import numpy as np
import pandas as pd

def calculate_storage_space(sku_data, peak_factor=1.5, utilization_target=0.85):
    """
    Calculate required warehouse storage space

    Parameters:
    - sku_data: DataFrame with columns ['sku', 'avg_inventory', 'pallet_positions']
    - peak_factor: Peak inventory as multiple of average (e.g., 1.5 = 50% above avg)
    - utilization_target: Target space utilization (0.85 = 85%)

    Returns:
    - Required pallet positions
    """

    # Calculate peak inventory
    sku_data = sku_data.copy()
    sku_data['peak_inventory'] = sku_data['avg_inventory'] * peak_factor

    # Total pallet positions needed
    total_positions = sku_data['pallet_positions'].sum()

    # Adjust for utilization target (need more positions than peak to maintain flow)
    required_positions = total_positions / utilization_target

    return {
        'avg_pallet_positions': sku_data['pallet_positions'].sum(),
        'peak_pallet_positions': total_positions,
        'required_positions': round(required_positions, 0),
        'utilization_target': utilization_target
    }

# Example
sku_data = pd.DataFrame({
    'sku': [f'SKU_{i}' for i in range(1, 101)],
    'avg_inventory': np.random.randint(10, 500, 100),
    'pallet_positions': np.random.randint(1, 50, 100)
})

storage_req = calculate_storage_space(sku_data, peak_factor=1.5, utilization_target=0.85)
print(f"Required pallet positions: {storage_req['required_positions']}")

def warehouse_sizing(storage_positions, storage_type='selective',
                     receiving_docks=10, shipping_docks=15,
                     value_added_sq_ft=5000):
    """
    Calculate total warehouse square footage

    storage_type: 'selective', 'drive-in', 'push-back', 'pallet-flow'
    """

    # Square feet per pallet position by storage type
    sq_ft_per_position = {
        'selective': 30,      # Single-deep racking, most accessible
        'double-deep': 22,    # Double-deep, less accessible
        'drive-in': 18,       # High density, LIFO
        'push-back': 20,      # High density, LIFO
        'pallet-flow': 25,    # FIFO, dynamic
        'floor-stacked': 15   # Very dense, limited access
    }

    storage_sq_ft = storage_positions * sq_ft_per_position.get(storage_type, 30)

    # Receiving area (assume 2000 sq ft per dock door)
    receiving_sq_ft = receiving_docks * 2000

    # Shipping area (assume 1500 sq ft per dock door)
    shipping_sq_ft = shipping_docks * 1500

    # Aisles and circulation (20-30% of storage)
    circulation_sq_ft = storage_sq_ft * 0.25

    # Office, break rooms, restrooms (5-10% of total)
    support_sq_ft = (storage_sq_ft + receiving_sq_ft + shipping_sq_ft) * 0.08

    # Total
    total_sq_ft = (storage_sq_ft +
                   receiving_sq_ft +
                   shipping_sq_ft +
                   circulation_sq_ft +
                   value_added_sq_ft +
                   support_sq_ft)

    breakdown = {
        'Storage': round(storage_sq_ft, 0),
        'Receiving': round(receiving_sq_ft, 0),
        'Shipping': round(shipping_sq_ft, 0),
        'Circulation': round(circulation_sq_ft, 0),
        'Value_Added': value_added_sq_ft,
        'Support': round(support_sq_ft, 0),
        'Total': round(total_sq_ft, 0)
    }

    return breakdown

# Example
sizing = warehouse_sizing(
    storage_positions=5000,
    storage_type='selective',
    receiving_docks=10,
    shipping_docks=15,
    value_added_sq_ft=5000
)

print("Warehouse Space Breakdown:")
for area, sq_ft in sizing.items():
    print(f"  {area}: {sq_ft:,.0f} sq ft")

print(f"\nTotal warehouse size: {sizing['Total']:,.0f} sq ft")

def throughput_capacity(storage_positions, picks_per_day, orders_per_day,
                       lines_per_order=5, pick_rate_per_hour=100):
    """
    Analyze warehouse throughput capacity

    Parameters:
    - storage_positions: Total pallet positions
    - picks_per_day: Daily pick volume (lines)
    - orders_per_day: Daily order volume
    - lines_per_order: Average lines per order
    - pick_rate_per_hour: Picks per person per hour

