Weakening the Bullwhip Effect in the Supply Chain Based on Data Fusion

Introduction

Under the economic policy guidelines of high-quality development, China should improve economic efficiency, change the traditional supply chain management model, and internalize industrial competitiveness. Demand forecasting is increasingly drawing attention from scholars and supply chain practitioners as an essential element in supply chain decision-making problems. The traditional supply chain includes raw material suppliers, manufacturers, distributors, retailers, etc. The analysis of customer demand should consist of the customer as a factor, so the above aspects need to be included in demand forecasting. Nevertheless, the supply chain demand is hardly steady and foreseeable, and the bullwhip effect is amplified demand. The bullwhip effect represents a needing in the supply chain. It seeks the phenomenon of variation and amplification so that when the information is transmitted from downstream consumers to the original upstream enterprises, it is impossible to effectively realize information sharing, which will cause information distortion and step-by-step amplification (Giri & Glock, 2021). Vast amounts of data have been generated from various devices, such as sensors, web data, smart devices, since information technology was dramatically implemented in the supply chain (Herlyn, 2014). In addition, combining the data and application, these generated data have profound impacts on supply chain management, presenting the opportunity to demand prediction based on data analysis (Herlyn, 2014). The amplification of demand information due to the bullwhip effect variation leads to overproduction, product backlogs, increased inventory and production costs, resulting in uneven distribution of corporate resources, increasing the risk of corporate operations, reducing corporate competitiveness, and detrimental to the entire supply chain development. Many scholars and supply chain participants have devoted their attention to studying supply chain relative questions. To treat the problem of prediction and amplification, some scholars and members of the supply chain put forward a concept, named supply chain collaboration, to improve supply chain performance and create value and advantages for members of the supply chain (Hazen et al., 2014). However, there is little research about utilizing data technology to improve the efficiency of sharing information, and most scholars believe that reducing the level in the supply chain can effectively bring down the impact of the bullwhip effect.

This study is theoretical research, assuming a new model for supply chain operation, using data fusion under data exchanged, weakening the bullwhip effect, and improving the effective and efficient demand forecasting in supply chain management. Essentially, the bullwhip effect is a demand forecast deviation caused by information dissymmetry (Lee et al., 1997). It is crucial to introduce digital technology to the supply chain to strengthen the information interaction among members. In addition, for utilizing this equipment of digital technology, it is required mutual trust in a complex environment (Babar & Mahalle 2021). The data generated in the supply chain comes from sensors of various types and levels, to create a matrix for utilizing data reasonably (Bejaoui et al., 2019). Moreover, in this process, a data has different characteristics, and these characteristics are not all useful (Shadrach et al., 2022).