Name: Capacity Planning
Author: kishorkukreja

Skills suchen.../

Capacity Planning | Skills Pool

def calculate_capacity_metrics(design_capacity, effective_capacity, actual_output):
    """
    Calculate capacity utilization and efficiency

    Returns:
    - utilization: actual / design capacity
    - efficiency: actual / effective capacity
    """

    utilization = (actual_output / design_capacity) * 100
    efficiency = (actual_output / effective_capacity) * 100

    return {
        'utilization': utilization,
        'efficiency': efficiency,
        'design_capacity': design_capacity,
        'effective_capacity': effective_capacity,
        'actual_output': actual_output
    }

# Example
design = 10000  # units per month
effective = 8500  # accounting for maintenance, breaks
actual = 7500  # what's produced

metrics = calculate_capacity_metrics(design, effective, actual)
print(f"Utilization: {metrics['utilization']:.1f}%")  # 75%
print(f"Efficiency: {metrics['efficiency']:.1f}%")    # 88.2%

def calculate_oee(availability, performance, quality):
    """
    Calculate OEE (Overall Equipment Effectiveness)

    Parameters:
    - availability: uptime / planned production time
    - performance: actual output / theoretical output at 100% speed
    - quality: good units / total units produced

    World-class OEE: > 85%
    """

    oee = availability * performance * quality * 100

    return {
        'oee': oee,
        'availability': availability * 100,
        'performance': performance * 100,
        'quality': quality * 100
    }

# Example
availability = 0.90  # 90% uptime
performance = 0.85   # 85% of theoretical speed
quality = 0.95       # 95% good units

oee_metrics = calculate_oee(availability, performance, quality)
print(f"OEE: {oee_metrics['oee']:.1f}%")  # 72.7%

import pandas as pd
import numpy as np

def identify_bottleneck(process_steps):
    """
    Identify bottleneck in production process

    Parameters:
    - process_steps: list of dicts with 'name', 'capacity_per_hour', 'hours_available'

    Returns bottleneck step and throughput
    """

    df = pd.DataFrame(process_steps)

    # Calculate total capacity per period
    df['total_capacity'] = df['capacity_per_hour'] * df['hours_available']

    # Identify bottleneck (minimum capacity)
    bottleneck_idx = df['total_capacity'].idxmin()
    bottleneck = df.loc[bottleneck_idx]

    # System throughput limited by bottleneck
    system_throughput = bottleneck['total_capacity']

    # Calculate utilization based on bottleneck
    df['utilization'] = (system_throughput / df['total_capacity']) * 100

    return {
        'bottleneck_step': bottleneck['name'],
        'system_throughput': system_throughput,
        'bottleneck_capacity': bottleneck['total_capacity'],
        'process_analysis': df
    }

# Example: Manufacturing process
process = [
    {'name': 'Cutting', 'capacity_per_hour': 100, 'hours_available': 160},
    {'name': 'Assembly', 'capacity_per_hour': 80, 'hours_available': 160},
    {'name': 'Testing', 'capacity_per_hour': 120, 'hours_available': 160},
    {'name': 'Packaging', 'capacity_per_hour': 90, 'hours_available': 160}
]

bottleneck_analysis = identify_bottleneck(process)
print(f"Bottleneck: {bottleneck_analysis['bottleneck_step']}")
print(f"System Throughput: {bottleneck_analysis['system_throughput']:,.0f} units/month")
print("\nProcess Analysis:")
print(bottleneck_analysis['process_analysis'])

class DrumBufferRope:
    """
    Theory of Constraints scheduling method

    - Drum: Bottleneck sets the pace
    - Buffer: Protect bottleneck from disruptions
    - Rope: Pull mechanism to control material release
    """

    def __init__(self, bottleneck_capacity, buffer_time_days=3):
        self.bottleneck_capacity = bottleneck_capacity
        self.buffer_time = buffer_time_days

    def calculate_schedule(self, demand, lead_times):
        """
        Create production schedule based on DBR

        Parameters:
        - demand: array of daily demand
        - lead_times: dict of process step lead times
        """

        schedule = []

        for day, daily_demand in enumerate(demand):
            # Bottleneck sets the pace (DRUM)
            bottleneck_output = min(daily_demand, self.bottleneck_capacity)

            # Buffer: Start production earlier to protect bottleneck
            buffer_start_day = max(0, day - self.buffer_time)

            # Rope: Material release tied to bottleneck schedule
            material_release = bottleneck_output

            schedule.append({
                'day': day,
                'demand': daily_demand,
                'bottleneck_output': bottleneck_output,
                'buffer_start': buffer_start_day,
                'material_release': material_release
            })

        return pd.DataFrame(schedule)

