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A Neural Network For Collaborative Forecasting, Abdulrahman M. Enani
A Neural Network For Collaborative Forecasting, Abdulrahman M. Enani
Masters Theses
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 …
A Simulation Model Of The “Bio-Depot” Concept In The Context Of Components Of Variance And The “Taguchi Loss Function”, Maximilian Platzer
A Simulation Model Of The “Bio-Depot” Concept In The Context Of Components Of Variance And The “Taguchi Loss Function”, Maximilian Platzer
Masters Theses
The research focuses on the simulation, statistical evaluation, costs, and continuous improvement of supply chains for bio-based materials. A significant challenge of using cellulosic feedstocks for biofuel or bioenergy production is the high per unit costs of final products, e.g., biofuels. The goal of the research is to provide practitioners with useful statistical methods and a simulation Excel template for evaluating the variance and costs associated with the supply chain of bio-based products. Statistical Process Control (SPC), components of variance, Taguchi Loss Function, and reliability block diagrams (RBD) are used in this thesis for the evaluation of the supply chain …