An algorithm designer may risk redoing his work if problem is not completely understood

Explanation

A computer scientist is tasked with designing an algorithm to sort a list of customer orders based on priority. However, they misunderstand the requirements and assume that priority is determined only by order value. They design an efficient sorting algorithm (e.g., a modified quicksort) that arranges orders from highest to lowest monetary value.

Later, they learn that priority also depends on:

1. Customer loyalty tier (e.g., premium users come first).
2. Delivery deadlines (urgent orders take precedence).

Because the problem was not fully understood initially, the algorithm fails to meet the actual requirements. The designer must now redo the work, either:

• Combining multiple sorting criteria (e.g., a stable sort by deadline, then loyalty, then value).
• Rewriting the algorithm entirely (e.g., using a priority queue with a composite key).

Result: Time and effort are wasted due to incomplete problem analysis. This highlights why thorough requirement-gathering is critical in algorithm design.