Study of the Bullwhip Effect under Various Forecasting Methods in Electronics Supply Chain with Dual Retailers considering Market Share

We establish in this paper a new two-stage supply chain with one manufacturer and two retailers which have a fixed market share in the mature and stable market with specific reference to consumer electronics industry. This paper offers insights into how the three forecasting methods affect the bullwhip effect considering the market share under the ARMA(1,1) demand process and the order-up-to inventory policy. We also discuss the stability of the order with the theory of entropy. In particular, we derive the expressions of bullwhip effect measure under the MMSE, MA, and ES methods and compare them by numerical simulations. Results show that the MA is always better in contrast to the ES for reducing the bullwhip effect in our supply chain model. When moving average coefficient is lower than a certain value, the MMSE method is the best for reducing the bullwhip effect; otherwise, the MA method is the best. Moreover, the larger the market share of the retailer with a long lead time is, the greater the bullwhip effect is, no matter what the forecasting method is

Mitigating the Bullwhip Effect and Enhancing Supply Chain Performance through Demand Information Sharing: An ARENA Simulation Study

The supply chain is a network of organizations that collaborate and leverage their resources to deliver products or services to end-customers. In today's globalized and competitive market, organizations must specialize and form partnerships to gain a competitive edge. To thrive in their respective industries, organizations need to prioritize supply chain coordination, as it is integral to their business processes.   Supply chain management focuses on the collaboration of organizations within the supply chain. However, when each echelon member optimizes their goals without considering the network's impact, it leads to suboptimal performance and inefficiencies. This phenomenon is known as the Bullwhip effect, where order variability increases as it moves upstream in the supply chain. The lack of coordination, unincorporated material and information flows, and absence of ordering rules contribute to poor supply chain dynamics. To improve supply chain performance, it is crucial to align organizational activities. Previous research has proposed solutions to mitigate the Bullwhip effect, which has been a topic of intense study for many decades. This research aims to investigate the causes and mitigations of the Bullwhip effect based on existing research. Additionally, the paper utilizes ARENA simulation to examine the impact of sharing end-customer demand information. As far as we are aware, no study has been conducted to deeply simulate the bullwhip effect using the ARENA simulation. Previous studies have investigated this phenomenon, but without delving into its intricacies. The simulation results offer potential strategies to mitigate the Bullwhip effect through demand information sharing. Keywords: Supply Chain Management, Bullwhip effect, Inventory management, ARENA simulation, Information sharing, forecasting technique, Demand variability. DOI: 10.7176/JESD/14-14-07 Publication date:August 31st 202