EOQ = sqrt((2 * D * S) / H)

Where:
  D = Annual demand (units)
  S = Ordering cost per order ($)
  H = Holding cost per unit per year ($)

TC = (D/Q) * S + (Q/2) * H + D * C

Where:
  Q = Order quantity
  C = Unit cost

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

def eoq(annual_demand, order_cost, holding_cost_rate, unit_cost):
    """
    Calculate Economic Order Quantity

    Parameters:
    - annual_demand: Annual demand in units
    - order_cost: Cost per order ($)
    - holding_cost_rate: Holding cost as % of unit cost (e.g., 0.25 for 25%)
    - unit_cost: Cost per unit ($)

    Returns:
    - Dictionary with EOQ, total cost, orders per year, etc.
    """

    # Holding cost per unit per year
    holding_cost = unit_cost * holding_cost_rate

    # EOQ formula
    eoq_qty = np.sqrt((2 * annual_demand * order_cost) / holding_cost)

    # Number of orders per year
    orders_per_year = annual_demand / eoq_qty

    # Total annual cost
    ordering_cost_total = (annual_demand / eoq_qty) * order_cost
    holding_cost_total = (eoq_qty / 2) * holding_cost
    purchase_cost = annual_demand * unit_cost
    total_cost = ordering_cost_total + holding_cost_total + purchase_cost

    # Order interval (days)
    order_interval = 365 / orders_per_year

    return {
        'EOQ': round(eoq_qty, 0),
        'Orders_Per_Year': round(orders_per_year, 1),
        'Order_Interval_Days': round(order_interval, 1),
        'Total_Annual_Cost': round(total_cost, 2),
        'Ordering_Cost': round(ordering_cost_total, 2),
        'Holding_Cost': round(holding_cost_total, 2),
        'Purchase_Cost': round(purchase_cost, 2)
    }

