In building a model base (e.g., for a Decision Support System), preparation cannot proceed until which prerequisites are met?

Introduction / Context:
A model base houses analytical, statistical, and optimization models supporting managerial decisions. Standing up a model base requires foundational choices and people with the competencies to implement and maintain those models. This item asks which prerequisites are essential before preparation proceeds meaningfully.

Given Data / Assumptions:

• We are at the stage of enabling model execution and integration.
• Platform compatibility and staff expertise are critical.
• Facilities (while useful) are less constraining than hardware and skilled personnel.

Concept / Approach:
Models depend on the computing environment (OS, libraries, performance characteristics) and on MIS/analytics staff to curate, validate, and operationalize them. Without a selected platform, technical architecture decisions are premature; without the right staff, models cannot be implemented or governed effectively.

Step-by-Step Solution:
1) Identify critical path dependencies: platform and people. 2) Evaluate each option’s necessity at start-up. 3) Recognize that both hardware selection and staff organization are mandatory. 4) Choose the combined prerequisite (both a and b).

Verification / Alternative check:
DSS implementation references call out environment standardization and cross-functional MIS/analytics staffing as preconditions for effective model base deployment.

Why Other Options Are Wrong:
Option A or B alone is insufficient; both are required.
Option C: Facilities are supportive but not the gating factor.
Option E: Incorrect because there are prerequisites.

Common Pitfalls:
Attempting to prototype models before establishing platform/toolchain or assigning accountable owners; this leads to rework and inconsistency.

Final Answer:
Both (a) and (b)