Retail Expert

Retail Expert

Expert guidance for retail systems, point-of-sale solutions, inventory management, e-commerce platforms, customer analytics, and omnichannel retail strategies.

Core Concepts

Retail Systems

• Point of Sale (POS) systems
• Inventory Management Systems (IMS)
• Customer Relationship Management (CRM)
• Order Management Systems (OMS)
• Warehouse Management Systems (WMS)
• E-commerce platforms
• Payment processing

Omnichannel Retail

• Online-to-offline (O2O) integration
• Buy online, pick up in store (BOPIS)
• Ship from store
• Unified customer profiles
• Cross-channel inventory visibility
• Consistent pricing across channels
• Integrated loyalty programs

Technologies

• Mobile POS (mPOS)
• Self-checkout systems
• Electronic shelf labels (ESL)
• RFID for inventory tracking
• Computer vision for analytics
• AI-powered recommendations
• Contactless payments

Inventory Management

import numpy as np
from datetime import datetime, timedelta

class InventoryManagementSystem:
    """Inventory management and optimization"""

    def __init__(self):
        self.products = {}
        self.warehouses = {}
        self.transfer_orders = []

    def calculate_reorder_point(self,
                               average_daily_demand: float,
                               lead_time_days: int,
                               service_level: float = 0.95) -> dict:
        """Calculate optimal reorder point"""
        # Safety stock calculation
        demand_std_dev = average_daily_demand * 0.2  # Assume 20% variation

        # Z-score for service level
        from scipy import stats
        z_score = stats.norm.ppf(service_level)

        safety_stock = z_score * demand_std_dev * np.sqrt(lead_time_days)
        reorder_point = (average_daily_demand * lead_time_days) + safety_stock

        return {
            'reorder_point': int(np.ceil(reorder_point)),
            'safety_stock': int(np.ceil(safety_stock)),
            'average_daily_demand': average_daily_demand,
            'lead_time_days': lead_time_days,
            'service_level': service_level
        }

    def calculate_economic_order_quantity(self,
                                         annual_demand: float,
                                         ordering_cost: Decimal,
                                         holding_cost_per_unit: Decimal) -> dict:
        """Calculate Economic Order Quantity (EOQ)"""
        eoq = np.sqrt(
            (2 * annual_demand * float(ordering_cost)) /
            float(holding_cost_per_unit)
        )

        # Calculate total annual cost
        number_of_orders = annual_demand / eoq
        ordering_cost_total = number_of_orders * float(ordering_cost)
        holding_cost_total = (eoq / 2) * float(holding_cost_per_unit)
        total_cost = ordering_cost_total + holding_cost_total

        return {
            'eoq': int(np.ceil(eoq)),
            'orders_per_year': number_of_orders,
            'order_frequency_days': int(365 / number_of_orders),
            'total_annual_cost': total_cost,
            'ordering_cost': ordering_cost_total,
            'holding_cost': holding_cost_total
        }

    def analyze_abc(self, products: List[dict]) -> dict:
        """ABC analysis for inventory classification"""
        # Calculate annual value for each product
        for product in products:
            product['annual_value'] = (
                product['unit_cost'] * product['annual_demand']
            )

        # Sort by annual value
        sorted_products = sorted(
            products,
            key=lambda x: x['annual_value'],
            reverse=True
        )

        total_value = sum(p['annual_value'] for p in sorted_products)
        cumulative_value = 0
        results = {'A': [], 'B': [], 'C': []}

        for product in sorted_products:
            cumulative_value += product['annual_value']
            percentage = (cumulative_value / total_value) * 100

            if percentage <= 80:
                category = 'A'  # Top 20% items, 80% value
            elif percentage <= 95:
                category = 'B'  # Next 30% items, 15% value
            else:
                category = 'C'  # Bottom 50% items, 5% value

            product['abc_category'] = category
            results[category].append(product)

        return {
            'classification': results,
            'summary': {
                'A_items': len(results['A']),
                'B_items': len(results['B']),
                'C_items': len(results['C']),
                'total_value': total_value
            }
        }

    def forecast_demand(self,
                       historical_sales: List[float],
                       periods_ahead: int = 12) -> dict:
        """Forecast future demand using exponential smoothing"""
        # Triple exponential smoothing (Holt-Winters)
        alpha = 0.3  # Level smoothing
        beta = 0.1   # Trend smoothing
        gamma = 0.2  # Seasonality smoothing
        season_length = 12  # Monthly seasonality

        n = len(historical_sales)
        forecast = []

        # Initialize level and trend
        level = np.mean(historical_sales[:season_length])
        trend = (np.mean(historical_sales[season_length:2*season_length]) -
                np.mean(historical_sales[:season_length])) / season_length

        # Initialize seasonal indices
        seasonal = np.array(historical_sales[:season_length]) / level

        # Generate forecasts
        for i in range(periods_ahead):
            season_idx = i % season_length
            forecast_value = (level + trend * (i + 1)) * seasonal[season_idx]
            forecast.append(max(0, forecast_value))

        return {
            'forecast': forecast,
            'periods_ahead': periods_ahead,
            'method': 'holt_winters',
            'confidence_interval_95': self._calculate_confidence_interval(
                historical_sales,
                forecast
            )
        }

    def check_stock_levels(self) -> List[dict]:
        """Check stock levels and generate alerts"""
        alerts = []

        for sku, product in self.products.items():
            # Check for low stock
            if product.stock_quantity <= product.reorder_point:
                alerts.append({
                    'type': 'reorder',
                    'severity': 'high',
                    'sku': sku,
                    'product_name': product.name,
                    'current_stock': product.stock_quantity,
                    'reorder_point': product.reorder_point,
                    'action': 'Place purchase order'
                })

            # Check for overstock
            max_stock = product.reorder_point * 3
            if product.stock_quantity > max_stock:
                alerts.append({
                    'type': 'overstock',
                    'severity': 'medium',
                    'sku': sku,
                    'product_name': product.name,
                    'current_stock': product.stock_quantity,
                    'max_stock': max_stock,
                    'action': 'Review purchasing strategy'
                })

            # Check for no sales (dead stock)
            # Implementation would check sales history

        return alerts

    def _calculate_confidence_interval(self,
                                      historical: List[float],
                                      forecast: List[float]) -> dict:
        """Calculate 95% confidence interval for forecast"""
        # Simplified confidence interval
        std_error = np.std(historical) * 1.5
        return {
            'lower': [max(0, f - 1.96 * std_error) for f in forecast],
            'upper': [f + 1.96 * std_error for f in forecast]
        }

Customer Analytics

from sklearn.cluster import KMeans
import pandas as pd

class CustomerAnalytics:
    """Customer segmentation and analytics"""

    def __init__(self):