Generating decision support for alarm processing in cold supply chains using a hybrid k-NN algorithm

Abstract

Real-time temperature monitoring is necessary in cold pharmaceutical supply chains (SCs), where exposures to extreme temperatures can lead to product quality deterioration. Temperature alarms (TAs) triggered by the current rule-based systems still require lengthy examinations before a suitable corrective measure (CM) can be chosen. However, provision of additional information relevant to TAs can expedite the examination process.In the related areas of recommender systems and false alarm/anomaly detection, k-nearest neighbors (k-NN) algorithm has proven to be successful because of its interpretability and ease of use. However, in the context of TA processing, it may suffer from some inherent limitations (i.e., varying neighborhood radius, unreliable classifications in sparse and noisy regions, and blindness to natural class boundaries). To overcome these limitations, we propose a hybrid k-NN (Hk-NN) algorithm based on the principles of local similarity and neighborhood homogeneity. It incorporates a two-step voting procedure with an entropy-optimized k-NN radius, decision trees with k-constrained leaves, and nearest neighbor predictions.We investigate 16,525 comments by alarm personnel for TAs in a pharmaceutical SC and encode them in terms of deviation causes and CMs (target features). We use SC data on cargo location, SC phase, sensor role, and temperature characteristics as predictor features for TA similarity estimation. In eight experimental setups, Hk-NN consistently outperforms k-NN with an optimized k in terms of accuracy, balanced accuracy, macro-average precision, recall, and specificity. At the same time, Hk-NN refrains from predicting observations, for which k-NN’s accuracy is close to a random guess.