In product design and development, teams typically prepare both analytical models (calculations and simulations) and physical models (mock-ups and prototypes) to evaluate feasibility, performance, and risks before committing to production. Confirm whether this statement is correct.

Introduction / Context:
Product design rarely jumps straight to manufacturing. Engineers and designers de-risk ideas by building knowledge in two complementary ways: analytical models (hand calculations, spreadsheets, finite-element/CFD simulations) and physical models (mock-ups, breadboards, appearance models, and functional prototypes). This statement asserts that both are often prepared; we evaluate why that is correct.

Given Data / Assumptions:

• Phase: early to mid stages of product development.
• Goal: assess feasibility, performance, manufacturability, and cost.
• Analytical models approximate real behavior using equations and computation.
• Physical models provide tangible validation with real parts or materials.

Concept / Approach:
Analytical models are fast, inexpensive, and allow broad parameter sweeps. Physical models expose integration issues, tolerances, ergonomics, assembly order, and unknown unknowns. Using both creates a closed loop: predict, build, test, and refine.

Step-by-Step Solution:
1) Establish requirements: performance, safety, regulatory, cost.2) Build analytical model: equations and simulation to screen concepts.3) Create physical prototype: mock-up or functional unit to validate key risks.4) Compare results: simulation vs test, update models and design.5) Iterate until metrics converge and risks are acceptable.

Verification / Alternative check:
Industry standards in automotive, aerospace, consumer electronics, and medical devices all institutionalize analysis plus prototyping (e.g., digital twin + bench tests).

Why Other Options Are Wrong:
“Incorrect”: Conflicts with established best practice.“Only analytical models are used”: Ignores real-world variances and integration risks.“Only physical prototypes are used”: Wasteful and slow without prior narrowing via analysis.

Common Pitfalls:
Over-trusting a single high-fidelity simulation, skipping tolerance/DFM checks, or treating a looks-like prototype as performance-representative.

Final Answer:
Correct