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capacity-planning

Analyze capacity metrics and identify bottlenecks in supply chain operations.

56|16|Updated Oct 18, 2025
Search Tags:#resource optimization#capacity planning#CRP#bottleneck analysis#OEE#TOC#aggregate planning
One-click install
npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill capacity-planning-kishorkukreja
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: capacity-planning
Source: https://github.com/kishorkukreja/awesome-supply-chain/tree/main/skills/capacity-planning
Command: npx skills add https://github.com/kishorkukreja/awesome-supply-chain --skill capacity-planning-kishorkukreja

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, pulp, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps organizations align their production, warehouse, or transportation capacity with fluctuating demand, preventing costly over- or under-utilization and ensuring operational efficiency.

Core Features & Use Cases

  • Capacity Analysis: Measure and analyze key capacity metrics like design capacity, effective capacity, and OEE.
  • Bottleneck Identification: Pinpoint constraints in production or logistics processes using methods like TOC and DBR.
  • Optimization Models: Employ linear programming for aggregate planning and CRP for detailed work center load balancing.
  • Investment Analysis: Evaluate capacity expansion projects using NPV and decision trees under uncertainty.
  • Use Case: A manufacturing plant is experiencing frequent delays. This Skill can analyze its production lines, identify the bottleneck assembly station, and suggest optimal scheduling or capacity adjustments to improve overall throughput.

Quick Start

Use the capacity-planning skill to analyze the current capacity utilization for our main production line.

Frequently Asked Questions about capacity-planning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify production bottlenecks and optimize throughput in a manufacturing plant?▼

Bottleneck identification uses Theory of Constraints (TOC) and Drum-Buffer-Rope (DBR) methodologies to pinpoint production constraints. The Skill then suggests optimal scheduling or capacity adjustments to resolve these limits and improve overall manufacturing throughput.

Can I use linear programming for aggregate planning and work center load balancing?▼

Linear programming supports aggregate planning to match supply with demand fluctuations. Capacity Requirements Planning (CRP) models further balance work center loads by calculating detailed resource requirements and optimizing allocation across production lines.

How do I calculate and analyze OEE and effective capacity for my supply chain?▼

Capacity analysis measures key metrics including design capacity, effective capacity, and Overall Equipment Effectiveness (OEE). These quantitative models utilize pandas and numpy to evaluate current utilization rates and identify operational efficiency gaps.

What is the best way to evaluate capacity expansion projects under demand uncertainty?▼

Investment analysis evaluates capacity expansion projects using Net Present Value (NPV) and decision trees. This approach models financial outcomes under uncertainty, helping organizations make strategic investment decisions for scaling production or warehouse facilities.

Do I need Python libraries like PuLP and matplotlib to run capacity planning models?▼

Yes, the Skill requires pandas, numpy, PuLP, and matplotlib to perform quantitative modeling. PuLP drives linear programming for optimization, while matplotlib visualizes capacity utilization metrics and bottleneck analysis results.

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