An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Mehtodology
3. Results
3.1. Feature Selection
- national inv
- lead time
- in transit qty
- forecast_3_month
- forecast_6_month
- forecast_9_month
- sales_1_month
- sales_3_month
- sales_6_month
- sales_9_month
- min bank
- pieces past due
- perf_6_month_avg
- perf_12_month_avg
- local bo qty
- deck risk
- ppap risk
3.2. Classification
3.2.1. Validation
3.2.2. Calibration
3.3. Explainability
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Name | Description | Type |
---|---|---|
national inv | Current inventory level of component | Numerical |
lead time | Transit time | Numerical |
in transit qty | Quantity in transit | Numerical |
forecast x month | Forecast sales for the net 3, 6, 9 months, where x represents the months | Numerical |
sales x month | Sales quantity for the prior 1, 3, 6, 9 months, where x represents the months | Numerical |
min bank | Minimum recommended amount in stock | Numerical |
potential issue | Indictor variable noting potential issue with item | Categorical |
pieces past due | Parts overdue from source | Numerical |
perf x months avg | Source performance in the last 6 and 12 months, where x represents the months | Categorical |
local bo qty | Amount of stock orders overdue | Numerical |
X17–X22 | General Risk Flags | |
deck risk, oe constraint, ppap risk, stop auto buy, rev stop | Different Flags (Yes or No) set for the product | Categorical |
went on back order | Product went on backorder | Categorical |
Classifiers | Hyperparameters |
---|---|
RF | criterion: [gini, entropy], n estimators: [10, 15, 20, 25, 27, 30], min samples leaf: [1, 2, 3, 4, 5], min samples split: [2, 3, 4, 5, 6, 7] |
KNN | n neighbors: [3, 4, 5, 7, 9, 12, 14, 15, 16, 17], leaf size: [1, 2, 3, 5], weights: [uniform, distance], algorithm: [auto, ball tree, kd tree, brute] |
NN | hidden layer sizes: [(2, 5, 10), (5, 10, 20), (10, 20, 50)], activation: [tanh, relu], solve: [sgd, adam], alpha: [0.0001, 0.05], learning rate: [constant, adaptive] |
RL | penalty = [11, 12], C: [0, 1, 2, 4, 6, 8, 10] |
SVM | C: [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], kernel: [linear,sigmoid,rbf,poly] |
XGB | max depth: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], min child weight: [1, 2, 3, 4, 5, 6, 8, 10], gamma: [0, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1] |
LGBM | n estimators: range (200, 600, 80), num leaves: range (20, 60, 10) |
BB | n estimators: [10, 50, 100, 300, 500, 1000, 1100, 1200, 1300, 1400, 1500] |
Classifiers | Accuracy (%) | Recall (%) | F1-Score (%) | Precision (%) | Confusion Matrix | Hyperparameters | ||
---|---|---|---|---|---|---|---|---|
RF | 88.82 | 89.94 | 89.01 | 88.10 | 0 | 1 | Criterion = entropy, min samples leaf = 1, min samples split = 5, n estimators = 30 | |
0 | 2432 | 342 | ||||||
1 | 283 | 2531 | ||||||
KNN | 75.93 | 79.82 | 76.96 | 74.30 | 0 | 1 | algorithm = auto, leaf size = 1, n neighbors = 3, weights = distance | |
0 | 1997 | 777 | ||||||
1 | 568 | 2246 | ||||||
NN (MLP) | 85.68 | 85.54 | 85.75 | 85.96 | 0 | 1 | activation = tanh, alpha = 0.0001, hidden layer sizes = (10, 20, 50), learning rate = constant, solver = adam | |
0 | 2381 | 393 | ||||||
1 | 407 | 2407 | ||||||
LR | 70.22 | 74.09 | 71.48 | 69.04 | 0 | 1 | penalty = l2, C = 10.0 | |
0 | 1839 | 935 | ||||||
1 | 729 | 2085 | ||||||
SVM | 72.39 | 85.86 | 75.80 | 67.85 | 0 | 1 | C = 15, kernel = rbf | |
0 | 1629 | 1145 | ||||||
1 | 398 | 2416 | ||||||
XGBoost | 88.53 | 90.26 | 88.80 | 87.38 | 0 | 1 | gamma = 0.7, max depth = 9, min child weight = 1 | |
0 | 2407 | 367 | ||||||
1 | 274 | 2540 | ||||||
LightGBM | 87.78 | 89.02 | 88.00 | 87.01 | 0 | 1 | n estimators = 520, num leaves = 50 | |
0 | 2400 | 374 | ||||||
1 | 309 | 2505 | ||||||
BB | 88.85 | 90.69 | 89.12 | 87.61 | 0 | 1 | n estimators = 1100 | |
0 | 2413 | 361 | ||||||
1 | 262 | 2552 |
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Ntakolia, C.; Kokkotis, C.; Karlsson, P.; Moustakidis, S. An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management. Sensors 2021, 21, 7926. https://doi.org/10.3390/s21237926
Ntakolia C, Kokkotis C, Karlsson P, Moustakidis S. An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management. Sensors. 2021; 21(23):7926. https://doi.org/10.3390/s21237926
Chicago/Turabian StyleNtakolia, Charis, Christos Kokkotis, Patrik Karlsson, and Serafeim Moustakidis. 2021. "An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management" Sensors 21, no. 23: 7926. https://doi.org/10.3390/s21237926
APA StyleNtakolia, C., Kokkotis, C., Karlsson, P., & Moustakidis, S. (2021). An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management. Sensors, 21(23), 7926. https://doi.org/10.3390/s21237926