**EOQ with Quantity Discounts:**

```python
def eoq_with_discounts(annual_demand, order_cost, holding_cost_rate, price_breaks):
    """
    EOQ with all-units quantity discount

    price_breaks: list of tuples [(qty, price), ...]
    Example: [(0, 50), (500, 48), (1000, 45)]
    """

    results = []

    for qty_break, unit_price in price_breaks:
        holding_cost = unit_price * holding_cost_rate

        # Calculate EOQ at this price
        eoq_qty = np.sqrt((2 * annual_demand * order_cost) / holding_cost)

        # If EOQ < qty_break, use qty_break as order quantity
        order_qty = max(eoq_qty, qty_break)

        # Total cost at this quantity
        ordering_cost = (annual_demand / order_qty) * order_cost
        holding_cost_total = (order_qty / 2) * holding_cost
        purchase_cost = annual_demand * unit_price
        total_cost = ordering_cost + holding_cost_total + purchase_cost

        results.append({
            'Quantity_Break': qty_break,
            'Unit_Price': unit_price,
            'EOQ_at_Price': round(eoq_qty, 0),
            'Order_Quantity': round(order_qty, 0),
            'Total_Cost': round(total_cost, 2)
        })

    # Find minimum cost option
    results_df = pd.DataFrame(results)
    optimal = results_df.loc[results_df['Total_Cost'].idxmin()]

    return results_df, optimal

---

from scipy import stats
import numpy as np

def safety_stock(demand_std, lead_time_days, service_level=0.95):
    """
    Calculate safety stock based on service level

    Parameters:
    - demand_std: Standard deviation of daily demand
    - lead_time_days: Lead time in days
    - service_level: Target service level (e.g., 0.95 for 95%)

    Returns:
    - Safety stock quantity
    """

    # Z-score for desired service level
    z = stats.norm.ppf(service_level)

    # Safety stock = Z * std_dev during lead time
    # Assuming demand is independent across days
    std_during_lt = demand_std * np.sqrt(lead_time_days)

    ss = z * std_during_lt

    return round(ss, 0)

def safety_stock_variable_lt(demand_avg, demand_std, lead_time_avg,
                              lead_time_std, service_level=0.95):
    """
    Safety stock with variable demand AND lead time

    More comprehensive formula accounting for both sources of variability
    """

    z = stats.norm.ppf(service_level)

    # Combined variance formula
    variance = (lead_time_avg * demand_std**2 +
                demand_avg**2 * lead_time_std**2)

    std_during_lt = np.sqrt(variance)

    ss = z * std_during_lt

    return round(ss, 0)

undefined

ROP = (Average Daily Demand × Lead Time) + Safety Stock

def reorder_point(demand_avg_daily, lead_time_days, safety_stock):
    """
    Calculate reorder point

    ROP = Expected demand during lead time + Safety stock
    """

    expected_demand_lt = demand_avg_daily * lead_time_days
    rop = expected_demand_lt + safety_stock

    return round(rop, 0)

def rop_with_review_period(demand_avg_daily, lead_time_days,
                           review_period_days, safety_stock):
    """
    Reorder point with periodic review

    Must cover lead time + review period
    """

    total_period = lead_time_days + review_period_days
    expected_demand = demand_avg_daily * total_period
    rop = expected_demand + safety_stock

    return round(rop, 0)

---

class ContinuousReviewPolicy:
    """(s, Q) continuous review inventory policy"""

    def __init__(self, reorder_point, order_quantity):
        self.s = reorder_point
        self.Q = order_quantity
        self.inventory_position = 0
        self.on_hand = 0
        self.on_order = 0
        self.orders_placed = []

    def check_and_order(self, current_on_hand, current_on_order):
        """Check if order should be placed"""

        self.on_hand = current_on_hand
        self.on_order = current_on_order
        self.inventory_position = current_on_hand + current_on_order

        if self.inventory_position <= self.s:
            # Place order
            order = {
                'quantity': self.Q,
                'inventory_position': self.inventory_position,
                'on_hand': self.on_hand,
                'on_order': self.on_order
            }
            self.orders_placed.append(order)
            return True, self.Q

        return False, 0

    def simulate(self, demand_series, lead_time=14):
        """Simulate inventory policy over time"""

        inventory = []
        current_inv = self.Q  # Start with one order quantity
        on_order_queue = []

        for day, demand in enumerate(demand_series):
            # Receive orders (if any arrive today)
            arrivals = [o for o in on_order_queue if o['arrival_day'] == day]
            for arrival in arrivals:
                current_inv += arrival['quantity']
                on_order_queue.remove(arrival)

            # Satisfy demand
            current_inv = max(0, current_inv - demand)

            # Check if order needed
            current_on_order = sum(o['quantity'] for o in on_order_queue)
            should_order, order_qty = self.check_and_order(current_inv, current_on_order)

            if should_order:
                on_order_queue.append({
                    'quantity': order_qty,
                    'order_day': day,
                    'arrival_day': day + lead_time
                })

            inventory.append({
                'day': day,
                'on_hand': current_inv,
                'demand': demand,
                'on_order': current_on_order,
                'order_placed': should_order
            })

        return pd.DataFrame(inventory)

undefined

class PeriodicReviewPolicy:
    """(R, S) periodic review inventory policy"""

    def __init__(self, review_period, order_up_to_level):
        self.R = review_period
        self.S = order_up_to_level

    def calculate_order_qty(self, current_position):
        """Calculate order quantity to reach S"""
        return max(0, self.S - current_position)

    def simulate(self, demand_series, lead_time=14):
        """Simulate periodic review policy"""

        inventory = []
        current_inv = self.S  # Start at order-up-to level
        on_order_queue = []

        for day, demand in enumerate(demand_series):
            # Receive orders
            arrivals = [o for o in on_order_queue if o['arrival_day'] == day]
            for arrival in arrivals:
                current_inv += arrival['quantity']
                on_order_queue.remove(arrival)

            # Satisfy demand
            current_inv = max(0, current_inv - demand)

            # Check if it's a review day
            should_order = (day % self.R == 0)
            order_qty = 0

            if should_order:
