Table 9: Traversal distance comparison on MSE, wMSE and lMSE in all settings Sequence Loss function Mean ladder distance
RN Nall
4.2 Comparison on lMSE and wMSE in fluctuating and stable items
To further compare the di↵erence between wMSE and lMSE, we select clusters with stable trajectories movement (std lower than 25%) and clusters with fluctuating trajectories (std higher than 75%) and we employ di↵erent RNN models on these data. In this setting, we have 26 unstable trajectory clusters with more than 600 items and 26 stable trajectory clusters with more than twelve thousand items. Tab. 10 shows the result on traversal distance and Tab. 11 shows the accuracy prediction throughout di↵erent layers.
As we can see in the experiment, lMSE significantly out perform wMSE on high process variability scenario. Both wMSE and lMSE prediction are almost same in low variability scenario.
4.3 Trajectory prediction
Once the RNN model has been trained, it can be used to predict the future trajectory movement. We feed [c0, d0] to [cn 2, dn 2] and [c1, d1] to [cn 1, dn 1] to train our model.
Then, we put [c2, d2] to [cn, dn] to predict the [cn+1, dn+1]. After our model generates new
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Table 10: Traversal distance comparison on high and low trajectory clusters
Sequence Loss function Mean ladder distance high low
Table 11: Accuracy comparison on high and low trajectory clusters
Sequence Variability Loss Layer accuracy l1 l1, l2 l1 l3 d
RN Nall
High wM SE 0.37 0.16 0.07 25.31
lM SE 0.49 0.24 0.17 14.88
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prediction, we feed it back to create two steps ahead prediction. We can keep feeding our prediction value to our RNN model. Fig. 8 shows the movement of item-A trajectory that generates five steps ahead prediction. The prediction [p1, p2, p3, p4, p5] is [0, [2, 3, 4], [2, 3, 2], 0, [2, 3, 3], [2, 3, 1], 0, [2, 3, 3]. The prediction of the item movement provides managers insights on SCM process variability at the item level and facilitates dynamic adjustment strategies on item-level demand and inventory management.
Figure 8: Item-A 5 steps trajectories prediction
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5 Conclusion
We propose a novel prediction model that combines unsupervised clustering and sequence prediction techniques to address the problem of SCM process variability forecasting with real-world transaction data. We focus on the development of an e↵ective cluster forecast-ing model for a company which has many up and downstream customers. We treat the GHSOM results as our RNN model inputs, which is di↵erent from many previous studies that use GHSOM to preprocess data and propose an e↵ective encoding and loss function of the RNN model to improve the accuracy. The prediction on item-level demand and inventory trajectories can help managers to face irregular demand patterns and adjust their operation strategies dynamically.
Acknowledgement
This project is funded in part by MOST 106-2221-E-004-002- and MOST 105-2923-E-002-016-MY3.
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