capacity-planner

capacity-planner

You are capacity-planner - a specialized skill for capacity requirements planning and strategic capacity decisions.

Overview

This skill enables AI-powered capacity planning including:

• Capacity requirements calculation from demand
• Resource capacity documentation
• Capacity gap analysis
• Rough-cut capacity planning (RCCP)
• Detailed capacity requirements planning (CRP)
• Lead, lag, match strategy evaluation
• Make vs buy analysis
• Capacity investment justification

Capabilities

1. Capacity Requirements Calculation

import pandas as pd
import numpy as np

def calculate_capacity_requirements(demand_forecast: pd.DataFrame,
                                   product_routing: dict,
                                   work_center_data: dict):
    """
    Calculate capacity requirements from demand

    demand_forecast: DataFrame with columns ['period', 'product', 'quantity']
    product_routing: {product: [(work_center, time_per_unit), ...]}
    work_center_data: {work_center: {'available_hours': x, 'efficiency': y}}
    """
    requirements = []

    for _, row in demand_forecast.iterrows():
        product = row['product']
        quantity = row['quantity']
        period = row['period']

        if product in product_routing:
            for work_center, time_per_unit in product_routing[product]:
                required_hours = quantity * time_per_unit / 60  # Convert to hours

                requirements.append({
                    'period': period,
                    'product': product,
                    'work_center': work_center,
                    'required_hours': required_hours
                })

    req_df = pd.DataFrame(requirements)

    # Aggregate by work center and period
    summary = req_df.groupby(['period', 'work_center'])['required_hours'].sum().reset_index()

    return {
        'detailed_requirements': req_df,
        'summary': summary
    }

2. Capacity Gap Analysis

def analyze_capacity_gaps(requirements: pd.DataFrame, capacity: dict):
    """
    Identify capacity gaps between requirements and available capacity
    """
    gaps = []

    for _, row in requirements.iterrows():
        wc = row['work_center']
        required = row['required_hours']

        if wc in capacity:
            available = capacity[wc]['available_hours']
            efficiency = capacity[wc].get('efficiency', 1.0)
            effective_capacity = available * efficiency

            gap = required - effective_capacity
            utilization = required / effective_capacity * 100 if effective_capacity > 0 else float('inf')

            gaps.append({
                'period': row['period'],
                'work_center': wc,
                'required_hours': required,
                'available_hours': available,
                'effective_capacity': effective_capacity,
                'gap_hours': gap,
                'utilization_percent': utilization,
                'status': 'overcapacity' if gap > 0 else 'undercapacity' if gap < -effective_capacity * 0.2 else 'balanced'
            })

    gap_df = pd.DataFrame(gaps)

    # Summary
    summary = {
        'total_overcapacity_hours': gap_df[gap_df['gap_hours'] > 0]['gap_hours'].sum(),
        'total_undercapacity_hours': abs(gap_df[gap_df['gap_hours'] < 0]['gap_hours'].sum()),
        'bottleneck_work_centers': gap_df[gap_df['status'] == 'overcapacity']['work_center'].unique().tolist(),
        'underutilized_work_centers': gap_df[gap_df['status'] == 'undercapacity']['work_center'].unique().tolist()
    }

    return {
        'gaps': gap_df,
        'summary': summary
    }

3. Rough-Cut Capacity Planning (RCCP)

def rough_cut_capacity_plan(mps: pd.DataFrame, bill_of_resources: dict,
                           available_capacity: dict):
    """
    Rough-Cut Capacity Planning

    mps: Master Production Schedule with ['period', 'product', 'quantity']
    bill_of_resources: {product: {resource: hours_per_unit}}
    available_capacity: {resource: hours_per_period}
    """
    rccp_results = []

    for _, row in mps.iterrows():
        product = row['product']
        quantity = row['quantity']
        period = row['period']

        if product in bill_of_resources:
            for resource, hours_per_unit in bill_of_resources[product].items():
                load = quantity * hours_per_unit

                rccp_results.append({
                    'period': period,
                    'product': product,
                    'resource': resource,
                    'load_hours': load
                })

    rccp_df = pd.DataFrame(rccp_results)

    # Aggregate by resource and period
    resource_load = rccp_df.groupby(['period', 'resource'])['load_hours'].sum().reset_index()

    # Compare to available
    capacity_status = []
    for _, row in resource_load.iterrows():
        resource = row['resource']
        load = row['load_hours']
        available = available_capacity.get(resource, 0)

        capacity_status.append({
            'period': row['period'],
            'resource': resource,
            'load': load,
            'available': available,
            'utilization': load / available * 100 if available > 0 else float('inf'),
            'feasible': load <= available
        })

    return {
        'rccp_detail': rccp_df,
        'resource_load': resource_load,
        'capacity_status': pd.DataFrame(capacity_status),
        'infeasible_periods': [r for r in capacity_status if not r['feasible']]
    }

4. Capacity Strategy Evaluation

def evaluate_capacity_strategies(demand_growth: float, current_capacity: float,
                                capacity_increment: float, options: dict):
    """
    Evaluate Lead, Lag, and Match capacity strategies

    demand_growth: annual growth rate
    current_capacity: current capacity units
    capacity_increment: size of capacity additions
    options: {'planning_horizon': years, 'discount_rate': rate}
    """
    horizon = options.get('planning_horizon', 5)
    discount_rate = options.get('discount_rate', 0.10)

    strategies = {}

    # Project demand
    demand = [current_capacity * (1 + demand_growth) ** t for t in range(horizon)]

    # LEAD strategy - add capacity before needed
    lead_capacity = [current_capacity]
    lead_additions = []
    for t in range(1, horizon):
        if demand[t] > lead_capacity[-1] * 0.8:  # Add at 80% utilization
            lead_capacity.append(lead_capacity[-1] + capacity_increment)
            lead_additions.append(t)
        else:
            lead_capacity.append(lead_capacity[-1])

    strategies['lead'] = {
        'description': 'Add capacity before demand reaches current capacity',
        'capacity_timeline': lead_capacity,
        'additions': lead_additions,
        'average_utilization': np.mean([d / c for d, c in zip(demand, lead_capacity)]) * 100,
        'lost_sales_risk': 'Low',
        'capital_efficiency': 'Low'
    }

    # LAG strategy - add capacity after demand exceeds
    lag_capacity = [current_capacity]
    lag_additions = []
    for t in range(1, horizon):
        if demand[t-1] > lag_capacity[-1]:  # Add after exceeding
            lag_capacity.append(lag_capacity[-1] + capacity_increment)
            lag_additions.append(t)
        else:
            lag_capacity.append(lag_capacity[-1])

    strategies['lag'] = {
        'description': 'Add capacity after demand exceeds current capacity',
        'capacity_timeline': lag_capacity,
        'additions': lag_additions,
        'average_utilization': np.mean([min(1, d / c) for d, c in zip(demand, lag_capacity)]) * 100,
        'lost_sales_risk': 'High',
        'capital_efficiency': 'High'
    }

    # MATCH strategy - add capacity to match demand
    match_capacity = [current_capacity]
    match_additions = []
    for t in range(1, horizon):
        target_util = 0.85
        needed = demand[t] / target_util
        if