                current_on_order = sum(o['quantity'] for o in on_order_queue)
                current_position = current_inv + current_on_order
                order_qty = self.calculate_order_qty(current_position)

                if order_qty > 0:
                    on_order_queue.append({
                        'quantity': order_qty,
                        'order_day': day,
                        'arrival_day': day + lead_time
                    })

            inventory.append({
                'day': day,
                'on_hand': current_inv,
                'demand': demand,
                'order_qty': order_qty,
                'review_day': should_order
            })

        return pd.DataFrame(inventory)

---

def abc_analysis(df, sku_col='sku', demand_col='annual_demand',
                 price_col='unit_price'):
    """
    Perform ABC analysis on inventory

    Parameters:
    - df: DataFrame with SKU, demand, and price
    - Returns: DataFrame with ABC classification
    """

    # Calculate annual value
    df = df.copy()
    df['annual_value'] = df[demand_col] * df[price_col]

    # Sort by value descending
    df = df.sort_values('annual_value', ascending=False)

    # Calculate cumulative percentage
    total_value = df['annual_value'].sum()
    df['cumulative_value'] = df['annual_value'].cumsum()
    df['cumulative_pct'] = df['cumulative_value'] / total_value * 100

    # Classify
    def classify(pct):
        if pct <= 80:
            return 'A'
        elif pct <= 95:
            return 'B'
        else:
            return 'C'

    df['abc_class'] = df['cumulative_pct'].apply(classify)

    # Add ranking
    df['rank'] = range(1, len(df) + 1)

    return df

undefined

def xyz_analysis(df, demand_history_col='demand_history'):
    """
    Classify items by demand variability

    demand_history_col should contain list/array of historical demand
    """

    def classify_variability(demands):
        if len(demands) < 2:
            return 'Z'  # Insufficient data

        mean_demand = np.mean(demands)
        std_demand = np.std(demands)

        if mean_demand == 0:
            return 'Z'

        cv = std_demand / mean_demand  # Coefficient of variation

        if cv < 0.5:
            return 'X'
        elif cv < 1.0:
            return 'Y'
        else:
            return 'Z'

    df = df.copy()
    df['xyz_class'] = df[demand_history_col].apply(classify_variability)

    return df

matrix = pd.crosstab(df['abc_class'], df['xyz_class'],
                     values=df['sku'], aggfunc='count')

return matrix

undefined

def service_level_analysis(demand_std, lead_time, unit_cost,
                           holding_cost_rate, stockout_cost):
    """
    Analyze optimal service level balancing holding vs. stockout costs
    """

    service_levels = np.arange(0.80, 0.995, 0.01)
    results = []

    holding_cost_per_unit = unit_cost * holding_cost_rate

    for sl in service_levels:
        # Safety stock required
        z = stats.norm.ppf(sl)
        std_during_lt = demand_std * np.sqrt(lead_time)
        ss = z * std_during_lt

        # Holding cost
        holding_cost = ss * holding_cost_per_unit

        # Expected stockout cost
        # Simplified: (1 - service_level) * stockout_cost
        expected_stockouts = (1 - sl) * stockout_cost

        total_cost = holding_cost + expected_stockouts

        results.append({
            'service_level': sl,
            'safety_stock': round(ss, 0),
            'holding_cost': round(holding_cost, 2),
            'stockout_cost': round(expected_stockouts, 2),
            'total_cost': round(total_cost, 2)
        })

    results_df = pd.DataFrame(results)

    # Find optimal
    optimal_idx = results_df['total_cost'].idxmin()
    optimal = results_df.iloc[optimal_idx]

    return results_df, optimal

undefined

Inventory Turns = Cost of Goods Sold (COGS) / Average Inventory Value

def inventory_metrics(annual_cogs, avg_inventory_value, target_turns=None):
    """
    Calculate inventory turnover metrics
    """

    turns = annual_cogs / avg_inventory_value
    days_on_hand = 365 / turns

    metrics = {
        'Inventory_Turns': round(turns, 2),
        'Days_On_Hand': round(days_on_hand, 1),
        'Avg_Inventory': avg_inventory_value
    }

    if target_turns:
        target_inventory = annual_cogs / target_turns
        reduction = avg_inventory_value - target_inventory
        metrics['Target_Inventory'] = round(target_inventory, 0)
        metrics['Inventory_Reduction'] = round(reduction, 0)
        metrics['Cash_Freed'] = round(reduction, 0)

    return metrics

undefined

from scipy.optimize import minimize

def optimize_multi_sku_inventory(skus_data, total_budget, target_service_level=0.95):
    """
    Optimize inventory allocation across multiple SKUs with budget constraint

    skus_data: DataFrame with columns ['sku', 'demand_avg', 'demand_std',
                                        'lead_time', 'unit_cost', 'stockout_cost']
    """

    n_skus = len(skus_data)

    def objective(safety_stocks):
        """Minimize total cost (holding + expected stockouts)"""

        total_cost = 0

        for i, ss in enumerate(safety_stocks):
            row = skus_data.iloc[i]

            # Holding cost
            holding_cost = ss * row['unit_cost'] * 0.25  # 25% holding rate

            # Calculate achieved service level from safety stock
            z = ss / (row['demand_std'] * np.sqrt(row['lead_time']))
            service_level = stats.norm.cdf(z)

            # Stockout cost
            stockout_prob = 1 - service_level
            expected_stockout = stockout_prob * row['stockout_cost']

            total_cost += holding_cost + expected_stockout

        return total_cost

    def budget_constraint(safety_stocks):
        """Total inventory value must not exceed budget"""
        total_value = sum(
            ss * skus_data.iloc[i]['unit_cost']
            for i, ss in enumerate(safety_stocks)
        )
        return total_budget - total_value

    # Initial guess: equal allocation
    x0 = np.full(n_skus, total_budget / (n_skus * skus_data['unit_cost'].mean()))

    # Constraints
    constraints = [
        {'type': 'ineq', 'fun': budget_constraint}
    ]

    # Bounds: non-negative safety stock
    bounds = [(0, None) for _ in range(n_skus)]

    # Optimize
    result = minimize(objective, x0, method='SLSQP',
                     bounds=bounds, constraints=constraints)

    # Extract results
    optimal_ss = result.x

    results_df = skus_data.copy()
    results_df['optimal_safety_stock'] = optimal_ss
    results_df['inventory_value'] = optimal_ss * results_df['unit_cost']

    return results_df, result

---