Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks
Abstract
1. Introduction
- Implementing DANN for data generalization on small data with high variation.
- Enhancing the capability of the ML model to forecast the sales.
- Utilizing the transfer learning approach on a dataset to predict the sales of different products (different target variables).
- Implication of result for supply chain optimization using the sales prediction.
2. Related Work
2.1. Datasets in Supply Chain
2.2. Models in a High Variable and Limited Data Framework
3. Proposed Methodology
Algorithm 1 Pipeline of Proposed Methodology |
|
3.1. Dataset Description
3.2. Data Handling and Analysis
Non-Stationary Testing of the Dataset
3.3. Data Preprocessing
3.4. Domain Adversarial Neural Network
3.5. Machine Learning Models
3.5.1. Linear Regression Model
3.5.2. Support Vector Regressor Model
3.5.3. Decision Tree Regressor Model
3.5.4. Random Forest Regressor Model
3.5.5. Extreme Gradient Boost XGBoost Regressor Model
3.6. Performance Parameters of Model
4. Results and Discussion
4.1. Results of DANN Model for Data Generalization
4.2. Comparative Study of Outcomes of Various Machine Learning Models
4.3. Sales Prediction for Supply Chain Optimization
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
DANN | Domain Adversarial Neural Networks |
SCO | Supply Chain Optimization |
SCM | Supply Chain Management |
FMCG | Fast-Moving Consumer Goods |
ML | Machine Learning |
DL | Deep Learning |
RNN | Recurrent Neural Network |
SVR | Support Vector Regression |
RF | Random Forest |
DTR | Decision Tree Regressor |
RMSE | Root Mean Square Error |
MSE | Mean Square Error |
MAE | Mean Absolute Error |
MAPE | Mean Absolute Percentage Error |
XGBoost | Extreme Gradient Boosting |
KPSS | Kwiatkowski–Phillips–Schmidt–Shin |
ReLU | Rectified Linear Unit |
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Sr. No. | Product ID | Stationarity | KPSS Result (p-Value) | Sr. No. | Product ID | Stationarity | KPSS Result (p-Value) |
---|---|---|---|---|---|---|---|
1 | AT5X5K | NS | 0.0419 | 16 | POV001L24P | NS | 0.01 |
2 | ATN01K24P | S | 0.1 | 17 | POV002L09P | S | 0.1 |
3 | ATN02K12P | S | 0.1 | 18 | POV005L04P | S | 0.1 |
4 | ATWWP001K24P | NS | 0.046 | 19 | POV500M24P | S | 0.0993 |
5 | ATWWP002K12P | S | 0.1 | 20 | SE200G24P | NS | 0.01 |
6 | MAR01K24P | S | 0.1 | 21 | SE500G24P | NS | 0.019 |
7 | MAR02K12P | NS | 0.049 | 22 | SOP001L12P | NS | 0.025 |
8 | MASR025K | S | 0.1 | 23 | SOS001L12P | NS | 0.01 |
9 | POP001L12P.1 | S | 0.1 | 24 | SOS002L09P | NS | 0.022 |
10 | POP001L12P | S | 0.089 | 25 | SOS003L04P | NS | 0.0254 |
11 | POP002L09P | S | 0.074 | 26 | SOS005L04P | NS | 0.01 |
12 | POP005L04P | NS | 0.048 | 27 | SOS008L02P | NS | 0.01 |
13 | POP500M24P | S | 0.826 | 28 | SOS250M48P | Non-Stationary | 0.025 |
14 | POPF01L12P | NS | 0.036 | 29 | SOS500M24P | NS | 0.05 |
15 | MAHS025K | NS | 0.029 |
Sr. No. | Product ID | Score | ||||
---|---|---|---|---|---|---|
1 | AT5X5K | 0.014 | 0.118 | 0.985 | 0.078 | 0.28 |
2 | ATN01K24P | 0.01 | 0.102 | 0.989 | 0.077 | 0.24 |
3 | ATN02K12P | 0.027 | 0.164 | 0.972 | 0.096 | 0.72 |
4 | MAR01K24P | 0.011 | 0.104 | 0.988 | 0.082 | 0.34 |
5 | MAR02K12P | 0.012 | 0.109 | 0.987 | 0.076 | 0.24 |
6 | POP001L12P.1 | 0.008 | 0.089 | 0.991 | 0.06 | 0.44 |
7 | POP001L12P | 0.022 | 0.148 | 0.977 | 0.084 | 0.58 |
8 | POP002L09P | 0.016 | 0.126 | 0.983 | 0.094 | 0.345 |
9 | POPF01L12P | 0.012 | 0.109 | 0.987 | 0.074 | 0.335 |
10 | POV001L24P | 0.028 | 0.167 | 0.971 | 0.107 | 0.32 |
11 | SE200G24P | 0.015 | 0.122 | 0.984 | 0.08 | 0.265 |
12 | SE500G24P | 0.015 | 0.122 | 0.984 | 0.089 | 0.328 |
13 | SOP001L12P | 0.0107 | 0.103 | 0.989 | 0.085 | 0.226 |
14 | SOS001L12P | 0.008 | 0.089 | 0.991 | 0.073 | 0.38 |
15 | SOS008L02P | 0.011 | 0.105 | 0.988 | 0.078 | 0.24 |
16 | SOS002L09P | 0.04 | 0.197 | 0.956 | 0.13 | 0.49 |
17 | SOS003L04P | 0.017 | 0.13 | 0.982 | 0.102 | 0.38 |
18 | SOS005L04P | 0.008 | 0.089 | 0.991 | 0.073 | 0.18 |
Model | Product ID | Performance Metrics | ||||
---|---|---|---|---|---|---|
Score | ||||||
Linear Regression | ATWWP001K24P | 0.014 | 0.121 | 0.985 | 0.085 | 0.526 |
ATWWP002K12P | 0.011 | 0.109 | 0.988 | 0.075 | 0.572 | |
MAHS025K | 0.012 | 0.11 | 0.987 | 0.072 | 0.253 | |
MASR025K | 0.036 | 0.18 | 0.963 | 0.143 | 0.41 | |
POP005L04P | 0.039 | 0.198 | 0.96 | 0.147 | 0.57 | |
POP500M24P | 0.125 | 0.353 | 0.874 | 0.25 | 0.74 | |
POV002L09P | 0.042 | 0.205 | 0.957 | 0.161 | 0.72 | |
POV005L04P | 0.03 | 0.175 | 0.969 | 0.136 | 0.613 | |
POV500M24P | 0.025 | 0.16 | 0.974 | 0.124 | 0.6 | |
SOS250M48P | 0.06 | 0.245 | 0.939 | 0.175 | 0.523 | |
SOS500M24P | 0.081 | 0.284 | 0.918 | 0.223 | 0.575 | |
SVR (Linear kernel) | ATWWP001K24P | 0.018 | 0.136 | 0.98 | 0.106 | 0.56 |
ATWWP002K12P | 0.015 | 0.12 | 0.984 | 0.085 | 0.58 | |
MAHS025K | 0.012 | 0.11 | 0.987 | 0.074 | 0.254 | |
MASR025K | 0.038 | 0.19 | 0.969 | 0.14 | 0.4 | |
POP005L04P | 0.04 | 0.2 | 0.959 | 0.147 | 0.52 | |
POP500M24P | 0.127 | 0.35 | 0.87 | 0.25 | 0.81 | |
POV002L09P | 0.045 | 0.21 | 0.954 | 0.163 | 0.68 | |
POV005L04P | 0.032 | 0.17 | 0.967 | 0.135 | 0.54 | |
POV500M24P | 0.026 | 0.16 | 0.973 | 0.127 | 0.65 | |
SOS250M48P | 0.061 | 0.24 | 0.938 | 0.16 | 0.55 | |
SOS500M24P | 0.082 | 0.28 | 0.917 | 0.22 | 0.57 | |
SVR (RBF kernel) | ATWWP001K24P | 0.023 | 0.152 | 0.976 | 0.11 | 0.69 |
ATWWP002K12P | 0.112 | 0.335 | 0.887 | 0.11 | 0.55 | |
MAHS025K | 0.053 | 0.23 | 0.966 | 0.107 | 0.26 | |
MASR025K | 0.142 | 0.377 | 0.857 | 0.14 | 0.34 | |
POP005L04P | 0.078 | 0.28 | 0.921 | 0.131 | 0.5 | |
POP500M24P | 0.06 | 0.26 | 0.932 | 0.17 | 0.76 | |
POV002L09P | 0.034 | 0.18 | 0.965 | 0.11 | 0.31 | |
POV005L04P | 0.031 | 0.17 | 0.968 | 0.1 | 0.34 | |
POV500M24P | 0.029 | 0.17 | 0.97 | 0.1 | 0.64 | |
SOS250M48P | 0.063 | 0.25 | 0.936 | 0.142 | 0.39 | |
SOS500M24P | 0.046 | 0.214 | 0.953 | 0.14 | 0.49 | |
XGBoost Regressor | ATWWP001K24P | 1.17 × | 0.001 | 0.999 | 7.5 × | 0.003 |