# Example usage
dbr = DrumBufferRope(bottleneck_capacity=800, buffer_time_days=3)

# Daily demand for next 10 days
demand = np.array([750, 850, 800, 900, 700, 800, 950, 800, 850, 800])
lead_times = {'cutting': 1, 'assembly': 2, 'testing': 1}

schedule = dbr.calculate_schedule(demand, lead_times)
print(schedule)

from pulp import *
import pandas as pd
import numpy as np

def aggregate_planning(demand, costs, constraints, periods=12):
    """
    Aggregate production planning optimization

    Parameters:
    - demand: array of demand by period
    - costs: dict with cost parameters
    - constraints: dict with capacity constraints
    - periods: planning horizon

    Returns optimal plan
    """

    # Create problem
    prob = LpProblem("Aggregate_Planning", LpMinimize)

    # Decision variables
    P = LpVariable.dicts("Production", range(periods), lowBound=0)
    W = LpVariable.dicts("Workforce", range(periods), lowBound=0, cat='Integer')
    O = LpVariable.dicts("Overtime", range(periods), lowBound=0)
    I = LpVariable.dicts("Inventory", range(periods), lowBound=0)
    H = LpVariable.dicts("Hire", range(periods), lowBound=0, cat='Integer')
    F = LpVariable.dicts("Fire", range(periods), lowBound=0, cat='Integer')
    B = LpVariable.dicts("Backorder", range(periods), lowBound=0)
    S = LpVariable.dicts("Subcontract", range(periods), lowBound=0)

    # Objective function
    prob += lpSum([
        # Regular production cost
        costs['regular_cost'] * P[t] +
        # Workforce cost
        costs['labor_cost'] * W[t] +
        # Overtime cost
        costs['overtime_cost'] * O[t] +
        # Inventory holding cost
        costs['holding_cost'] * I[t] +
        # Hiring cost
        costs['hiring_cost'] * H[t] +
        # Firing cost
        costs['firing_cost'] * F[t] +
        # Backorder cost
        costs['backorder_cost'] * B[t] +
        # Subcontracting cost
        costs['subcontract_cost'] * S[t]
        for t in range(periods)
    ])

    # Constraints

    # Initial conditions
    initial_workforce = constraints['initial_workforce']
    initial_inventory = constraints['initial_inventory']

    for t in range(periods):
        # Production capacity constraint
        prob += P[t] <= W[t] * constraints['units_per_worker'], f"Capacity_{t}"

        # Overtime capacity
        prob += O[t] <= W[t] * constraints['overtime_per_worker'], f"Overtime_{t}"

        # Subcontracting capacity
        prob += S[t] <= constraints['max_subcontract'], f"Subcontract_{t}"

        # Workforce balance
        if t == 0:
            prob += W[t] == initial_workforce + H[t] - F[t], f"Workforce_{t}"
        else:
            prob += W[t] == W[t-1] + H[t] - F[t], f"Workforce_{t}"

        # Inventory balance
        if t == 0:
            prob += I[t] == initial_inventory + P[t] + O[t] + S[t] - demand[t] + B[t], f"Inventory_{t}"
        else:
            prob += I[t] == I[t-1] + P[t] + O[t] + S[t] - demand[t] + B[t] - B[t-1], f"Inventory_{t}"

        # Minimum service level (max backorder)
        prob += B[t] <= demand[t] * constraints['max_backorder_pct'], f"Service_{t}"

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    # Extract results
    results = {
        'status': LpStatus[prob.status],
        'total_cost': value(prob.objective),
        'production': [P[t].varValue for t in range(periods)],
        'workforce': [W[t].varValue for t in range(periods)],
        'overtime': [O[t].varValue for t in range(periods)],
        'inventory': [I[t].varValue for t in range(periods)],
        'hired': [H[t].varValue for t in range(periods)],
        'fired': [F[t].varValue for t in range(periods)],
        'backorders': [B[t].varValue for t in range(periods)],
        'subcontract': [S[t].varValue for t in range(periods)]
    }

    # Create summary DataFrame
    df = pd.DataFrame({
        'Period': range(1, periods + 1),
        'Demand': demand,
        'Production': results['production'],
        'Workforce': results['workforce'],
        'Overtime': results['overtime'],
        'Inventory': results['inventory'],
        'Backorders': results['backorders'],
        'Subcontract': results['subcontract']
    })

    results['summary'] = df

    return results

# Example usage
demand = np.array([1000, 1200, 1500, 1800, 2000, 1800,
                   1600, 1400, 1300, 1200, 1100, 1000])

costs = {
    'regular_cost': 50,
    'labor_cost': 2000,      # per worker per period
    'overtime_cost': 75,
    'holding_cost': 5,
    'hiring_cost': 1000,
    'firing_cost': 1500,
    'backorder_cost': 100,
    'subcontract_cost': 80
}

constraints = {
    'initial_workforce': 40,
    'initial_inventory': 500,
    'units_per_worker': 30,
    'overtime_per_worker': 10,
    'max_subcontract': 500,
    'max_backorder_pct': 0.10
}

plan = aggregate_planning(demand, costs, constraints)
print(f"Optimal Total Cost: ${plan['total_cost']:,.0f}")
print("\nProduction Plan:")
print(plan['summary'])

