OMBA 102 · Unit 7 of 7
Optimization and Managerial Modeling
Data, Statistics and Managerial Decisions
Start unit · 5 lessons →Learning objectives
After completing this unit, you will be able to:
- Formulate optimization problems with objectives and decision variables
- Represent constraints, feasible regions, and tradeoffs
- Solve linear programs in Excel or Google Sheets
- Build resource allocation and product-mix models
- Interpret solver results and stress-test recommendations
Why this matters
Capacity, staffing, blend, and budget allocation are optimization problems whether you name them or not. Unit 7 teaches you to structure those problems explicitly and use spreadsheet solvers to find feasible, optimal (or good-enough) solutions.
Unit overview
Work through the five lessons below in order. Build solver models as you read.
| # | Lesson | Core idea |
|---|---|---|
| 1 | Optimization Problems, Objectives, and Decision Variables | LP setup |
| 2 | Constraints, Feasible Regions, and Tradeoffs | Feasibility vs optimality |
| 3 | Linear Programming in Excel or Google Sheets | Solver mechanics |
| 4 | Resource Allocation and Product Mix Models | Capacity and margin |
| 5 | Interpreting Solver Results and Stress Testing Recommendations | Shadow prices and sensitivity |
Connection to applied work
Complete your OMBA 102 applied project with an optimization or allocation model tied to a real constraint (budget, hours, or capacity). Document assumptions and stress test one input.
Practice
- Write objective, variables, and constraints for a staffing or mix problem.
- Graph a two-variable feasible region and identify the optimal corner.
- Solve a small LP in Sheets/Excel and interpret the binding constraint.
- Change one coefficient by 10% and note whether the solution flips.
Knowledge check
- What makes a problem a linear program?
- What is an infeasible model telling you?
- What is a shadow price?
- When is the optimal solution at a corner point?
- How do you stress-test a solver recommendation?
Key takeaways
- Optimization makes tradeoffs explicit under constraints.
- Solver output requires managerial interpretation, not blind trust.
- OMBA 102 ends with models that connect data, uncertainty, and decisions.
- Complete all lessons and assessments to finish the course.
Unit assessment
Complete each section below. Score 80%+ on the quiz to finish this unit's assessment.
Exercises
Apply what you learned in this unit with structured practice.
Deliverable
300–500 word analysis document saved to your portfolio under OMBA 102.
Rubric
- • Framework applied correctly (not just named)
- • Specific evidence from a real example
- • Clear recommendation with tradeoffs acknowledged
- • Professional writing with source citation
Deliverable
Problem solutions + 150-word reflection in your OMBA 102 workbook.
Rubric
- • Attempted all practice items before checking answers
- • Honest reflection on errors
- • Identifies a specific review action
Model / spreadsheet
Build or extend a spreadsheet model tied to this unit.
Deliverable
Structured model document (2+ pages) · One-paragraph summary of key insight from the model · Screenshot or export saved to portfolio
Rubric
- • Assumptions stated explicitly
- • Logic is auditable (formulas or steps visible)
- • Output answers a specific business question
- • Sensitivity or scenario considered
Knowledge quiz
Check your understanding before marking the unit complete.
1. In a product-mix LP, decision variables typically represent:
2. FreshPack maximizes 3S + 5E + 4D subject to capacity and demand caps. The objective coefficients are:
3. A constraint is binding at the optimum when:
4. The feasible region for a linear program is:
5. Solver returns Optimal but hours used exceed capacity. The most likely setup error is:
6. A shadow price of $12 on a 160-hour capacity constraint means:
7. Minimum total units S+E+D ≥ 350 in FreshPack is modeled in Solver as:
8. Stress-testing an LP solution should include: