📦 供应链数据分析在线学习

# 供应链节点模拟 supply_chain = { "供应商": ["原材料A", "原材料B"], "制造商": ["产品X", "产品Y"], "分销商": ["区域1", "区域2"], "零售商": ["门店A", "门店B"] } print("供应链结构：") print("="*40) for node, items in supply_chain.items(): print(f"{node}: {', '.join(items)}") # 计算供应链长度 chain_length = len(supply_chain) print(f"\n供应链层级数：{chain_length}")

# 需求预测 - 移动平均法 sales_history = [120, 135, 128, 142, 155, 148] print("历史销售数据：", sales_history) # 3期移动平均 n = 3 ma_forecast = sum(sales_history[-n:]) / n print(f"3期移动平均预测：{ma_forecast:.1f}") # 简单指数平滑（alpha=0.3） alpha = 0.3 es_forecast = sales_history[0] for actual in sales_history[1:]: es_forecast = alpha * actual + (1 - alpha) * es_forecast print(f"指数平滑预测：{es_forecast:.1f}") # 预测误差计算 actual = 150 error = actual - ma_forecast print(f"\n预测误差：{error:.1f}")

# 库存管理计算 import math # 基本参数 annual_demand = 1200 # 年需求量 order_cost = 100 # 每次订货成本 holding_cost = 5 # 单位年持有成本 lead_time = 7 # 提前期（天） daily_demand = annual_demand / 365 # 计算EOQ eoq = math.sqrt((2 * annual_demand * order_cost) / holding_cost) # 计算再订货点 reorder_point = daily_demand * lead_time # 计算安全库存（假设需求标准差为10） std_demand = 10 service_level = 0.95 z_score = 1.645 # 95%服务水平对应的Z值 safety_stock = z_score * std_demand * math.sqrt(lead_time) print("库存管理分析：") print(="*40) print(f"经济订货批量(EOQ)：{eoq:.0f}件") print(f"再订货点：{reorder_point:.0f}件") print(f"安全库存：{safety_stock:.0f}件") print(f"建议最高库存：{eoq + safety_stock:.0f}件")

# 物流成本计算与优化 # 配送路线数据 routes = [ {"路线": "A", "距离": 100, "重量": 500, "成本系数": 0.5}, {"路线": "B", "距离": 150, "重量": 800, "成本系数": 0.5}, {"路线": "C", "距离": 80, "重量": 300, "成本系数": 0.5} ] print("物流成本分析：") print(="*50) total_cost = 0 for route in routes: cost = route["距离"] * route["重量"] * route["成本系数"] / 1000 total_cost += cost print(f"路线{route['路线']}: {route['距离']}km, {route['重量']}kg, 成本¥{cost:.2f}") print(="*50) print(f"总运输成本：¥{total_cost:.2f}") # 装载率计算 vehicle_capacity = 1000 # 车辆载重kg total_weight = sum(r["重量"] for r in routes) load_rate = (total_weight / vehicle_capacity) * 100 print(f"\n车辆装载率：{load_rate:.1f}%") print(f"装载状态：{'合理' if load_rate <= 100 else '超载'}")

# 供应商评估 - 加权评分法 suppliers = [ {"名称": "供应商A", "质量": 90, "价格": 85, "交货": 80, "服务": 88}, {"名称": "供应商B", "质量": 85, "价格": 95, "交货": 85, "服务": 82}, {"名称": "供应商C", "质量": 88, "价格": 80, "交货": 90, "服务": 85} ] # 权重设置 weights = {"质量": 0.4, "价格": 0.3, "交货": 0.2, "服务": 0.1} print("供应商评估结果：") print(="*50) print(f"{'供应商':<10} {'质量':<8} {'价格':<8} {'交货':<8} {'服务':<8} {'综合得分':<10}") print(-"*50) scores = [] for s in suppliers: score = (s["质量"] * weights["质量"] + s["价格"] * weights["价格"] + s["交货"] * weights["交货"] + s["服务"] * weights["服务"]) scores.append((s["名称"], score, s)) print(f"{s['名称']:<10} {s['质量']:<8} {s['价格']:<8} {s['交货']:<8} {s['服务']:<8} {score:.2f}") # 排序并选择最佳供应商 scores.sort(key=lambda x: x[1], reverse=True) print(="*50) print(f"\n推荐供应商：{scores[0][0]} (得分: {scores[0][1]:.2f})")

# 供应链成本分析 costs = { "采购成本": 500000, "库存成本": 120000, "运输成本": 80000, "管理成本": 50000, "其他成本": 30000 } print("供应链成本分析：") print(="*50) total_cost = sum(costs.values()) for category, cost in costs.items(): percentage = (cost / total_cost) * 100 bar = "█" * int(percentage / 5) print(f"{category:<10}: ¥{cost:>8,} ({percentage:>5.1f}%) {bar}") print(="*50) print(f"总成本：¥{total_cost:,}") # 成本优化建议 print("\n成本优化建议：") max_cost_category = max(costs, key=costs.get) print(f"1. {max_cost_category}占比最高({(costs[max_cost_category]/total_cost*100):.1f}%)，建议优先优化") print(f"2. 库存成本可通过提高周转率降低") print(f"3. 运输成本可通过路线优化和装载率提升来降低")

# 供应链KPI计算 kpi_data = { "总订单数": 1000, "满足订单数": 950, "准时交付数": 920, "销售成本": 5000000, "平均库存": 500000, "供应链周期(天)": 15 } print("供应链KPI评估：") print(="*50) # 计算各项指标 order_fulfillment = (kpi_data["满足订单数"] / kpi_data["总订单数"]) * 100 on_time_delivery = (kpi_data["准时交付数"] / kpi_data["总订单数"]) * 100 inventory_turnover = kpi_data["销售成本"] / kpi_data["平均库存"] print(f"订单满足率：{order_fulfillment:.1f}% {'✓ 优秀' if order_fulfillment >= 95 else '⚠ 需改进'}") print(f"准时交付率：{on_time_delivery:.1f}% {'✓ 优秀' if on_time_delivery >= 90 else '⚠ 需改进'}") print(f"库存周转率：{inventory_turnover:.1f}次/年 {'✓ 优秀' if inventory_turnover >= 8 else '⚠ 需改进'}") print(f"供应链周期：{kpi_data['供应链周期(天)']}天 {'✓ 优秀' if kpi_data['供应链周期(天)'] <= 14 else '⚠ 需改进'}") # 综合评分 score = (min(order_fulfillment/100, 1) * 0.3 + min(on_time_delivery/100, 1) * 0.3 + min(inventory_turnover/10, 1) * 0.2 + (1 - min(kpi_data["供应链周期(天)"]/30, 1)) * 0.2) * 100 print(="*50) print(f"综合绩效评分：{score:.1f}/100") print(f"评级：{'A级-卓越' if score >= 90 else ('B级-良好' if score >= 80 else 'C级-待改进')}")