Optimization of Forecasting Supply Chain Management Sustainable Collaboration Using Hybrid Artificial Neural Network

Artificial Neural Network (ANN) is widely used in business to optimize forecasting. Various techniques have been developed to improve outcomes such as adding more diverse algorithms, feature selection and feature weighting in input variables, and modification of input case using instance selection. In this research, ANN is applied to solve problems in forecasting a Supply Chain Management (SCM) sustainable collaboration. This research compares the performance of forecasting SCM sustainable collaboration with four types of ANN models: COANN (COnventional ANN), FWANN (ANN with Feature Weighting), FSANN (ANN with Feature Selection), and HYANN (HYbrid ANN with Feature Weighting and Feature Selection). Using HYANN to forecast an SCM sustainable collaboration gave the best results.

[1]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[2]  R. Kaplan,et al.  The balanced scorecard--measures that drive performance. , 2015, Harvard business review.

[3]  Peter C. Brewer,et al.  USING THE BALANCED SCORECARD TO MEASURE SUPPLY CHAIN PERFORMANCE. , 2000 .

[4]  Marjorie B. Platt,et al.  Probabilistic Neural Networks in Bankruptcy Prediction , 1999 .

[5]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[6]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[7]  Kurt Fanning,et al.  A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction , 1994 .

[8]  Ingoo Han,et al.  Hybrid Genetic Algorithms and Case-Based Reasoning Systems , 2004, CIS.

[9]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[10]  Hans-Peter Kriegel,et al.  Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach* , 2003, Knowledge and Information Systems.

[11]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[12]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[13]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.