The techniques of data mining allow for accurate forecasting of the future by analyzing data from the past. Data Mining is a process that sorts through enormous volumes of data in order to unearth previously unknown information and connections. Data mining has made it easier to implement mass customization and personalization systems. Download project on it enabled fuzzy network scm network data mining.
It has also been used to detect supply chain scam in e-procurement transactions, reduce the supply chain risk by analyzing public data information, and improve the financial performance of corporations by optimizing the use of supply chain data maintained in data centers. Data mining has had a significant impact on supply chain management. The information on choices that is often provided by experts is sometimes vague, which may make it difficult to make judgments.
In this line, a large number of researchers have used fuzzy methodologies to find solutions to difficulties involving decision making in SCM. Even if there were review articles regarding either fuzzy methods or SCM, the vast majority of them did not employ bibliometrics approaches, and they did not take into account fuzzy sets theory-based procedures in SCM in a thorough manner.
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- Accelerating business operations and removing bottlenecks are two of the primary responsibilities of information technology in supply chain management.
- Especially in manufacturing, businesses are getting closer and closer to their goals of achieving on-time procurement, reduced inventory, and improved efficiency.
- The use of information technology is enabling businesses in the supply chain to better satisfy the requirements of end customers.
- The processing of variables may be done using a method called fuzzy logic, which allows for numerous potential truth values to be processed by the same variable at the same time.
- In contemporary data mining initiatives, a variety of important data mining methods, such as association, classification, clustering, prediction, sequential patterns, and regression, have been created and deployed.
Utilizing a fuzzy network model to evaluate the responsiveness of SCM is recommended. The literature analysis led us to discover that two studies had offered a fuzzy network model as a means to evaluate risk resilience ability and measure the performance of agility in a SC. This is a very unique approach from which to investigate SCM issues. Data mining is not like other statistical approaches in the sense that it processes a much larger volume of information.
CONCLUSION OF IT-ENABLED SCM, FUZZY MODELLING, DATA MINING
Conclusion reached to Data mining has also been shown to have a great potential in the identification of fraud in the supply chain, the improvement of the financial performance of the organization as a result of better use of assets, and the reduction of the risk of the supply chain management.
We have included the idea of dynamic supply chain information flows into e-supply chain management as a response to the wide variety of functionality and level of complexity shown by supply chain management systems. The pre-processing of customer attribute values may be made easier with the help of an agent, which was created to do both of these things.
|Project Name||: IT-enable SCM, Fuzzy Modeling, Data Mining|
|Project Category||: PHD Dissertation|
|Pages Available||: 55-65/pages|
|Project PPT cost||: Rs 500/ $10|
|Project Synopsis||: Rs 500/ $10|
|Project Cost||: Rs 1750/$ 30|
|Delivery Time||: 24 Hours|
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