    Returns:
    - Capacity analysis and labor requirements
    """

    # Labor calculations
    picks_per_day = orders_per_day * lines_per_order
    hours_per_day = 16  # Assume 2-shift operation
    pickers_needed = picks_per_day / (pick_rate_per_hour * hours_per_day)

    # Receiving capacity (pallets per dock per day)
    pallets_per_dock_day = 80  # Industry average
    receiving_capacity = pallets_per_dock_day  # per dock

    # Shipping capacity (orders per dock per day)
    orders_per_dock_day = 100  # Depends on order size
    shipping_capacity = orders_per_dock_day  # per dock

    return {
        'picks_per_day': picks_per_day,
        'pickers_needed': round(pickers_needed, 1),
        'receiving_pallets_per_dock': pallets_per_dock_day,
        'shipping_orders_per_dock': orders_per_dock_day
    }

# Example
capacity = throughput_capacity(
    storage_positions=5000,
    picks_per_day=0,  # Will calculate
    orders_per_day=2000,
    lines_per_order=5,
    pick_rate_per_hour=100
)

print(f"Daily picks: {capacity['picks_per_day']}")
print(f"Pickers needed: {capacity['pickers_needed']}")

def design_functional_zones(total_sq_ft, order_profile='mixed'):
    """
    Allocate space to functional zones

    order_profile: 'pallet', 'case', 'each', 'mixed'
    """

    # Space allocation percentages by order profile
    allocations = {
        'pallet': {
            'Reserve_Storage': 0.50,
            'Forward_Pick': 0.10,
            'Receiving': 0.12,
            'Shipping': 0.15,
            'Value_Added': 0.03,
            'Support': 0.10
        },
        'each': {
            'Reserve_Storage': 0.35,
            'Forward_Pick': 0.25,
            'Receiving': 0.10,
            'Shipping': 0.15,
            'Value_Added': 0.05,
            'Support': 0.10
        },
        'mixed': {
            'Reserve_Storage': 0.40,
            'Forward_Pick': 0.20,
            'Receiving': 0.12,
            'Shipping': 0.15,
            'Value_Added': 0.05,
            'Support': 0.08
        }
    }

    profile_alloc = allocations.get(order_profile, allocations['mixed'])

    zones = {
        zone: round(total_sq_ft * pct, 0)
        for zone, pct in profile_alloc.items()
    }

    return zones

# Example
zones = design_functional_zones(200000, order_profile='mixed')
print("Functional Zone Allocation:")
for zone, sq_ft in zones.items():
    print(f"  {zone}: {sq_ft:,.0f} sq ft ({sq_ft/sum(zones.values())*100:.1f}%)")

def select_storage_system(sku_velocity, sku_diversity, fifo_required=False,
                         space_constraint=False):
    """
    Recommend storage system based on operational requirements

    Parameters:
    - sku_velocity: 'fast', 'medium', 'slow'
    - sku_diversity: 'high' (>1000 SKUs), 'medium' (100-1000), 'low' (<100)
    - fifo_required: True if FIFO needed (perishables, date-coded)
    - space_constraint: True if space is at premium
    """

    recommendations = []

    if sku_diversity == 'high':
        if sku_velocity == 'fast':
            recommendations.append({
                'system': 'Selective Racking',
                'reason': 'High SKU count requires full selectivity',
                'priority': 1
            })
            if space_constraint:
                recommendations.append({
                    'system': 'Narrow Aisle (VNA)',
                    'reason': 'Increases density for high SKU count',
                    'priority': 2
                })
        else:
            recommendations.append({
                'system': 'Selective Racking',
                'reason': 'Full selectivity for diverse SKU mix',
                'priority': 1
            })

    elif sku_diversity == 'medium':
        if sku_velocity == 'fast':
            recommendations.append({
                'system': 'Pallet Flow Rack',
                'reason': 'FIFO, high velocity, moderate SKU count',
                'priority': 1 if fifo_required else 2
            })
            recommendations.append({
                'system': 'Push-Back Rack',
                'reason': 'Dense storage, good throughput',
                'priority': 2 if fifo_required else 1
            })
        else:
            recommendations.append({
                'system': 'Double-Deep',
                'reason': 'Good density with reasonable selectivity',
                'priority': 1
            })

    else:  # low SKU diversity
        if space_constraint:
            recommendations.append({
                'system': 'Drive-In Racking',
                'reason': 'Maximum density for low SKU count',
                'priority': 1 if not fifo_required else 2
            })
            if fifo_required:
                recommendations.append({
                    'system': 'Drive-Through Racking',
                    'reason': 'Dense FIFO for low SKU count',
                    'priority': 1
                })

