基于数据挖掘的电力装备制造企业多价值链协同下的成本预测研究

doi:10.16381/j.cnki.issn1003-207x.2023.1836

Abstract

Abstract:

In the context of multi value chain collaboration in power equipment manufacturing enterprises， enterprise cost prediction is influenced by multiple factors such as supply chain， production chain， marketing chain， and service chain. In order to improve the accuracy of cost prediction for power equipment manufacturing enterprises and improve the level of cost management， a cost prediction model is constructed based on data mining technology， which combines firefly algorithm （FA） and sparrow search algorithm （SSA） optimization BP neural network （FA-SSA-BP）. Firstly， using network data mining technology， a database of influencing factors for multi value chain collaboration in power equipment manufacturing enterprises is constructed； Secondly， the Pearson correlation coefficient and grey relational analysis （GRA） method are used to screen the influencing factor library and determine key factors； Then， a FA-SSA-BP cost prediction model is constructed， in which FA-SSA strengthened the global search ability， avoided premature falling into local optima， and improved convergence accuracy. Subsequently， based on the relevant data of the main business Ring Main Unit of Q power equipment manufacturing enterprise， the prediction model constructed in this paper is used for cost prediction and error analysis， and the prediction effect is compared with other optimization models. The results indicate that the model proposed in this paper significantly reduces prediction errors and effectively improves the accuracy of cost prediction compared to the comparative model. Then， sensitivity analysis is conducted on key factors， pointing out the impact of different influencing factors on enterprise costs. Finally， based on the above research， targeted cost management suggestions are proposed for power equipment manufacturing enterprises under multi value chain collaboration. The cost prediction model constructed in this paper and the proposed management suggestions lay a solid foundation for cost management in power equipment manufacturing enterprises under multi value chain collaboration.

Key words: multi value chain collaboration, FA-SSA-BP, cost prediction, data mining, power equipment manufacturing enterprise

CLC Number:

F425

Xiaomin Xu,Shipeng Zheng,Zhiyi Wang, et al. Research on Cost Prediction of Electric Power Equipment Manufacturing Enterprises under Multi Value Chain Collaboration Based on Data Mining[J]. Chinese Journal of Management Science, 2025, 33(12): 200-213.

Figures/Tables 19

References 44

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所在价值链	影响因素指标	所在价值链	影响因素指标
供应价值链	十种有色金属产量	供应价值链	电网基本建设投资
	精炼铜产量	生产价值链	研发支出
	原铝(电解铝)产量		原材料
	光电子器件产量		生产设备损耗
	合成橡胶产量		生产职工工资
	初级形态塑料产量	营销价值链	销售费用职工薪酬
	集成电路产量	营销价值链	环网柜系列销售量
	电工仪器仪表产量	服务价值链	管理费用
	全国环网柜市场规模		售后费用
	社会固定资产投资		售后费用

模型	参数	值	模型	参数	值	模型	参数	值
BP	迭代次数	1000	SSA	初始种群规模	30	FA	种群规模	30
	学习速率	0.01		最大进化代数	50		光吸收系数	1
	训练目标最小误差	0.00001		发现者/警戒者比例	0.7/0.2		最大吸引度	2
	最小性能梯度	1.00E-06		安全值	0.6		步长因子	0.2

误差指标	平均绝对误差MAE	均方误差MSE	均方根误差RMSE	平均绝对百分比误差MAPE（%）
PSO-BP	61.790	5728.453	75.687	1.440
WOA-BP	56.876	4530.006	67.305	1.219
SSA-BP	47.085	3114.146	55.805	1.013
FA-SSA-BP	18.690	640.510	25.308	0.363

影响因素	敏感性系数（%）	影响因素	敏感性系数（%）
售后费用	69.97	精炼铜产量	12.07
研发支出	64.69	光电子器件产量	11.66
环网柜销售量	58.64	有色金属产量	10.99
销售费用薪酬	56.12	合成橡胶产量	8.79
生产职工工资	36.94	集成电路产量	7.57
管理费用	34.23	原铝产量	6.78
原材料	28.30	社会资产投资	5.36
电网基建投资	25.47	初级塑料产量	4.40
环网柜市场规模	19.36	电工仪器产量	4.11
生产设备损耗	15.97	电工仪器产量	4.11

影响因素	-5%	-4%	-3%	-2%	-1%	1%	2%	3%	4%	5%
售后费用	-0.162	-0.113	-0.072	-0.039	-0.014	0.014	0.017	0.013	0.002	-0.016
研发支出	-0.228	-0.181	-0.135	-0.089	-0.044	0.044	0.087	0.129	0.171	0.212
销售量	-0.089	-0.063	-0.040	-0.022	-0.007	0.010	0.014	0.013	0.009	0.002
销售费用	-0.605	-0.476	-0.350	-0.230	-0.113	0.109	0.214	0.315	0.413	0.507
生产工资	0.163	0.131	0.099	0.066	0.033	-0.034	-0.068	-0.102	-0.137	-0.173
管理费用	0.238	0.196	0.153	0.106	0.056	-0.053	-0.113	-0.177	-0.245	-0.317

Research on Cost Prediction of Electric Power Equipment Manufacturing Enterprises under Multi Value Chain Collaboration Based on Data Mining

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