Masters Theses

Date of Award


Degree Type


Degree Name

Master of Science


Industrial Engineering

Major Professor

Xueping Li

Committee Members

Mingzhou Jin, Rapinder Sawhney


As the supply chain activities’ backbone, demand forecasting must be accurate. This paper proposes an artificial neural network forecasting model, which integrates and synchronizes shared information, such as sales or consumption rate among different partners, to improve the forecasting’s accuracy. This information sharing is part of the collaborative planning, forecasting and replenishment (CPFR) model, which is a supply chain model aiming to enhance the supply chain’s efficiency by jointly planning and forecasting between two or more supply chain partners that will be used as the base for production and replenishment activities. The model is validated using a tuna product sales data, and the combination of individual forecasts resulted in better demand forecasting accuracy for the supply chain. This improvement will lead to reduced costs associated with the forecast’s overestimation or underestimation.

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