Paper Info
Title
Learning approaches for developing successful seller strategies in dynamic supply chain management
Abstract
Variable, dynamic pricing is a key characteristic of the modern electronic trading environments, allowing for prices that change or fluctuate due to uncertainty and different conditions and context. Being able to manage dynamic pricing strategies is vital for companies wishing to succeed in the world of modern business. The ability to accurately predict selling prices at a given time can help organizations to maximize their profit. This paper addresses the problem of predicting customer order prices and choosing the selling strategy which can lead to a greater profit in the context of supply chain management (SCM). The potential of the Neural Networks (NN) and Genetic Programming (GP) learning techniques is explored for making price forecasts. In particular, different parameter settings and methods for preprocessing input data are investigated in the paper. Although, both techniques showed the potential for dealing with the problem of dynamic pricing in SCM, NN models outperform GP models in the context under consideration in terms of accuracy of prediction, complexity of implementation, and execution time.
Year
DOI
Venue
2011
10.1016/j.ins.2011.04.014
Inf. Sci.
Keywords
Field
DocType
greater profit,different condition,execution time,nn model,dynamic pricing,different parameter setting,modern electronic trading environment,successful seller strategy,dynamic pricing strategy,dynamic supply chain management,gp model,modern business,supply chain management,profitability,neural network
Dynamic pricing,Operations research,Genetic programming,Supply chain management,Preprocessor,Artificial intelligence,Execution time,Electronic trading,Artificial neural network,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
181
16
0020-0255
Citations 
PageRank 
References 
10
0.53
40
Authors
2
Name
Order
Citations
PageRank
Maria Fasli132646.22
Yevgeniya Kovalchuk2389.76