Data science and reinforcement learning for price forecasting and raw material procurement in petrochemical industry
Journal
Advanced Engineering Informatics
Journal Volume
51
Date Issued
2022
Author(s)
Abstract
Petrochemical industry is one of the major sectors contributing to the world-wide economy and the digital transformation is urgent to enhance core competence. In general, ethylene, propylene and butadiene, which are associated with synthetic chemicals, are the main raw materials of this industry with around 70–80% cost structure. In particular, butadiene is one of the key materials for producing synthetic rubber and used for several daily commodities. However, the price of butadiene fluctuates along with the demand–supply mismatch or by the international economy and political events. This study proposes two-stage data science framework to predict the weekly price of butadiene and optimize the procurement decision. The first stage suggests several the price prediction models with a comprehensive information including contract price, supply rate, demand rate, and upstream and downstream information. The second stage applies the analytic hierarchy process and reinforcement learning technique to derive an optimal policy of procurement decision and reduce the total procurement cost. An empirical study is conducted to validate the proposed framework, and the results improve the accuracy of price forecasts and the procurement cost reduction of the raw materials. ? 2021 Elsevier Ltd
Subjects
Deep learning
Digital transformation
Price forecasting
Raw material procurement
Reinforcement learning
Butadiene
Cost reduction
E-learning
Ethylene
Industry 4.0
Metadata
Petrochemicals
Rubber
Core competence
Material procurement
Petrochemical industry
Procurement costs
Procurement decisions
Synthetic chemicals
Forecasting
SDGs
Type
journal article