Predicting the Impact of Supply Chain Disruptions Using Statistical Analysis and Machine Learning

Detta är en Master-uppsats från KTH/Matematisk statistik

Sammanfattning: The dairy business is vulnerable to supply chain disruptions since large safety stocks to cover up losses are not always a viable option, therefore it is crucial to maintain a smooth supply chain to ensure stable delivery accuracies. Disruptions are unpredictable and hard to avoid in the supply chain, especially in cases where production errors cause lost production volume. This thesis proposes the use of machine learning and statistical modelling together with data from Arla to predict when a shortage will occur and its duration to allow proactive decision making to mitigate the consequences of the disruption. The aim of this thesis is to create one predictive model for delay and one for duration based on data from multiple products and explore how the features and methods used can capture the product specific characteristics in the data and thereupon improve the models. The model used for evaluating these factors was a random forest classifier, and permutation feature importance was used to determine the relevant features for the models. The issue of having imbalanced data was handled by first grouping the data and then applying the oversampling method SMOTE. The two models were trained on different datasets where the duration model was trained on all disruptions and the delay model was only trained on a subset were a shortage have occurred. One finding was that applying SMOTE yielded the best results. The best duration model had an accuracy of 62% with precision and recall of 79% and 76% respectively for the majority class, but very low for the other classes with a combined average of 21% and 24%. The most important feature for the duration was the the quotient describing the lost production. The best delay model had an accuracy of 62% with more accurate predictions over all classes and an average precision and recall of 59% and 57%. The most important feature for the delay was how often a product is produced.

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