{"id":968176,"date":"2024-12-27T05:04:37","date_gmt":"2024-12-26T21:04:37","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/968176.html"},"modified":"2024-12-27T05:04:41","modified_gmt":"2024-12-26T21:04:41","slug":"python%e5%a6%82%e4%bd%95mrp%e7%9a%84%e8%ae%a1%e7%ae%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/968176.html","title":{"rendered":"python\u5982\u4f55MRP\u7684\u8ba1\u7b97"},"content":{"rendered":"

\"python\u5982\u4f55MRP\u7684\u8ba1\u7b97\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0MRP\uff08\u7269\u6599\u9700\u6c42\u8ba1\u5212\uff09\u7684\u8ba1\u7b97\uff0c\u5305\u62ec\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\u3001\u7b97\u6cd5\u5b9e\u73b0\u548c\u5e93\u51fd\u6570\u8c03\u7528\u3002\u5b9e\u73b0MRP\u7684\u5173\u952e\u5728\u4e8e\u7406\u89e3\u7269\u6599\u6e05\u5355\uff08BOM\uff09\u3001\u5e93\u5b58\u6c34\u5e73\u3001\u751f\u4ea7\u8ba1\u5212\u7b49\u56e0\u7d20\u3002\u4f7f\u7528Python\u8fdb\u884cMRP\u8ba1\u7b97\uff0c\u53ef\u4ee5\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3001\u7b80\u5316\u6d41\u7a0b\u3001\u5b9e\u65f6\u66f4\u65b0\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

MRP\u7684\u6838\u5fc3\u662f\u5c06\u9700\u6c42\u8f6c\u5316\u4e3a\u751f\u4ea7\u8ba1\u5212\u548c\u91c7\u8d2d\u8ba1\u5212\uff0c\u4ee5\u786e\u4fdd\u7269\u6599\u5728\u6b63\u786e\u7684\u65f6\u95f4\u3001\u6b63\u786e\u7684\u6570\u91cf\u548c\u6b63\u786e\u7684\u5730\u70b9\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0MRP\u8ba1\u7b97\u7684\u5404\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001MRP\u6982\u8ff0\u4e0e\u57fa\u672c\u539f\u7406<\/p>\n<\/p>\n

MRP\uff08Material Requirements Planning\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u7ba1\u7406\u5236\u9020\u8fc7\u7a0b\u7684\u7cfb\u7edf\u65b9\u6cd5\u3002\u5b83\u7684\u4e3b\u8981\u76ee\u6807\u662f\u786e\u4fdd\u751f\u4ea7\u8ba1\u5212\u4e2d\u6240\u6709\u7269\u6599\u7684\u53ef\u7528\u6027\uff0c\u51cf\u5c11\u5e93\u5b58\u6c34\u5e73\uff0c\u5e76\u4f18\u5316\u751f\u4ea7\u548c\u91c7\u8d2d\u8ba1\u5212\u3002<\/p>\n<\/p>\n

    \n
  1. MRP\u7684\u57fa\u672c\u539f\u7406<\/strong><\/li>\n<\/ol>\n

    MRP\u7cfb\u7edf\u901a\u8fc7\u9700\u6c42\u9884\u6d4b\u3001\u751f\u4ea7\u8ba1\u5212\u548c\u5e93\u5b58\u7ba1\u7406\u6765\u8ba1\u7b97\u6bcf\u79cd\u7269\u6599\u7684\u9700\u6c42\u65f6\u95f4\u548c\u6570\u91cf\u3002\u5176\u57fa\u672c\u8f93\u5165\u5305\u62ec\uff1a<\/p>\n<\/p>\n

      \n
    • \u4e3b\u751f\u4ea7\u8ba1\u5212\uff08MPS\uff09\uff1a\u5b9a\u4e49\u6700\u7ec8\u4ea7\u54c1\u7684\u751f\u4ea7\u65f6\u95f4\u548c\u6570\u91cf\u3002<\/li>\n
    • \u7269\u6599\u6e05\u5355\uff08BOM\uff09\uff1a\u63cf\u8ff0\u4ea7\u54c1\u7684\u7ec4\u6210\u7ed3\u6784\u548c\u5b50\u7ec4\u4ef6\u3002<\/li>\n
    • \u5e93\u5b58\u8bb0\u5f55\uff1a\u73b0\u6709\u5e93\u5b58\u7684\u6570\u91cf\u548c\u72b6\u6001\u3002<\/li>\n
    • \u91c7\u8d2d\u548c\u751f\u4ea7\u63d0\u524d\u671f\uff1a\u4ece\u4e0b\u5355\u5230\u4ea4\u8d27\u7684\u65f6\u95f4\u3002<\/li>\n<\/ul>\n
        \n
      1. MRP\u7684\u8ba1\u7b97\u8fc7\u7a0b<\/strong><\/li>\n<\/ol>\n

        MRP\u8ba1\u7b97\u8fc7\u7a0b\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

          \n
        • \u51c0\u9700\u6c42\u8ba1\u7b97\uff1a\u6839\u636eMPS\u548c\u73b0\u6709\u5e93\u5b58\u8ba1\u7b97\u51c0\u9700\u6c42\u3002<\/li>\n
        • \u6279\u91cf\u8ba1\u5212\uff1a\u786e\u5b9a\u6bcf\u6b21\u751f\u4ea7\u6216\u91c7\u8d2d\u7684\u6279\u91cf\u3002<\/li>\n
        • \u65f6\u95f4\u8ba1\u5212\uff1a\u6839\u636e\u63d0\u524d\u671f\u5b89\u6392\u751f\u4ea7\u548c\u91c7\u8d2d\u65f6\u95f4\u3002<\/li>\n
        • \u8ba2\u5355\u53d1\u5e03\uff1a\u751f\u6210\u751f\u4ea7\u548c\u91c7\u8d2d\u8ba2\u5355\u3002<\/li>\n<\/ul>\n

          \u4e8c\u3001Python\u5b9e\u73b0MRP\u7684\u51c6\u5907\u5de5\u4f5c<\/p>\n<\/p>\n

          \u5728Python\u4e2d\u5b9e\u73b0MRP\u8ba1\u7b97\uff0c\u9700\u8981\u51c6\u5907\u6570\u636e\u7ed3\u6784\u548c\u7b97\u6cd5\u8bbe\u8ba1\u3002<\/p>\n<\/p>\n

            \n
          1. \u6570\u636e\u7ed3\u6784\u8bbe\u8ba1<\/strong><\/li>\n<\/ol>\n

            \u4e3a\u4e86\u5b58\u50a8\u548c\u5904\u7406MRP\u6570\u636e\uff0c\u9700\u8981\u8bbe\u8ba1\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u5b57\u5178\u6216\u6570\u636e\u6846\u6765\u5b58\u50a8\u7269\u6599\u6e05\u5355\uff08BOM\uff09\u3001\u5e93\u5b58\u8bb0\u5f55\u548c\u751f\u4ea7\u8ba1\u5212\u3002<\/p>\n<\/p>\n

