Why explainability matters

A should-cost result is used to make important decisions: select a concept, challenge a supplier, approve an investment or commit to a target price. Decision-makers need more than a predicted total. They need to understand the manufacturing route, material assumptions, cycle time, utilisation, labour content and risk range. Without this transparency, an automated estimate may look precise but remain commercially unusable.

Explainable AI means that the system can show the evidence and logic behind its recommendation in a form that engineers and buyers can review.

What an explainable cost result should contain

A credible output should display the proposed material and process sequence, key geometry or product attributes, reference data used, major cost drivers and uncertainty. It should show how the result changes when volume, location or technical parameters change. Comparable historical parts can provide context, but their differences must be visible.

The system should also distinguish between extracted facts, user inputs, rules and predictions. For example, a material designation read from a drawing is different from an AI-inferred material based on similar components. This distinction supports responsible review.

A hybrid model architecture

One practical architecture uses AI for recognition and recommendation, then applies transparent cost equations. The AI identifies features, classifies the component and proposes a routing. A process model calculates material usage, machine time, labour, scrap and overhead. The expert can edit every assumption and see the financial effect immediately.

This hybrid structure is easier to validate than an end-to-end black box. It can also handle limited data because established engineering logic remains available when statistical evidence is weak.

Validation and feedback

Automated models should be tested against known production cases, supplier quotations and expert calculations. Accuracy should be evaluated by process family, volume range and product type rather than only as one overall average. Large deviations need root-cause analysis: Was the routing wrong? Was the geometry interpreted incorrectly? Was the market price outdated?

Validated results should feed back into the knowledge base. Corrections made by experts are valuable training information, provided they are reviewed and structured.

Responsible deployment

Companies should define where automated estimates may be used and where expert approval is mandatory. Early concept screening can tolerate wider uncertainty than a supplier negotiation or sourcing decision. Access rights, data protection and documentation must match the application.

Explainability is not an obstacle to AI adoption; it is the condition for adoption in industrial decision processes. When users can inspect, challenge and improve the model, automation becomes a trusted extension of cost engineering rather than a competing black box.

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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