Supplier Score Model Based on Pearson Correlation Coefficient and Entropy Weighting Method
DOI:
https://doi.org/10.54097/6d2x1n98Keywords:
Pearson correlation analysis; entropy method; multi-objective planning models.Abstract
For the problem of ordering and transportation of raw materials in production enterprises, a comprehensive score of supplier supply characteristics is established based on the entropy value method, and a mathematical model reflecting the importance of guaranteeing the production of enterprises is established. Firstly, based on the raw material suppliers' supply data, we get the better 60 suppliers according to the total supply; secondly, secondly, based on the supply and ordering data, we excavate the indexes to measure the supply characteristics of each supplier, and carry out the Pearson correlation analysis, and get the four indexes which are weakly correlated with the type of materials supplied by the suppliers, order fulfillment, stability of supply, and enterprise purchasing. Finally, the entropy value method is used to determine the weight of each indicator, to construct the indicator system reflecting the importance of safeguarding the production of the enterprise, to obtain the comprehensive score of the supply characteristics of the 60 suppliers after screening, and to determine the top 50 suppliers such as S361, S218, etc. as the most important suppliers according to the descending order of the score.
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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.
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