A Computational Intelligence Approach to Supply Chain Demand Forecasting

2011 ◽  
pp. 110-124
Author(s):  
Nicholas Ampazis
Keyword(s):  
Supply Chain ◽  
Computational Intelligence ◽  
Demand Forecasting ◽  
Business Environment ◽  
Chain Structure ◽  
Support Vector ◽  
Customer Demand ◽  
Public Data ◽  
Vector Machines ◽  
Supply Chain Structure

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

A Computational Intelligence Approach to Supply Chain Demand Forecasting

2012 ◽  
pp. 1551-1565 ◽  
Author(s):  
Nicholas Ampazis
Keyword(s):  
Supply Chain ◽  
Computational Intelligence ◽  
Demand Forecasting ◽  
Business Environment ◽  
Chain Structure ◽  
Support Vector ◽  
Customer Demand ◽  
Public Data ◽  
Vector Machines ◽  
Supply Chain Structure

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

Forecasting Demand in Supply Chain Using Machine Learning Algorithms

2015 ◽  
Vol 5 (1) ◽  
pp. 56-73 ◽  
Author(s):  
Nicholas Ampazis
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Learning Algorithms ◽  
Chain Structure ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Sources Of Information ◽  
Public Data ◽  
Supply Chain Structure

Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks (ANNs) trained with an effective second order algorithm, and Support Vector Machines (SVMs) for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews (Rotten Tomatoes), and a movie oriented social network (Flixster). Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.

scholarly journals Intelligent Method of Supply Chain Circulation Industry Structure Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
JingFei Ran
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Real Data ◽  
Chain Structure ◽  
Percentage Error ◽  
Support Vector ◽  
Data Simulation ◽  
Average Percentage ◽  
Four Levels ◽  
Supply Chain Structure

In the deepening of supply chain competition, whether the structure of supply chain industry is reasonable and scientific has been severely tested. For warehousing, purchase and distribution channels, and customers, it largely determines whether the structure of supply chain is stable and efficient. The rationality of structure can determine the value of supply chain. By analyzing these four levels, this paper judges whether the supply chain structure is reasonable; the judgment standard is based on the three popular machine learning models, Stochastic Forest, XGBoost, and Support Vector Machine. The three models are based on a large number of real data environments. Through data simulation and parameter optimization, four supply chain characteristics are put into the model for simulation training for many times, and the three error numbers of MAE, RMSE, and MAPE of the model are analyzed to judge the reliability of the model. On this basis, through the combination of models, it is determined that the average percentage error of the combination of the three models is higher than that of the other pairwise combinations, reaching 0.937, which completes the expectation of intelligent prediction of supply chain structure.

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