数产供应商详情分析数据集
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Data Collection: Collect various indicator data related to suppliers via automated data scraping tools or manual entry, including price, quality, on-time delivery rate, supply flexibility, delivery frequency, minimum batch size, supply quality, transportation quality, etc. The data sources are the enterprise's ERP system, supply chain management system, or other relevant transaction records. Data Cleaning and Processing: Preprocess the collected data, including removing duplicate data, correcting erroneous data, filling missing values, etc. Convert non-numeric evaluations into quantitative indicators; for example, convert star ratings into specific scores. For indicators of different dimensions with different measurement units or scales, perform normalization processing to enable comparison on a unified scale. Data Analysis Model Construction: Design a weighted summation model using machine learning algorithms (Long Short-Term Memory (LSTM) neural network). Assign different weights to each dimension, multiply each supplier's scores across all dimensions by their corresponding weights, then sum the products to obtain the supplier's comprehensive capability score. Radar chart analysis model: Create a radar chart where each vertex represents one dimension, and the connections between vertices form a closed area. A larger area indicates stronger comprehensive capability of the supplier in all aspects. Ranking analysis model: Rank suppliers based on their comprehensive capability scores to generate a ranking list. Implement a dynamic adjustment mechanism: Set up a periodic evaluation and update mechanism, and recalculate the comprehensive capability scores and rankings of all suppliers whenever new evaluation data is received. Result Evaluation and Feedback: Conduct data visualization processing, use visualization tools to present the data, and review the final results to ensure no important information is omitted and no obvious deviations occur.