            import pandas as pd<\/p>\n
            \u793a\u4f8b\u7269\u6599\u6e05\u5355\uff08BOM\uff09<\/strong><\/h2>\n
            bom_data = {<\/p>\n
                'Product': ['A', 'B', 'C'],<\/p>\n
                'Component': ['B', 'C', 'D'],<\/p>\n
                'Quantity': [2, 3, 1]<\/p>\n
            }<\/p>\n
            bom_df = pd.DataFrame(bom_data)<\/p>\n
            \u5e93\u5b58\u8bb0\u5f55<\/strong><\/h2>\n
            inventory_data = {<\/p>\n
                'Component': ['A', 'B', 'C', 'D'],<\/p>\n
                'On_Hand': [10, 20, 30, 40]<\/p>\n
            }<\/p>\n
            inventory_df = pd.DataFrame(inventory_data)<\/p>\n
            \u4e3b\u751f\u4ea7\u8ba1\u5212\uff08MPS\uff09<\/strong><\/h2>\n
            mps_data = {<\/p>\n
                'Product': ['A'],<\/p>\n
                'Demand': [50],<\/p>\n
                'Due_Date': ['2023-10-15']<\/p>\n
            }<\/p>\n
            mps_df = pd.DataFrame(mps_data)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u7b97\u6cd5\u8bbe\u8ba1<\/strong><\/li>\n<\/ol>\n
              MRP\u7b97\u6cd5\u7684\u8bbe\u8ba1\u9700\u8981\u8003\u8651\u5982\u4f55\u4ece\u751f\u4ea7\u8ba1\u5212\u63a8\u5bfc\u51fa\u5404\u4e2a\u7ec4\u4ef6\u7684\u9700\u6c42\u548c\u8ba2\u5355\u3002\u53ef\u4ee5\u4f7f\u7528\u9012\u5f52\u7b97\u6cd5\u904d\u5386\u7269\u6599\u6e05\u5355\uff0c\u8ba1\u7b97\u6bcf\u4e2a\u7ec4\u4ef6\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
              \u4e09\u3001Python\u5b9e\u73b0MRP\u8ba1\u7b97<\/p>\n<\/p>\n
              \u5177\u4f53\u5b9e\u73b0MRP\u8ba1\u7b97\u7684\u6b65\u9aa4\u5305\u62ec\u51c0\u9700\u6c42\u8ba1\u7b97\u3001\u6279\u91cf\u8ba1\u5212\u3001\u65f6\u95f4\u8ba1\u5212\u548c\u8ba2\u5355\u53d1\u5e03\u3002<\/p>\n<\/p>\n
                \n
              1. \u51c0\u9700\u6c42\u8ba1\u7b97<\/strong><\/li>\n<\/ol>\n
                \u9996\u5148\uff0c\u6839\u636e\u4e3b\u751f\u4ea7\u8ba1\u5212\uff08MPS\uff09\u548c\u73b0\u6709\u5e93\u5b58\u8ba1\u7b97\u51c0\u9700\u6c42\u3002\u51c0\u9700\u6c42\u662f\u6307\u5728\u6ee1\u8db3\u751f\u4ea7\u8ba1\u5212\u7684\u9700\u6c42\u540e\uff0c\u5b9e\u9645\u9700\u8981\u8865\u5145\u7684\u7269\u6599\u6570\u91cf\u3002<\/p>\n<\/p>\n
                def calculate_net_requirements(mps_df, inventory_df, bom_df):<\/p>\n
                    net_requirements = []<\/p>\n
                    for index, row in mps_df.iterrows():<\/p>\n
                        product = row['Product']<\/p>\n
                        demand = row['Demand']<\/p>\n
                        due_date = row['Due_Date']<\/p>\n
                        # \u67e5\u627e\u4ea7\u54c1\u7684\u7ec4\u4ef6\u548c\u6570\u91cf<\/p>\n
                        components = bom_df[bom_df['Product'] == product]<\/p>\n
                        for _, component_row in components.iterrows():<\/p>\n
                            component = component_row['Component']<\/p>\n
                            required_quantity = component_row['Quantity'] * demand<\/p>\n
                            # \u67e5\u627e\u73b0\u6709\u5e93\u5b58<\/p>\n
                            on_hand = inventory_df.loc[inventory_df['Component'] == component, 'On_Hand'].values[0]<\/p>\n
