Production Planning (MPS/MRP)

Framework

IRON LAW: Plan Hierarchically — Demand → MPS → MRP → Shop Floor

Production planning flows TOP-DOWN:
1. Demand forecast / customer orders → what to make, when
2. MPS → master schedule for finished goods
3. MRP → material and component requirements (what to buy, when)
4. Shop floor scheduling → which machine, which sequence

Skipping levels (going from demand forecast directly to shop floor)
creates chaos — material shortages, capacity conflicts, missed deliveries.

Planning Hierarchy

Level	Plan	Horizon	Granularity	Decides
Strategic	S&OP (Sales & Operations Planning)	12-18 months	Monthly, product family	Capacity investments, workforce planning
Tactical	MPS (Master Production Schedule)	3-6 months	Weekly, end product	What to produce each week
Operational	MRP (Material Requirements Planning)	4-12 weeks	Daily, component/material	What to order, when, how much
Execution	Shop Floor Scheduling	1-2 weeks	Hourly, work center	Sequence, machine assignment

MPS Process

1. Inputs: Demand forecast + customer orders + current inventory + safety stock targets
2. Logic: For each week, calculate: Production Needed = Demand - Beginning Inventory - Scheduled Receipts + Safety Stock
3. Output: Planned production quantities by week for each finished product
4. Constraint: Must not exceed available capacity (rough-cut capacity planning check)

MRP Process

1. Inputs: MPS (what to make) + BOM (Bill of Materials — what it's made of) + Inventory status + Lead times
2. Logic: "Explode" the BOM — for each finished good, calculate when each component/material must be ordered to arrive in time
3. Output: Planned purchase orders and production orders with dates and quantities
4. Key calculation: Order date = Need date - Lead time

Scheduling Algorithms

Algorithm	Rule	Best When
FCFS (First Come First Served)	Process in order received	Low complexity, fairness matters
SPT (Shortest Processing Time)	Shortest job first	Minimize average flow time
EDD (Earliest Due Date)	Most urgent due date first	Minimize late deliveries
Bottleneck-first	Schedule the constraint first, subordinate rest	Capacity-constrained environments (TOC logic)

SMED (Single-Minute Exchange of Die)

Reduce changeover time to increase flexibility:

1. Separate internal setup (machine stopped) from external (can do while running)
2. Convert internal to external where possible
3. Streamline remaining internal steps
4. Practice and standardize

Output Format

# Production Plan: {Product/Period}

## Demand vs Capacity
| Week | Demand | Available Capacity | Gap |
|------|--------|-------------------|-----|
| W1 | {units} | {units} | {+/-} |

## Master Production Schedule
| Week | Product A | Product B | Product C | Total |
|------|----------|----------|----------|-------|
| W1 | {units} | {units} | {units} | {units} |

## Material Requirements
| Material | Quantity | Order Date | Supplier | Lead Time |
|----------|---------|-----------|----------|-----------|
| {material} | {qty} | {date} | {supplier} | {days} |

## Scheduling
| Work Center | Mon | Tue | Wed | Thu | Fri |
|------------|-----|-----|-----|-----|-----|
| {center} | {job} | {job} | ... | ... | ... |

Gotchas

• Forecast accuracy limits everything: MPS/MRP is only as good as the demand forecast. Track forecast accuracy (MAPE) and build safety stock proportional to forecast error.
• Lead time variability kills plans: If supplier lead time is "2-6 weeks" (4 week variability), you need safety stock for the worst case. Reduce variability through supplier management.
• BOM accuracy is critical for MRP: A wrong BOM means ordering wrong materials. Audit BOMs quarterly.
• Frozen zone: The first 1-2 weeks of MPS should be "frozen" (no changes) to allow stable execution. Changes inside the frozen zone cascade chaos through the system.
• Finite vs infinite capacity: Basic MRP assumes infinite capacity (ignores machine constraints). Always layer a capacity check (rough-cut or detailed) on top of MRP output.

References

• For S&OP process design, see references/sop-process.md
• For demand forecasting methods, see references/demand-forecasting.md