Machine Learning for Improving Detection of Cooling Complications : A case study

Abstract: The growing market for cold chain pharmaceuticals requires reliable and flexible logistics solutions that ensure the quality of the drugs. These pharmaceuticals must maintain cool to retain the function and effect. Therefore, it is of greatest concern to keep these drugs within the specified temperature interval. Temperature controllable containers are a common logistic solution for cold chain pharmaceuticals freight. One of the leading manufacturers of these containers provides lease and shipment services while also regularly assessing the cooling function. A method is applied for detecting cooling issues and preventing impaired containers to be sent to customers. However, the method tends to miss-classify containers, missing some faulty containers while also classifying functional containers as faulty. This thesis aims to investigate and identify the dependent variables associated with the cooling performance, then Machine Learning will be performed for evaluating if recall and precision could be improved. An improvement could lead to faster response, less waste and even more reliable freight which could be vital for both companies and patients. The labeled dataset has a binary outcome (no cooling issues, cooling issues) and is heavily imbalanced since the containers have high quality and undergo frequent testing and maintenance. Therefore, just a small amount has cooling issues. After analyzing the data, extensive deviations were identified which suggested that the labeled data was misclassified. The believed misclassification was corrected and compared to the original data. A Random Forest classifier in combination with random oversampling and threshold tuning resulted in the best performance for the corrected class labels. Recall reached 86% and precision 87% which is a very promising result. A Random Forest classifier in combination with random oversampling resulted in the best score for the original class labels. Recall reached 77% and precision 44% which is much lower than the adjusted class labels but still displayed a valid result in context of the believed extent of misclassification. Power output variables, compressor error variables and standard deviation of inside temperature were found clear connection toward cooling complications. Clear links could also be found to the critical cases where set temperature could not be met. These cases could therefore be easily detected but harder to prevent since they often appeared without warning.