Driving Digital Transformation in Quick Service Laboratory Supply Chains Through Statistical Anomaly Detection

ABSTRACT Quick Service Laboratories (QSL) provide the necessary diagnostic services that have to be performed within limited time frames and rely on coordinated solutions across its supply chain to operate successfully. The application of standard supply chain management approaches often fails to recognize the variable and unpredictable nature of QSL operations, which significantly contributes to stockouts, delays, or surplus inventory. This study looks into a different approach to the traditional methodologies of supply chain management by investigating the means when machine learning algorithms with the purpose of discovering anomalous behavior patterns are applied to QSL supply chain practices and generate value. In examining and evaluating the historical demand forecasting patterns, inventory levels, and operational performance metrics will be more easily identifiable as anomalous behaviors or dissenting levels such as demand spikes, unanticipated inventory shortfall levels, and atypical arrival patterns of inventory to generate disruption to laboratory operations. Machine learning models can be supervised or unsupervised to learn normal operation behaviors, and even detect anomalies in real time through model training. These models facilitate proactive interventions that would improve inventory management and distribution planning, as well as service delivery in general. When building on the results of our detection modeling, we found that machine learning anomaly detection could provide actionable suggestions and improved supply chain resiliency, and reduce stockouts and excess inventory, all while maintaining more controlled service levels. Our comparative evaluation of conventional monitoring and forecasting methods demonstrates superior capabilities over traditional methods in our results, by resorting to fully utilizing the complexity of simple linear and rare events found in QSL supply chains and their digital transformation story.