                            # \u8ba1\u7b97\u51c0\u9700\u6c42<\/p>\n
                            net_requirement = max(0, required_quantity - on_hand)<\/p>\n
                            net_requirements.append({<\/p>\n
                                'Component': component,<\/p>\n
                                'Net_Requirement': net_requirement,<\/p>\n
                                'Due_Date': due_date<\/p>\n
                            })<\/p>\n
                    return pd.DataFrame(net_requirements)<\/p>\n
                net_requirements_df = calculate_net_requirements(mps_df, inventory_df, bom_df)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u6279\u91cf\u8ba1\u5212<\/strong><\/li>\n<\/ol>\n
                  \u6279\u91cf\u8ba1\u5212\u7528\u4e8e\u786e\u5b9a\u6bcf\u6b21\u751f\u4ea7\u6216\u91c7\u8d2d\u7684\u6570\u91cf\u3002\u53ef\u4ee5\u6839\u636e\u7ecf\u6d4e\u6279\u91cf\uff08EOQ\uff09\u6216\u56fa\u5b9a\u6279\u91cf\u6765\u8bbe\u7f6e\u3002<\/p>\n<\/p>\n
                  def batch_planning(net_requirements_df, batch_size=10):<\/p>\n
                      net_requirements_df['Order_Quantity'] = (net_requirements_df['Net_Requirement'] \/ batch_size).apply(lambda x: (int(x) + 1) * batch_size)<\/p>\n
                      return net_requirements_df<\/p>\n
                  net_requirements_df = batch_planning(net_requirements_df)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u65f6\u95f4\u8ba1\u5212<\/strong><\/li>\n<\/ol>\n
                    \u65f6\u95f4\u8ba1\u5212\u6839\u636e\u63d0\u524d\u671f\u5b89\u6392\u751f\u4ea7\u548c\u91c7\u8d2d\u65f6\u95f4\u3002\u63d0\u524d\u671f\u662f\u6307\u4ece\u4e0b\u5355\u5230\u4ea4\u8d27\u6240\u9700\u7684\u65f6\u95f4\u3002<\/p>\n<\/p>\n
                    def time_planning(net_requirements_df, lead_time=7):<\/p>\n
                        net_requirements_df['Order_Date'] = pd.to_datetime(net_requirements_df['Due_Date']) - pd.Timedelta(days=lead_time)<\/p>\n
                        return net_requirements_df<\/p>\n
                    net_requirements_df = time_planning(net_requirements_df)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u8ba2\u5355\u53d1\u5e03<\/strong><\/li>\n<\/ol>\n
                      \u6700\u540e\uff0c\u751f\u6210\u751f\u4ea7\u548c\u91c7\u8d2d\u8ba2\u5355\u3002\u8ba2\u5355\u53d1\u5e03\u5305\u62ec\u4e0b\u8fbe\u751f\u4ea7\u548c\u91c7\u8d2d\u6307\u4ee4\uff0c\u786e\u4fdd\u7269\u6599\u5728\u9700\u8981\u65f6\u53ef\u7528\u3002<\/p>\n<\/p>\n
                      def generate_orders(net_requirements_df):<\/p>\n
                          orders = []<\/p>\n
                          for _, row in net_requirements_df.iterrows():<\/p>\n
                              if row['Net_Requirement'] > 0:<\/p>\n
                                  orders.append({<\/p>\n
                                      'Component': row['Component'],<\/p>\n
                                      'Order_Quantity': row['Order_Quantity'],<\/p>\n
                                      'Order_Date': row['Order_Date']<\/p>\n
                                  })<\/p>\n
                          return pd.DataFrame(orders)<\/p>\n
