Prof. Minghe Sun
The University of Texas at San Antonio, USA
Title: Sales Forecasting and Procurement Considering Supply and Demand Fluctuations: An Application to the Manufacturing Industry under Public Emergency
Abstract:
The impacts of a public emergency on the manufacturing industry are reflected in limited supplies of raw materials and rapidly changing demands of the final products. It is difficult for the traditional sales forecasting models to capture the non-stationary simultaneous supply and demand fluctuations in sales data. In order to help manufacturers effectively grasp the sudden changes in raw material procurement and in production and sales so as to enhance supply chain resilience, this work proposes an extended Bass model to consider the simultaneous supply and demand fluctuations caused by public emergencies such as the COVID-19 pandemic, and proposes a dynamic procurement framework by combining sale forecasting with multi-objective optimization. The real-world data of the automotive manufacturing industry in China during the COVID-19 pandemic are used to verify the effectiveness of the proposed methods. The experimental results show that the extended Bass model considering the simultaneous supply and demand fluctuations outperforms the standard Bass model and neural networks in sales forecasting. Based on the results of the extended Bass model, the raw material procurement decisions under the static and rolling optimization determined by the solution of the bi-objective optimization model are also investigated.
Keywords: Sales forecasting, Procurement decision, Supply chain resilience, Bass model, Multiobjective optimization
Biography:
MINGHE SUN received a Bachelor of Science degree in metallurgy from Northeastern University, China, in 1982, an MBA degree from the Chinese University of Hong Kong in 1987, and a Ph.D. degree in business administration with a major in management science and information
technology and a minor in operations management from the University of Georgia in 1992.
He has been working at the University of Texas at San Antonio since he received his doctorate. His research interests include data mining, machine learning, data analytics, mathematical programming, supply chain management and related areas, as well as their applications. His work has appeared in almost all major journals in the management science/operations research/decision sciences area including Management Science, Operations Research, Production and Operations Management, Decision Sciences, Transportation Science, INFORMS Journal on Computing, European Journal of Operational Research, Decision Support Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Journal of Heuristics, Omega, Annals of Operations Research, Networks, Journal of the Operational Research Society, Computers and Operations Research, Transportation Research Part E: Logistics and Transportation Review, Naval Research Logistics, International Journal of Production Research, among others. He received the prestigious Decision Sciences Institute Elwood S. Buffa Doctoral Dissertation Competition Award in 1993, and the Decision Sciences Institute Best Theoretical/Empirical Research Paper Award twice in 2003 and 2006, respectively. At the University of Texas at San Antonio, he teaches courses in management science/operations management and business statistics at all levels. He
received the University of Texas System Chancellor's Council Outstanding Teaching Award in 1999. He also received many institutional teaching, research and service awards.