Capacity Planning & Simulation Model

The Prompt

This prompt creates a comprehensive capacity planning model with simulation capabilities:

<operations_strategist_role>
You are a Manufacturing Strategy Director who has led capacity planning for Fortune 500 companies undergoing rapid growth. You've justified and executed $500M+ in capacity investments and understand both the engineering details and the financial decision-making. You balance optimism about growth with pragmatic risk assessment.
</operations_strategist_role>

<capacity_planning_mission>
Create a capacity planning model that:
1. Maps current capacity across all resources
2. Identifies bottlenecks and utilization imbalances
3. Projects future capacity requirements based on demand scenarios
4. Models capacity investment options with ROI analysis
5. Simulates production throughput under different configurations
</capacity_planning_mission>

<system_description>
<production_resources>
[DESCRIBE YOUR PRODUCTION SYSTEM]

For each resource (machine, line, work center):
| Resource | Capacity_Units | Capacity_Period | Available_Hours | OEE_% | Current_Utilization |
|----------|----------------|-----------------|-----------------|-------|---------------------|
| Line_1   | 500 units      | per day         | 16 hrs (2 shift)| 75%   | 88%                 |
| CNC_01   | 50 parts       | per hour        | 20 hrs (3 shift)| 82%   | 92%                 |
| Assembly | 200 units      | per day         | 8 hrs (1 shift) | 90%   | 95%                 |

Include:
- Labor-constrained resources (shifts, headcount)
- Equipment-constrained resources (machines, lines)
- Shared resources (testing, QC, packaging)
</production_resources>

<product_requirements>
For each product/family, specify resource consumption:

| Product | Resource_1_Time | Resource_2_Time | Resource_3_Time | Demand_Units |
|---------|-----------------|-----------------|-----------------|--------------|
| Prod_A  | 0.5 hr          | 1.0 hr          | 0.25 hr         | 1000/month   |
| Prod_B  | 0.3 hr          | 0.5 hr          | 0.50 hr         | 2000/month   |

Include routing if products have different flows.
</product_requirements>

<demand_scenarios>
Define demand projections:
- **Current State**: Present demand levels
- **Base Case**: Expected growth trajectory
- **High Growth**: Aggressive market scenario
- **New Product**: Impact of new product launches

Format:
| Period | Scenario    | Prod_A | Prod_B | Prod_New |
|--------|-------------|--------|--------|----------|
| Q1     | Base        | 1000   | 2000   | 0        |
| Q2     | Base        | 1050   | 2100   | 0        |
| Q1     | High Growth | 1200   | 2400   | 0        |
</demand_scenarios>

<capacity_options>
Available capacity expansion options:
| Option | Resource | Capacity_Gain | Lead_Time | CapEx | OpEx/Year | Notes |
|--------|----------|---------------|-----------|-------|-----------|-------|
| OT     | Labor    | +25%          | 0         | $0    | +$150K    | Overtime premium |
| Shift  | Line_1   | +50%          | 2 months  | $50K  | +$400K    | Add 3rd shift |
| Equipment | CNC   | +100%         | 6 months  | $800K | +$80K     | New CNC machine |
| Outsource | All  | +30%          | 1 month   | $0    | +$200K    | External supplier |
</capacity_options>
</system_description>

<analysis_framework>
Build comprehensive capacity model with Python:

### Module 1: Current State Analysis
- Calculate effective capacity (rated × OEE)
- Calculate required capacity from current demand
- Calculate utilization by resource
- Identify bottleneck resource

Visualization: Utilization waterfall chart by resource

### Module 2: Load vs. Capacity Timeline
- Project load requirements over planning horizon
- Overlay capacity levels
- Identify when demand exceeds capacity
- Calculate lead time to capacity constraint

Visualization: Demand vs. Capacity area chart over 24 months

### Module 3: Bottleneck Analysis
Apply Theory of Constraints logic:
- Identify constraint resource
- Calculate constraint impact on throughput
- Model "what if we add capacity to bottleneck?"
- Identify next bottleneck after constraint removed

Visualization: Bottleneck migration chart

### Module 4: Capacity Investment Scenarios
For each expansion option, calculate:
- Incremental capacity units
- Investment required (CapEx + setup)
- Payback period
- NPV over 5 years
- Break-even demand level

Model combinations:
- Option A alone
- Option B alone
- Options A + B
- All options

### Module 5: Monte Carlo Simulation
Simulate production outcomes:
- Input: Demand uncertainty (±X%)
- Input: OEE variability (±Y%)
- Input: Breakdown probability
- Output: Distribution of throughput outcomes
- Output: Service level probability

Visualization: Probability distribution of throughput with capacity scenarios

### Module 6: Decision Matrix
| Scenario | Demand_Level | Bottleneck | Recommended_Action | Investment | ROI |
</analysis_framework>

<output_deliverables>
Generate executive decision package:

1. **Capacity Dashboard**
   - Current utilization gauges by resource
   - Constraint identification (red highlight)
   - Timeline to capacity ceiling

2. **Scenario Comparison**
   - Side-by-side analysis of expansion options
   - Financial returns comparison
   - Risk assessment

3. **Investment Recommendation**
   - Recommended capacity strategy
   - Phased investment timeline
   - Trigger points for next phase

4. **Simulation Results**
   - Throughput probability distributions
   - Confidence intervals by scenario

5. **Sensitivity Analysis**
   - Which assumptions matter most?
   - Where should we invest in better data?

6. **Complete Python Model**
   - Parameterized for ongoing use
   - Monte Carlo simulation engine included
</output_deliverables>

How to Use This Prompt

1. Map your resources: List all capacity-constrained resources
2. Define consumption rates: How much of each resource each product consumes
3. Model demand scenarios: Conservative, base, and aggressive
4. List expansion options: All ways you could add capacity
5. Run analysis: Get bottleneck identification and investment recommendations

Capacity Planning Simulations

After running initial model, explore scenarios:

• “What if OEE improves from 75% to 85%?”
• “When do we hit capacity if demand grows 15% annually?”
• “What’s the payback if we automate the bottleneck?”
• “Should we add a shift before buying equipment?”
• “Model the impact of adding Product D with these resource requirements”

Integration Points

• Feed demand scenarios from S&OP process
• Link OEE data from MES/production systems
• Export investment options to capital planning
• Connect payback analysis to financial models