How big data characteristics can help the manufacturing industry mitigate the bullwhip effect in their supply chain

Nyamukoroso, M

Abstract

For years, practitioners and academics have significantly studied the impact, causes, and remedies of the bullwhip effect in the supply chain. Numerous approaches have been developed throughout the years to help minimise demand amplification; these include order batching, the bear game, and demand forecasting. The bullwhip effect phenomenon is caused by numerous disruptions in the supply chain network, such as natural disasters, shortages, overproduction, overstocking of inventory, pandemics such as COVID-19, and political issues, for example, Brexit. This study examines the potential for big data to enhance supply chain procedures and decision-making to alleviate demand amplification. In addition, the study investigates how big data characteristics might be utilised in the manufacturing sector to reduce the situation. Numerous academic publications on big data and data analytics were evaluated critically to comprehend how big data has been utilised in the supply chain to mitigate the bullwhip impact.
The researcher has developed a Simulink model to examine the supply-chain system dynamics. The first model is generic and does not incorporate any big data properties; however, the other three models incorporate big data attributes, mathematical formulas, and other factors that can be modified during model execution. The model was repeatedly simulated with random or demand data. Simultaneously, results were collected and plotted on an Excel spreadsheet and other tools to generate factual data in graphs and numbers. Meaningful results or a quantitative research approach were employed to carry out the research, while a Simulink model was used as a primary research tool. Additionally, a model was employed to generate numerical data for analysis and to achieve study objectives. The outputs of each model were analysed since they all produce different results due to their varied incorporation of features. These results assist in identifying the most beneficial aspects of big data that have the potential to minimise the bullwhip effect.

Citation

Nyamukoroso, M. How big data characteristics can help the manufacturing industry mitigate the bullwhip effect in their supply chain. (Dissertation). University of Salford

• Thesis Type: Dissertation
• Deposit Date: Apr 12, 2023
• Publicly Available Date: Apr 12, 2023
• Award Date: Jan 26, 2022