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

class CapacityRequirementsPlanning:
    """
    CRP - Calculate and analyze capacity requirements
    based on production schedule and routings
    """

    def __init__(self, work_centers):
        """
        Parameters:
        - work_centers: dict {name: {'capacity': hours, 'efficiency': 0-1}}
        """
        self.work_centers = work_centers

    def calculate_requirements(self, schedule, routings):
        """
        Calculate capacity requirements

        Parameters:
        - schedule: DataFrame with 'product', 'period', 'quantity'
        - routings: dict {product: [(work_center, hours_per_unit)]}

        Returns DataFrame with requirements by work center and period
        """

        requirements = []

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

            # Get routing for product
            routing = routings.get(product, [])

            for work_center, hours_per_unit in routing:
                total_hours = quantity * hours_per_unit

                # Adjust for efficiency
                efficiency = self.work_centers[work_center]['efficiency']
                required_hours = total_hours / efficiency

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

        df = pd.DataFrame(requirements)

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

        # Add capacity and utilization
        summary['capacity'] = summary['work_center'].map(
            lambda wc: self.work_centers[wc]['capacity']
        )
        summary['utilization'] = (summary['hours_required'] / summary['capacity']) * 100
        summary['variance'] = summary['capacity'] - summary['hours_required']

        return summary

    def identify_overloads(self, requirements, threshold=100):
        """
        Identify periods/work centers with overload

        Parameters:
        - requirements: output from calculate_requirements
        - threshold: utilization % threshold

        Returns overloaded resources
        """

        overloads = requirements[requirements['utilization'] > threshold].copy()
        overloads = overloads.sort_values(['period', 'work_center'])

        return overloads

    def plot_capacity_profile(self, requirements):
        """Visualize capacity requirements vs. available"""

        work_centers = requirements['work_center'].unique()

        fig, axes = plt.subplots(len(work_centers), 1,
                                figsize=(12, 4 * len(work_centers)),
                                squeeze=False)

        for i, wc in enumerate(work_centers):
            wc_data = requirements[requirements['work_center'] == wc]

            ax = axes[i, 0]

            # Plot capacity line
            ax.axhline(y=wc_data['capacity'].iloc[0],
                      color='green', linestyle='--',
                      linewidth=2, label='Capacity')

            # Plot requirements
            ax.bar(wc_data['period'], wc_data['hours_required'],
                  alpha=0.7, label='Required')

            # Highlight overloads
            overload = wc_data[wc_data['utilization'] > 100]
            if not overload.empty:
                ax.bar(overload['period'], overload['hours_required'],
                      color='red', alpha=0.7, label='Overload')

            ax.set_title(f'{wc} - Capacity Profile')
            ax.set_xlabel('Period')
            ax.set_ylabel('Hours')
            ax.legend()
            ax.grid(True, alpha=0.3)

        plt.tight_layout()
        return fig

# Example usage
work_centers = {
    'Cutting': {'capacity': 160, 'efficiency': 0.90},
    'Welding': {'capacity': 160, 'efficiency': 0.85},
    'Assembly': {'capacity': 160, 'efficiency': 0.92},
    'Inspection': {'capacity': 160, 'efficiency': 0.95}
}

crp = CapacityRequirementsPlanning(work_centers)

# Production schedule
schedule = pd.DataFrame({
    'product': ['A', 'A', 'B', 'B', 'C', 'C'] * 3,
    'period': [1, 2, 1, 2, 1, 2] * 3,
    'quantity': [100, 120, 80, 90, 60, 70] * 3
})

# Routings: hours per unit at each work center
routings = {
    'A': [('Cutting', 0.5), ('Welding', 0.8), ('Assembly', 1.0), ('Inspection', 0.3)],
    'B': [('Cutting', 0.6), ('Assembly', 1.2), ('Inspection', 0.4)],
    'C': [('Cutting', 0.4), ('Welding', 1.0), ('Assembly', 0.8), ('Inspection', 0.2)]
}

# Calculate requirements
requirements = crp.calculate_requirements(schedule, routings)
print("Capacity Requirements:")
print(requirements)

# Identify overloads
overloads = crp.identify_overloads(requirements)
if not overloads.empty:
    print("\nOverloaded Resources:")
    print(overloads[['period', 'work_center', 'utilization', 'variance']])

Capacity Planning

Initial Assessment

Capacity Planning

Initial Assessment

Capacity Planning Framework

Types of Capacity Planning

Capacity Strategies

Capacity Analysis Methods

Capacity Measurement

Bottleneck Analysis

Capacity Planning Models

Aggregate Planning (Linear Programming)

Capacity Requirements Planning (CRP)

Things Mac

Trello

Production Scheduling

Jira Integration

Production Scheduling

Cost Aware Llm Pipeline