Precision vs Simplicity

Business Context

Precise measures can incorporate multiple adjustments, detailed definitions, statistical controls, and carefully specified calculations. This can improve analytical accuracy, especially when systems differ materially. However, highly precise measures may become difficult for managers and operating teams to understand, reproduce, or influence. Simpler measures improve communication and usability but may omit variables required for valid comparison.

Benchmarking TRIZ Resolution

Organizations should separate calculation complexity from decision presentation. Detailed analytical models can generate robust benchmark values while simplified indicators communicate the result and its principal drivers. Sensitivity ranges should show whether greater precision materially changes the conclusion. If additional decimal accuracy or adjustment does not alter the decision, it should not dominate the reporting architecture.

Applicable TRIZ Principles

Principle 2 – Taking Out removes analytical complexity that does not change the decision.

Principle 7 – Nested Doll places detailed calculations beneath simpler management indicators.

Principle 24 – Intermediary uses interpretable measures to connect sophisticated analysis with operational decisions.

Expected Outcome

Higher analytical precision where necessary

Easier interpretation of performance measures

Reduced false precision

Greater managerial use of benchmarking information

Decision Indicators

Early indicators include:

Managers cannot explain how important benchmark measures are calculated.

Small methodological changes create insignificant operational differences but extensive analytical debate.

Simplified measures generate materially different conclusions from detailed models.

Specialists become necessary for routine interpretation.

Precision increases without improving decision quality.

These indicators suggest that calculation sophistication and managerial usability need separate design layers.