Overview
Production Planning & Manufacturing focuses on plan capacity, lots, materials, and shop-floor execution. In the map of OR, it connects Lot sizing, MRP, Line balancing to decisions that must be modeled, solved, explained, and revised as evidence changes.
Production planning covers aggregate planning, lot sizing, MRP, sequencing, bottlenecks, line balancing, and factory-flow decisions. The practical use case is clearest in Factories, Semiconductors, Food production, Pharma, where the method helps turn constraints and tradeoffs into a decision artifact someone can inspect.
Core ideas
Lot sizing
Lot sizing is a core checkpoint for Production Planning & Manufacturing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
MRP
MRP is a core checkpoint for Production Planning & Manufacturing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
Line balancing
Line balancing is a core checkpoint for Production Planning & Manufacturing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
Bottlenecks
Bottlenecks is a core checkpoint for Production Planning & Manufacturing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
Capacity planning
Capacity planning is a core checkpoint for Production Planning & Manufacturing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
How to use it
- 1Start with Factories: write the decision, time horizon, actors, and objective in operational language.
- 2Translate the problem into Lot sizing, MRP, and Line balancing; define units and data sources for each one.
- 3Build a small instance of Production Planning & Manufacturing that can be solved or simulated by hand inspection before using full production data.
- 4Compare the recommendation against a baseline policy, not just against mathematical optimality.
- 5Document assumptions, sensitivity results, and the conditions under which the recommendation should be revisited.
Applications
- Factories: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
- Semiconductors: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
- Food production: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
- Pharma: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
Common pitfalls
- Applying Production Planning & Manufacturing because the label sounds appropriate while leaving the actual decision boundary vague.
- Treating Lot sizing as a technical detail instead of a modeling choice that affects the recommendation.
- Reporting one answer without showing sensitivity to demand, capacity, costs, or behavioral assumptions.
- Ignoring implementation details such as data quality, explainability, ownership, and how users will override bad recommendations.
Resources
- Factory Physics
Topic-specific source curated for Production Planning & Manufacturing.
- Google OR-Tools
Practical toolkit for routing, assignment, CP-SAT, scheduling, flows, LP, and MIP.
- Pyomo Documentation
Python algebraic modeling documentation for optimization and production modeling workflows.
- MIT OCW 15.053 — Optimization Methods in Management Science
Course materials for LP, IP, networks, nonlinear programming, and management science applications.