Learning Approaches for Developing Successful Seller Strategies in Dynamic Supply Chain Management

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 a key ability for companies wishing to succeed in the world of modern business. The ability to predict selling prices at a given time accurately 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 studied in the paper. Both techniques showed the potential for dealing with the problem of dynamic pricing in SCM. At the same time, NN models outperform GP models within the context of considered settings in terms of accuracy of prediction, complexity of implementation, and execution time.