                      orders_df = generate_orders(net_requirements_df)<\/p>\n
                      print(orders_df)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u56db\u3001Python\u5b9e\u73b0MRP\u7684\u4f18\u5316\u4e0e\u5e94\u7528<\/p>\n<\/p>\n
                      \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cMRP\u7cfb\u7edf\u9700\u8981\u5904\u7406\u590d\u6742\u7684\u4e1a\u52a1\u9700\u6c42\u548c\u5927\u89c4\u6a21\u6570\u636e\uff0c\u56e0\u6b64\u5728Python\u5b9e\u73b0\u4e2d\u53ef\u4ee5\u8fdb\u884c\u4ee5\u4e0b\u4f18\u5316\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u6027\u80fd\u4f18\u5316<\/strong><\/li>\n<\/ol>\n
                        \u53ef\u4ee5\u901a\u8fc7\u5e76\u884c\u8ba1\u7b97\u3001\u7f13\u5b58\u4e2d\u95f4\u7ed3\u679c\u3001\u4f18\u5316\u7b97\u6cd5\u590d\u6742\u5ea6\u7b49\u65b9\u5f0f\u63d0\u9ad8MRP\u8ba1\u7b97\u7684\u6027\u80fd\u3002\u4f8b\u5982\uff0c\u4f7f\u7528NumPy\u548cPandas\u52a0\u901f\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
                          \n
                        1. \u6269\u5c55\u529f\u80fd<\/strong><\/li>\n<\/ol>\n
                          \u6839\u636e\u5b9e\u9645\u9700\u6c42\uff0c\u53ef\u4ee5\u6269\u5c55MRP\u529f\u80fd\uff0c\u5982\u591a\u5c42\u6b21BOM\u5904\u7406\u3001\u52a8\u6001\u5e93\u5b58\u66f4\u65b0\u3001\u4f9b\u5e94\u5546\u7ba1\u7406\u7b49\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u5b9e\u65f6\u6570\u636e\u66f4\u65b0<\/strong><\/li>\n<\/ol>\n
                            \u901a\u8fc7\u4e0eERP\u7cfb\u7edf\u96c6\u6210\uff0c\u5b9e\u73b0MRP\u6570\u636e\u7684\u5b9e\u65f6\u66f4\u65b0\u548c\u52a8\u6001\u8c03\u6574\uff0c\u63d0\u9ad8\u8ba1\u5212\u7684\u51c6\u786e\u6027\u548c\u54cd\u5e94\u901f\u5ea6\u3002<\/p>\n<\/p>\n
                              \n
                            1. \u53ef\u89c6\u5316\u5206\u6790<\/strong><\/li>\n<\/ol>\n
                              \u4f7f\u7528Python\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff08\u5982Matplotlib\u3001Seaborn\uff09\u5c55\u793aMRP\u8ba1\u7b97\u7ed3\u679c\uff0c\u5e2e\u52a9\u7ba1\u7406\u8005\u505a\u51fa\u66f4\u597d\u7684\u51b3\u7b56\u3002<\/p>\n<\/p>\n
                              \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                              \u901a\u8fc7Python\u5b9e\u73b0MRP\u8ba1\u7b97\uff0c\u53ef\u4ee5\u63d0\u9ad8\u4f01\u4e1a\u751f\u4ea7\u7ba1\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86MRP\u7684\u57fa\u672c\u539f\u7406\u3001\u6570\u636e\u7ed3\u6784\u8bbe\u8ba1\u3001\u7b97\u6cd5\u5b9e\u73b0\u53ca\u4f18\u5316\u5e94\u7528\u3002\u5c3d\u7ba1MRP\u7cfb\u7edf\u7684\u5b9e\u73b0\u9700\u8981\u8003\u8651\u591a\u79cd\u590d\u6742\u56e0\u7d20\uff0c\u4f46\u901a\u8fc7Python\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u548c\u7075\u6d3b\u6027\uff0c\u53ef\u4ee5\u6709\u6548\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u5e76\u4e3a\u4f01\u4e1a\u63d0\u4f9b\u5168\u9762\u7684\u751f\u4ea7\u8ba1\u5212\u548c\u7269\u6599\u7ba1\u7406\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               Python\u5982\u4f55\u5b9e\u73b0\u7269\u6599\u9700\u6c42\u8ba1\u5212\uff08MRP\uff09\u7684\u8ba1\u7b97\uff1f<\/strong>
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