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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 37-45    DOI: 10.11925/infotech.2096-3467.2018.0833
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Evaluating and Optimizing Supply Chains with LMBP Algorithm
Meng Hu1,2, Liang Xiaobei1, Yang Yixiong2,3, Li Min2,3()
1School of Economics and Management, Tongji University, Shanghai 200092, China
2College of Fashion and Design, Donghua University, Shanghai 200051, China
3Shanghai Institute of Design and Innovation, Tongji University, Shanghai 200092, China
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Abstract  

[Objective] This paper uses the LMBP algorithm of feedback neural network to evaluate and optimize the supply chains, aiming to improve the decision-making of enterprises. [Methods] First, we built an evaluation model for supply chains. Then, we generated 21 indicators for corporate performance based on this model. Third, we used the MATLAB to evaluate this algorithm. [Results] The proposed method helped enterprises obtain the results of performance analysis in time, and then improved the management of procurement, inventory, and sales. It reduced the operation costs of enterprises, and improved the decision making process. [Limitations] The new method should be examined with more cases. [Conclusions] The proposed method could improve the performance of supply chains.

Key wordsNeural Network Algorithm      Supply Chain Performance Measurement      Supply Chain Optimization     
Received: 26 July 2018      Published: 11 December 2018
ZTFLH:  F272.3  

Cite this article:

Meng Hu,Liang Xiaobei,Yang Yixiong,Li Min. Evaluating and Optimizing Supply Chains with LMBP Algorithm. Data Analysis and Knowledge Discovery, 2018, 2(11): 37-45.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0833     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I11/37

供应链绩效水平得分区间 绩效水平等级
[0,0.25)
[0.25,0.50)
[0.50,0.75)
[0.75,1]
指标项 权重 指标性质 2013 2014 2015 2016 2017
成本 劳动力成本 0.053 成本型 定量 1.000 0.710 0.289 0.127 0.000
原材料成本 0.048 成本型 定量 0.000 0.170 0.607 0.685 1.000
信息技术成本 0.025 成本型 定量 0.108 0.166 0.268 0.000 1.000
设施设备成本 0.013 成本型 定量 0.000 0.224 0.628 0.833 1.000
研发成本 0.026 成本型 定量 0.000 0.233 0.796 1.000 0.525
时间 存货成本 0.117 成本型 定量 1.000 0.946 0.429 0.388 0.000
供应链单元周期 0.014 成本型 定量 0.000 0.125 0.125 0.956 1.000
准时交货率 0.064 效益型 定量 0.000 0.340 0.720 1.000 0.960
质量 现金流时间 0.059 效益型 定量 0.074 0.000 0.852 0.778 1.000
订单完成率 0.035 效益型 定量 1.000 1.000 1.000 1.000 1.000
产品疵品率 0.067 成本型 定量 0.250 0.000 0.750 1.000 1.000
合作伙伴关系 0.105 效益型 定性 1.000 1.000 1.000 1.000 1.000
产品质量水平 0.059 效益型 定性 1.000 1.000 1.000 1.000 1.000
退货率 0.058 成本型 定量 0.000 0.125 0.500 1.000 0.875
可靠性 供应链单元可靠性 0.012 效益型 定性 0.800 0.800 0.850 0.850 0.900
组织结构可靠性 0.014 效益型 定性 0.850 0.800 0.900 0.850 0.900
协调可靠性 0.037 效益型 定性 0.700 0.700 0.800 0.750 0.800
柔性 运作柔性 0.010 效益型 定性 0.600 0.650 0.700 0.700 0.700
物流柔性 0.008 效益型 定性 0.800 0.850 0.850 0.900 0.900
信息柔性 0.024 效益型 定性 0.550 0.600 0.600 0.700 0.700
安全性 0.154 效益型 定性 1.000 0.750 1.000 1.000 0.750
绩效总值 0.620 0.589 0.741 0.804 0.750
绩效水平
分类序号 输入层到隐含层函数 隐含层到输出层函数 最优隐含节点数 Epoch MSE均方误差
1 Tansig
(正切函数)
Tansig 12 5 9.02E-26
2 Logsig 10 5 3.06E-22
3 Purelin 18 4 2.78E-18
4 Logsig
(对数函数)
Tansig 20 4 7.84E-22
5 Logsig 20 4 7.84E-22
6 Purelin 24 4 1.87E-22
7 Purelin
(线性函数)
Tansig 24 4 1.21E-25
8 Logsig 24 4 1.21E-25
9 Purelin 20 4 2.55E-25
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