    # Sort by priority
    recommendations.sort(key=lambda x: x['priority'])

    return recommendations

# Example
recs = select_storage_system(
    sku_velocity='fast',
    sku_diversity='high',
    fifo_required=True,
    space_constraint=True
)

print("Storage System Recommendations:")
for rec in recs:
    print(f"  {rec['priority']}. {rec['system']}: {rec['reason']}")

def rack_configuration(clear_height_ft, load_height_ft=5, load_depth_ft=4,
                      aisle_width_ft=12, rack_depth='single'):
    """
    Calculate rack configuration and capacity

    Parameters:
    - clear_height_ft: Building clear height
    - load_height_ft: Height per pallet level
    - load_depth_ft: Depth per pallet position
    - aisle_width_ft: Aisle width for equipment
    - rack_depth: 'single', 'double', 'back-to-back'
    """

    # Calculate number of levels (leave clearance for sprinklers/lights)
    usable_height = clear_height_ft - 4  # 4 ft clearance
    levels = int(usable_height / load_height_ft)

    # Rack bay width (typically 96-108 inches for 2 pallets side-by-side)
    bay_width_ft = 9  # 2 x 42" pallets + structure

    # Calculate positions per bay
    if rack_depth == 'single':
        positions_per_bay = 2  # 2 pallets wide
        depth_ft = load_depth_ft + 2  # Structure
    elif rack_depth == 'double':
        positions_per_bay = 4  # 2 deep x 2 wide
        depth_ft = (load_depth_ft * 2) + 2
    elif rack_depth == 'back-to-back':
        positions_per_bay = 4  # 2 racks back to back, 2 wide each
        depth_ft = (load_depth_ft * 2) + 3  # Shared structure

    # Positions per bay
    positions_per_bay_total = positions_per_bay * levels

    # Linear feet required per bay (includes aisle)
    linear_ft_per_bay = bay_width_ft

    # Calculate density (positions per 1000 sq ft)
    sq_ft_per_bay = bay_width_ft * (depth_ft + aisle_width_ft)
    positions_per_1000_sqft = (positions_per_bay_total / sq_ft_per_bay) * 1000

    return {
        'levels': levels,
        'positions_per_bay': positions_per_bay_total,
        'bay_width_ft': bay_width_ft,
        'depth_ft': depth_ft,
        'sq_ft_per_bay': round(sq_ft_per_bay, 1),
        'positions_per_1000_sqft': round(positions_per_1000_sqft, 1)
    }

# Example
config = rack_configuration(
    clear_height_ft=32,
    load_height_ft=5,
    aisle_width_ft=12,
    rack_depth='single'
)

print(f"Rack levels: {config['levels']}")
print(f"Positions per bay: {config['positions_per_bay']}")
print(f"Density: {config['positions_per_1000_sqft']} positions per 1000 sq ft")

def select_material_handling_equipment(storage_height_ft, aisle_budget='medium',
                                       throughput_level='medium',
                                       automation_interest=False):
    """
    Recommend material handling equipment

    Parameters:
    - storage_height_ft: How high storage needs to go
    - aisle_budget: 'tight' (<8 ft), 'medium' (8-12 ft), 'wide' (>12 ft)
    - throughput_level: 'low', 'medium', 'high'
    - automation_interest: Considering automation?
    """

    recommendations = []

    if storage_height_ft <= 15:
        recommendations.append({
            'equipment': 'Counterbalance Forklift',
            'aisle_width': '12-13 ft',
            'cost': '$25K-$40K',
            'reason': 'Low height, versatile, lowest cost'
        })

    elif storage_height_ft <= 30:
        if aisle_budget == 'tight':
            recommendations.append({
                'equipment': 'Very Narrow Aisle (VNA)',
                'aisle_width': '5-6.5 ft',
                'cost': '$60K-$100K',
                'reason': 'Maximum space utilization, tight aisles'
            })
        else:
            recommendations.append({
                'equipment': 'Reach Truck',
                'aisle_width': '8-10 ft',
                'cost': '$35K-$50K',
                'reason': 'Good balance of cost and density'
            })