ATWWP002K12P | 9.31 × | 9.6 × | 0.999 | 5.8 × | 0.0031 | |
MAHS025K | 5.65 × | 7.5 × | 0.999 | 4.8 × | 0.0025 | |
MASR025K | 1.14 × | 0.001 | 0.999 | 7.32 × | 0.0059 | |
POP005L04P | 9.17 × | 9.5 × | 0.999 | 6.4 × | 0.0039 | |
POP500M24P | 1.25 × | 0.0011 | 0.999 | 8 × | 0.017 | |
POV002L09P | 9.55 × | 9.7 × | 0.999 | 7.1 × | 0.0028 | |
POV005L04P | 1.59 × | 1.26 × | 0.999 | 8.7 × | 0.0048 | |
POV500M24P | 1.36 × | 0.0011 | 0.999 | 8.4 × | 0.0044 | |
SOS250M48P | 1.1 × | 0.001 | 0.999 | 7 × | 0.0027 | |
SOS500M24P | 9 × | 9.5 × | 0.999 | 6.8 × | 0.0036 |
Model | Product ID | Best Hyperparameters 1 | Performance Metrics | ||||
---|---|---|---|---|---|---|---|
Score | |||||||
DTR | ATWWP001K24P | [8; 2; 4] | 0.011 | 0.108 | 0.988 | 0.048 | 0.19 |
ATWWP002K12P | [7; 3; 4] | 0.014 | 0.12 | 0.985 | 0.045 | 0.283 | |
MAHS025K | [8; 1; 6] | 0.005 | 0.076 | 0.994 | 0.032 | 0.118 | |
MASR025K | [5; 1; 2] | 0.015 | 0.123 | 0.984 | 0.087 | 0.55 | |
POP005L04P | [7; 1; 3] | 0.006 | 0.079 | 0.993 | 0.044 | 0.395 | |
POP500M24P | [6; 3; 6] | 0.04 | 0.204 | 0.958 | 0.132 | 0.83 | |
POV002L09P | [6; 2; 4] | 0.016 | 0.129 | 0.983 | 0.094 | 0.59 | |
POV005L04P | [6; 4; 4] | 0.021 | 0.147 | 0.978 | 0.088 | 0.386 | |
POV500M24P | [6; 2; 3] | 0.008 | 0.092 | 0.991 | 0.067 | 0.29 | |
SOS250M48P | [6; 3; 4] | 0.025 | 0.16 | 0.974 | 0.079 | 0.42 | |
SOS500M24P | [5; 3; 5] | 0.063 | 0.252 | 0.936 | 0.171 | 0.81 | |
RF | ATWWP001K24P | [8; 1; 2; 100] | 0.005 | 0.072 | 0.994 | 0.03 | 0.13 |
ATWWP002K12P | [5; 2; 2; 100] | 0.0135 | 0.116 | 0.986 | 0.049 | 0.29 | |
MAHS025K | [8; 1; 4; 100] | 0.004 | 0.07 | 0.995 | 0.034 | 0.12 | |
MASR025K | [8; 1; 2; 300] | 0.006 | 0.083 | 0.993 | 0.04 | 0.27 | |
POP005L04P | [8; 1; 5; 100] | 0.015 | 0.12 | 0.984 | 0.069 | 0.42 | |
POP500M24P | [8; 1; 3; 100] | 0.023 | 0.153 | 0.976 | 0.108 | 0.69 | |
POV002L09P | [8; 1; 3; 100] | 0.01 | 0.1 | 0.989 | 0.071 | 0.43 | |
POV005L04P | [7; 1; 2; 100] | 0.005 | 0.076 | 0.994 | 0.054 | 0.21 | |
POV500M24P | [8; 1; 3; 300] | 0.004 | 0.069 | 0.995 | 0.051 | 0.19 | |
SOS250M48P | [7; 1; 3; 100] | 0.011 | 0.107 | 0.988 | 0.057 | 0.27 | |
SOS500M24P | [6; 2; 5; 100] | 0.038 | 0.195 | 0.961 | 0.133 | 0.77 |
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Sayyad, J.; Attarde, K.; Yilmaz, B. Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks. Big Data Cogn. Comput. 2024, 8, 81. https://doi.org/10.3390/bdcc8080081
Sayyad J, Attarde K, Yilmaz B. Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks. Big Data and Cognitive Computing. 2024; 8(8):81. https://doi.org/10.3390/bdcc8080081
Chicago/Turabian StyleSayyad, Javed, Khush Attarde, and Bulent Yilmaz. 2024. "Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks" Big Data and Cognitive Computing 8, no. 8: 81. https://doi.org/10.3390/bdcc8080081
APA StyleSayyad, J., Attarde, K., & Yilmaz, B. (2024). Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks. Big Data and Cognitive Computing, 8(8), 81. https://doi.org/10.3390/bdcc8080081