Stochastic demand forecast and inventory management of a seasonal product a supply chain system

Estimation of seasonal demand prior to an active demand season is essential in supply chain management. The business cycle of the seasonal demand is divided into two stages: stage-1, the slow-demand period, and stage-2, the peak-demand period. The focus here is to determine an appropriate demand forecast for the peak-demand period. In the first set of forecasting model, a standard gamma and an inverse gamma prior distribution are used to forecast demand. The parameters of the prior model are estimated and updated based on current observation using Bayesian technique. The forecasts are derived for both complete and incomplete datasets. The second set of forecast is derived by ARIMA method using Box-Jenkins approaches. A Bayesian ARIMA is proposed to forecast demand from incomplete dataset. A partial dataset of a seasonal product, collected from the US census bureau, is used in the models. Missing values in the dataset often arise in various situations. The models are extended to forecast demand from an incomplete dataset by the assumption that the original dataset contains missing values. The forecast by a multiplicative exponential smoothing model is used to compare all the forecast. The performances are tested by several error measures such as relative errors, mean absolute deviation, and tracking signals. A newsvendor inventory model with emergency procurement options and a periodic review model are studied to determine the procurement quantity and inventory costs. The inventory cost of each demand forecast relative to the cost of actual demand is used as the basis to choose an appropriate forecast for the dataset. This study improves the quality of demand forecasts and determines the best forecast. The result reveals that forecasting models using Bayesian ARIMA model and Bayesian probability models perform better. The flexibility in the Bayesian approaches allows wider variability in the model parameters helps to improve demand forecasts. These models are particularly useful when past demand information is incomplete or limited to few periods. Furthermore, it was found that improvements in demand forecasting can provide better cost reductions than relying on inventory models

Supply chain models for an assembly system with preprocessing of raw materials

An assembly line that procures raw materials from outside suppliers and processes the materials into finished products is considered in this research. An ordering policy is proposed for raw materials to meet the requirement of a production facility, which, in turn, must deliver finish products in a fixed quantity at a fixed time interval to the outside buyers. Two different types of raw materials, ‘unfinished’ and ‘ready-to-use’, are procured for the manufacturing system. The ‘unfinished raw materials’ are turned into ‘processed raw materials’ after preprocessing. In the assembly line, the ‘processed raw materials’ and the ‘ready raw materials’ are assembled to convert into the final products. A cost model is developed to aggregate the total costs of raw materials, Work-in-process, and finished goods inventory. Based on the product design and manufacturing requirement a relationship is established between the raw materials and the finished products at different stages of production. A non-linear integer-programming model is developed to determine the optimal ordering policies for procurement of raw materials, and shipment of assembly product, which ultimately minimize the total costs of the model. Numerical examples are presented to demonstrate the solution technique. Sensitivity analysis is performed to show the effects of the parameters on the total cost model. Future research direction is suggested for further improvement of the existing results

ACKNOWLEDGEMENTS

I would like to thank my Professor Bhaba R. Sarker, chairman of my doctoral committee, for his invaluable support, advice, and encouragement in bringing this research work to a successful completion. He has taught me a great many things, guided me as to how to deal with new problems. I would also like to express my gratitude to all members of my dissertation committee

ACKNOWLEDGEMENTS

I would like to express my gratitude to Dr. Bhaba R. Sarker for his guidance and support throughout this work. It has been a very rewarding experience working with him. His insights into issues other than the thesis have also been very useful. I would also like to thank Dr. Dennis Webster for providing valuable advice. His comments and suggestions have certainly improved the quality of this work. I would like to thank him, Dr. Lawrence Mann, Jr. for assisting and serving on my committee. Finally, special thanks to my parents, teachers, religious teachers, friends for their constant encouragement and best wishes. I also would like to thank my wife Asheka for her constant help and encouragement and who made my stay in USA a pleasant one. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS … … … … … … … … … … … … … … i