Tracking Supply Chain Process Variability with Unsupervised Cluster Traversal

Supply chain processes need stability and predictability for the supply to better match demand at the right time with the right quantity. Reaching stable operations under uncertainty, however, is challenging as fluctuating demand patterns in the downstream are so common and make inventory control at the upstream a daunting task. Working with one of the leading semiconductor distributors in the world, who piles up stock that hampers profitability for the sake of satisfying lumpy/erratic demand in the downstream production plants, we help the distributor track process variability in its operations. Specifically, we integrate unsupervised clustering with the recurrent neural network for tracking supply chain process variability without pre-assumptions on demand patterns. We first apply unsupervised learning techniques to characterize weekly process performance of a wide variety of electronic items, where item-week pairs that have relatively-high similarity on values of demand and stock attributes are clustered together. The operational variability of each item can then be measured with the trajectory of the item on its clusters ordered by time. To predict the trajectory of how each item moves from week to week, we propose a new cluster sequence encoding and employ the recurrent neural network structure for sequence prediction. We show that with a training loss function tailored to our encoding scheme, the presented approach can achieve high accuracy on variability prediction for real-world data. Since any upstream supply operations are driven by downstream demand patterns, the prediction on items' operational variability may help suppliers to better prepare for demand irregularities by dynamically adjusting their operations strategies, e.g., altering throughput rates, rescheduling deliveries, increasing/decreasing fulfillment frequencies, etc.