Essays on supply chain and healthcare analytics
Creators: name: Wang, Yijun
Publisher: name: No Publisher Supplied
Year: 2017

description: Abundant and real-time data from manufacturing and healthcare industries opens up  opportunities previously unimaginable to capture fast changing customer bases,  identify new sources of profit, and devise personalized medicine. This thesis consists  of three essays: the first essay develops a machine learning algorithm based on a  novel pharmacokinetic pharmacodynamic model for inpatients under warfarin therapy  to make personalized predictions on blood clotting times on a rolling horizon. The  second essay develops a set of methodologies to detect patterns in the online sales  data of a children-shoes manufacturer in China to predict price elasticity. The third  essay solves a challenging production scheduling problem in a three-stage supply  chain.    Specifically, the first essay designs a pharmacokinetic pharmacodynamic model  to make individualized and adaptive international normalized ratio (INR) predictions  for warfarin inpatients in changing clinical status. We tested a new model on 60  inpatients at Columbia Medical Center. The model personalizes four submodels and minimizes the number of parameters to be estimated. Prediction accuracy was  assessed by prediction error, absolute prediction error and percentage absolute  prediction error. The INRs (International Normalized Ratio, to measure blood clotting  times) were accurately predicted 5 days into the future. Median prediction error:  0.01–0.12; median absolute prediction error: 0.17–0.5 and median percentage  absolute prediction error: 9.85–26.06%. Patients exhibit interindividual and  intertemporal variability. The model captures the variability and provides accurate  and personalized INR predictions.    In the second essay, we study the problem of pricing and promotion for an online  seller of children shoes and try to estimate the price elasticity for different products at  different seasons. By testing the various models to capture the seasonality, price  elasticity and sales inertia, we may accurately forecast the sales lift for various price  discounts, so as to provide a foundation for data-driven price / promotion  optimization.    In the third essay, we study a three-stage supply chain, where the second stage  refers to the operations of external contracted manufacturers. The external  manufacturers each has multiple time windows in which the resources needed for the  outsourced operation are available. Both internal and external operations are  non-instantaneous, and the required processing times are subject to various resource  constraints. The problem is to assign and sequence a given set of customer orders to  both the internal and the external processes so the total tardiness in the order   fulfillment is minimized. We present mathematical models that define different  variations of this problem, analyze the special cases that can be solved in polynomial  times, and then develop heuristic algorithms that can be applied to quickly solve the  problems with a reasonable quality. In particular, we show that a heuristic solution  based on the 3D linear assignment algorithm has a potential to be an effective  approach for this type of problems.