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Dynamics and risk assessment of a remanufacturing closed-loop supply chain system using the internet of things and neural network approach

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Abstract

To address the problem of the large subjective error of expert evaluation methods in supply chain management, the supply chain system is comprehensively analyzed, and a deep learning backpropagation (BP) neural network-based supply chain risk assessment model is constructed. First, the basic theories of supply chain and risk assessment are described, and the process of supply chain risk management is explained. Then, the ANN (artificial neural network) is discussed in detail. On this basis, the feasibility of the BP neural network applied in the risk assessment of the supply chain is analyzed. In addition, the risks of the supply chain system are analyzed under the support of the Internet of Things (IoT), and the indices for risk assessment of the supply chain are determined. The reliability analysis, validity analysis, and factor analysis of the evaluation indices are implemented using a questionnaire survey, based on which the risk assessment indices of the supply chain are determined as 7 first-level indices and 20 sesond-level indices. Finally, a BP neural network-based supply chain risk assessment model is established, and the simulation results are analyzed in MATLAB. The maximum relative error of the proposed BP neural network model for supply chain risk assessment is as low as 0.03076923%, and that calculated by the AHP (analytic hierarchy process) is 57.41%. Compared with that of AHP, the fitting degree of the BP neural network-based supply chain risk assessment model is much higher. Meanwhile, the simulation experiment indicates that the established risk assessment model has strong generalization ability and learning ability. This work not only provides technical support for the development of remanufacturing closed-loop supply chain systems but also contributes to the improvement of the accuracy of supply chain risk assessment.

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Date Availability Statement

The data used to support the findings of this study are included within the article.

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Author information

Authors and Affiliations

  1. School of Business Administration, Huaqiao University, Quanzhou, 362000, China

    Wenjun Pan

  2. Business School of Liming Vocational University, Quanzhou, 362000, China

    Lin Miao

Authors
  1. Wenjun Pan
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  2. Lin Miao
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Corresponding author

Correspondence to Lin Miao.

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Appendix

Appendix

Risk assessment indices

First-level index

Second-level index

Environmental risk R1

Environmental disaster risk R11

 

Accident risk R12

Political risk R2

Policy and regulatory risk R21

 

Government intervention risk R22

 

Target strategic risk R31

Cooperation risk R3

Cooperative trust risk R32

 

Profit distribution risk R33

 

Market demand risk R41

Demand risk R4

Customer preference risk R42

 

Customer loyalty risk R43

 

Product cyclical risk R44

 

Delivery delay risk R51

Supply risk R5

Product quality risk R52

 

Competition risk between suppliers R53

 

Transport process risk R61

Logistics risk R6

Transportation product risk R62

 

Safety stock risk R63

 

Employee operation risk R64

Information risk R7

System security risk R71

 

Information sharing risk R72

Sample dataset

Index

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

R11

0.12

0.71

0.36

0.45

0.87

0.46

0.54

0.15

0.51

0.26

0.67

0.07

0.17

0.53

0.05

R12

0.32

0.53

0.21

0.52

0.52

0.23

0.12

0.45

0.31

0.41

0.35

0.27

0.25

0.8

0.24

R21

0.57

0.29

0.32

0.64

0.46

0.14

0.54

0.52

0.97

0.16

0.16

0.29

0.29

0.15

0.18

R22

0.29

0.3

0.64

0.62

0.23

0.95

0.34

0.06

0.84

0.65

0.29

0.65

0.37

0.63

0.49

R31

0.95

0.77

0.95

0.06

0.35

0.84

0.16

0.75

0.12

0.17

0.34

0.43

0.49

0.48

0.46

R32

0.39

0.76

0.18

0.19

0.73

0.35

0.68

0.25

0.77

0.41

0.2

0.81

0.97

0.37

0.67

R33

0.58

0.81

0.56

0.68

0.54

0.53

0.94

0.13

0.15

0.91

0.33

0.27

0.25

0.81

0.34

R41

0.62

0.7

0.37

0.84

0.24

0.78

0.71

0.25

0.25

0.73

0.51

0.94

0.27

0.96

0.46

R42

0.54

0.45

0.46

0.52

0.27

0.91

0.81

0.1

0.46

0.43

0.06

0.56

0.58

0.51

0.77

R43

0.03

0.41

0.67

0.74

0.66

0.14

0.21

0.62

0.16

0.28

0.98

0.18

0.39

0.7

0.85

R44

0.6

0.86

0.58

0.63

0.37

0.37

0.26

0.42

0.87

0.16

0.91

0.32

0.78

0.26

0.5

R51

0.58

0.01

0.65

0.16

0.56

0.35

0.12

0.55

0.26

0.6

0.31

0.63

0.73

0.34

0.43

R52

0.78

0.37

0.56

0.26

0.04

0.61

0.51

0.62

0.34

0.32

0.68

0.85

0.42

0.19

0.08

R53

0.09

0.56

0.32

0.36

0.34

0.74

0.66

0.54

0.38

0.64

0.8

0.24

0.38

0.28

0.67

R61

0.11

0.36

0.09

0.61

0.61

0.41

0.24

0.96

0.94

0.1

0.26

0.95

0.47

0.95

0.31

R62

0.41

0.52

0.35

0.41

0.54

0.86

0.55

0.15

0.27

0.92

0.36

0.72

0.36

0.37

0.29

R63

0.62

0.59

0.65

0.19

0.88

0.31

0.14

0.65

0.14

0.84

0.33

0.53

0.88

0.18

0.78

R64

0.85

0.87

0.71

0.63

0.74

0.78

0.84

0.51

0.37

0.58

0.16

0.65

0.51

0.22

0.23

R71

0.26

0.3

0.46

0.89

0.37

0.26

0.99

0.44

0.76

0.57

0.17

0.38

0.3

0.73

0.49

R72

0.35

0.61

0.16

0.26

0.51

0.38

0.76

0.58

0.49

0.87

0.07

0.27

0.62

0.86

0.99

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Pan, W., Miao, L. Dynamics and risk assessment of a remanufacturing closed-loop supply chain system using the internet of things and neural network approach. J Supercomput 79, 3878–3901 (2023). https://doi.org/10.1007/s11227-022-04727-6

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