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About Machine Learning in Supply Chain Forecasting
This document discusses using machine learning techniques like neural networks, recurrent neural networks, and support vector machines to forecast demand in a supply chain where information is distorted as it moves through different participants. It compares these advanced methods to more traditional approaches like moving averages and linear regression. The study uses simulated supply chain data and real orders data to test the forecasting accuracy of the different models. The findings suggest that while recurrent neural networks and support vector machines showed the best performance, their accuracy was not statistically significantly better than linear regression.
Detailed Information
| Author: | ['Karishma Khosla'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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