be reused across projects. It also creates the foundation for automation and AI-supported analysis because algorithms need structured, trustworthy industrial data.

The strategic advantage is clear: knowledge that was once fragmented becomes accessible and repeatable. This enables cost engineering to support more projects without sacrificing technical depth and helps the organisation make better product decisions at scale.

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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