Planning the infinite varieties! Big Data Analytics for Developing Hedge Strategies of Raw Material Procurement in Building Information Model

Abstract

Procurement hedge strategies is a key factor for survival in rapid circulation of logistics gold flow and information flow in the manufacturing industry where the original cost accounts for the largest proportion of the overall company's operating cost The enterprise resource planning (ERP) and material resource planning (MRP) system and its expanded bills of materials (BOM) are the key methods for controlling the procurement costs of raw material In large-scale manufacturing the Building Information Modeling (BIM) are deployed in layout process and the bill of materials for raw material cost control are deployed at the same time Specially the unit price of materials is established with the BIM system is completed in the initial stage But the prices of raw materials fluctuate with the fluctuation of the international economy at the correct time for material demand The impact of price fluctuations in related raw material may far exceed the evaluation with BOM Therefore the mastery of price fluctuation in raw material and the prediction of price fluctuation become the key tasks of hedging procurement Since copper is the main material for a factory in layout phase This study aims to predict the price of copper using big data analysis develop the hedging procurement strategies feedback the strategies to the BIM system for smart purchasing This study also adopts case study to verify the model in practice The results show that the model for predicting the price of copper trained with big data has a good fitness with actual data and it is helpful to develop the hedging procurement strategies In practice the hedging procurement strategies are helpful to the company for reducing the risk cost and lifting the profits in operation with the case study

Date of Award	2020
Original language	English
Supervisor	Sheng-Tun Li (Supervisor)