    else:  # > 30 ft
        recommendations.append({
            'equipment': 'VNA or Automated AS/RS',
            'aisle_width': '5-6.5 ft or N/A',
            'cost': '$60K+ or $2M+ system',
            'reason': 'Required for very high storage'
        })

    if automation_interest and throughput_level == 'high':
        recommendations.append({
            'equipment': 'Automated Storage/Retrieval (AS/RS)',
            'aisle_width': 'N/A',
            'cost': '$2M-$10M+ system',
            'reason': 'High throughput, labor savings, accuracy'
        })

    return recommendations

# Example
equipment = select_material_handling_equipment(
    storage_height_ft=28,
    aisle_budget='medium',
    throughput_level='medium',
    automation_interest=False
)

print("Material Handling Equipment Recommendations:")
for eq in equipment:
    print(f"\n  {eq['equipment']}")
    print(f"    Aisle width: {eq['aisle_width']}")
    print(f"    Cost: {eq['cost']}")
    print(f"    Reason: {eq['reason']}")

def calculate_dock_doors(inbound_trucks_per_day, outbound_trucks_per_day,
                        hours_of_operation=10, dwell_time_hours=2,
                        utilization_target=0.80):
    """
    Calculate required dock doors

    Parameters:
    - inbound_trucks_per_day: Daily truck arrivals
    - outbound_trucks_per_day: Daily truck departures
    - hours_of_operation: Working hours per day
    - dwell_time_hours: Hours per truck at dock (unload/load time)
    - utilization_target: Target dock utilization (0.80 = 80%)
    """

    # Capacity per dock door per day
    door_capacity = hours_of_operation / dwell_time_hours

    # Receiving doors needed
    receiving_doors_raw = inbound_trucks_per_day / door_capacity
    receiving_doors = receiving_doors_raw / utilization_target

    # Shipping doors needed
    shipping_doors_raw = outbound_trucks_per_day / door_capacity
    shipping_doors = shipping_doors_raw / utilization_target

    return {
        'receiving_doors': int(np.ceil(receiving_doors)),
        'shipping_doors': int(np.ceil(shipping_doors)),
        'total_doors': int(np.ceil(receiving_doors + shipping_doors)),
        'receiving_utilization': receiving_doors_raw / np.ceil(receiving_doors),
        'shipping_utilization': shipping_doors_raw / np.ceil(shipping_doors)
    }

# Example
doors = calculate_dock_doors(
    inbound_trucks_per_day=50,
    outbound_trucks_per_day=70,
    hours_of_operation=16,  # 2 shifts
    dwell_time_hours=2,
    utilization_target=0.80
)

print(f"Receiving doors needed: {doors['receiving_doors']}")
print(f"Shipping doors needed: {doors['shipping_doors']}")
print(f"Total dock doors: {doors['total_doors']}")
print(f"Receiving utilization: {doors['receiving_utilization']:.1%}")
print(f"Shipping utilization: {doors['shipping_utilization']:.1%}")

def calculate_pick_travel_distance(orders_per_day, lines_per_order,
                                  avg_travel_per_pick_ft=150,
                                  picker_speed_ft_per_min=200):
    """
    Calculate picker travel distance and time

    Parameters:
    - orders_per_day: Daily order volume
    - lines_per_order: Average order lines
    - avg_travel_per_pick_ft: Average travel distance per pick
    - picker_speed_ft_per_min: Walking/driving speed
    """

    picks_per_day = orders_per_day * lines_per_order

    # Total travel distance
    total_travel_ft = picks_per_day * avg_travel_per_pick_ft
    total_travel_miles = total_travel_ft / 5280

    # Travel time
    travel_time_min = total_travel_ft / picker_speed_ft_per_min
    travel_time_hours = travel_time_min / 60

    # If picks per hour = 100, calculate labor needed
    pick_time_hours = picks_per_day / 100

    total_labor_hours = pick_time_hours + travel_time_hours

    return {
        'picks_per_day': picks_per_day,
        'total_travel_miles': round(total_travel_miles, 1),
        'travel_time_hours': round(travel_time_hours, 1),
        'pick_time_hours': round(pick_time_hours, 1),
        'total_labor_hours': round(total_labor_hours, 1),
        'pickers_needed_single_shift': round(total_labor_hours / 8, 1)
    }

# Example
travel = calculate_pick_travel_distance(
    orders_per_day=2000,
    lines_per_order=5,
    avg_travel_per_pick_ft=150,
    picker_speed_ft_per_min=200
)

print(f"Daily picks: {travel['picks_per_day']}")
print(f"Daily travel: {travel['total_travel_miles']} miles")
print(f"Travel time: {travel['travel_time_hours']} hours")
print(f"Pickers needed: {travel['pickers_needed_single_shift']}")

def automation_roi_analysis(current_labor_cost, automation_cost,
                           labor_savings_pct, maintenance_cost_annual,
                           years=5):
    """
    Calculate ROI for warehouse automation

    Parameters:
    - current_labor_cost: Annual labor cost ($)
    - automation_cost: Upfront automation investment ($)
    - labor_savings_pct: Labor reduction (e.g., 0.40 for 40%)
    - maintenance_cost_annual: Annual maintenance ($)
    - years: Analysis period
    """

    annual_savings = current_labor_cost * labor_savings_pct
    annual_net_savings = annual_savings - maintenance_cost_annual

    # Simple payback period
    payback_years = automation_cost / annual_net_savings

    # NPV calculation (assume 10% discount rate)
    discount_rate = 0.10
    npv = -automation_cost
    for year in range(1, years + 1):
        npv += annual_net_savings / ((1 + discount_rate) ** year)

    # ROI
    total_savings = annual_net_savings * years
    roi = (total_savings - automation_cost) / automation_cost

    return {
        'automation_cost': automation_cost,
        'annual_labor_savings': round(annual_savings, 0),
        'annual_maintenance': maintenance_cost_annual,
        'annual_net_savings': round(annual_net_savings, 0),
        'payback_years': round(payback_years, 2),
        'npv_5_year': round(npv, 0),
        'roi_5_year': round(roi, 2)
    }

# Example
roi = automation_roi_analysis(
    current_labor_cost=2_000_000,   # $2M annual labor
    automation_cost=4_000_000,      # $4M automation investment
    labor_savings_pct=0.50,         # 50% labor reduction
    maintenance_cost_annual=200_000, # $200K annual maintenance
    years=5
)

print(f"Automation cost: ${roi['automation_cost']:,.0f}")
print(f"Annual labor savings: ${roi['annual_labor_savings']:,.0f}")
print(f"Annual net savings: ${roi['annual_net_savings']:,.0f}")
print(f"Payback period: {roi['payback_years']} years")
print(f"5-year NPV: ${roi['npv_5_year']:,.0f}")
print(f"5-year ROI: {roi['roi_5_year']:.1%}")

def warehouse_kpis(total_sq_ft, storage_sq_ft, occupied_positions,
                   total_positions, throughput_units, labor_hours,
                   orders_shipped, order_accuracy_pct):
    """
    Calculate warehouse performance KPIs
    """

    # Space utilization
    space_utilization = occupied_positions / total_positions

    # Inventory density
    inventory_density = occupied_positions / (storage_sq_ft / 1000)

    # Productivity
    units_per_labor_hour = throughput_units / labor_hours

    # Order accuracy
    order_accuracy = order_accuracy_pct / 100

    # Operating cost per unit (example)
    # Would need cost inputs for full calculation

    kpis = {
        'Space_Utilization_%': round(space_utilization * 100, 1),
        'Inventory_Density_per_1000sqft': round(inventory_density, 1),
        'Units_per_Labor_Hour': round(units_per_labor_hour, 1),
        'Order_Accuracy_%': round(order_accuracy * 100, 2),
        'Orders_per_Labor_Hour': round(orders_shipped / labor_hours, 1)
    }

    return kpis

# Example
kpis = warehouse_kpis(
    total_sq_ft=200000,
    storage_sq_ft=120000,
    occupied_positions=4200,
    total_positions=5000,
    throughput_units=50000,
    labor_hours=500,
    orders_shipped=2000,
    order_accuracy_pct=99.5
)

print("Warehouse KPIs:")
for metric, value in kpis.items():
    print(f"  {metric}: {value}")