Sam’s Club Analysis Based on the SWOT, PEST and the Porter’s Five Force Model
DOI:
https://doi.org/10.54691/bcpbm.v44i.4820Keywords:
Sam’s Club; social media platform; e-commerce.Abstract
This paper will describe the business analysis of Sam's Club in China. Due to the impact of COVID-19, China has a large sales market and is an indispensable resource for Sam's Club. With the general economic downturn in China in recent years, Sam's Club's profitability has been unstable and its revenue has fluctuated in this context. This paper will use the SWOT model and PEST model to analyze the current situation of the company from four factors. The Porter 5 force analysis was used to analyze the competitive environment of Sam's Club to understand the situation of other businesses in the same industry. This paper mainly wants to talk about how, to be a market leader and hold a position in the same industry, one must be a first mover and transition to a market segment with more room for growth, which in turn serves to revitalize the growth potential of the business and the business economy.
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Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
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