A framework for modeling efficient demand forecasting using data mining in supply chain of food products export industry

Abstract

According to the Hamburger effect, food products export industry sector, especially cooked chicken products export to Japan of Thai industry, effort has been spent in the supply chain management (SCM) of internal efficiency, solely aiming at competitiveness survival in terms of cost reduction, better quality. To meet the customer satisfaction, the company must work towards a right time and volume of demand delivery. Therefore, forecasting technique is the crucial element of SCM. The more understanding how their company use the right forecasting based on information sharing in their SCM context; the more reducing inventory and capacity planning cost increase their company competitiveness. Presently, most of companies, in this sector, do not have a right knowledge to implement the suitable forecasting system to sustain their business; furthermore, they only use top management judgment and some of economical data for forecasting decision making to production. Because the complex, stochastic, dynamic nature and multi-criteria of the logistics operations along the food products exporting to Japan of Thai industry supply chain, the existing time series forecasting approaches cannot provide the information to operate demand from upstream to downstream effectively. The objective of the paper is how to develop a conceptual framework for an innovative and simplified forecasting system implementation for this industry based on data mining including time series factors and causal factors. Then we discuss a methodology to determine appropriated implementation guideline. © Springer-Verlag Berlin Heidelberg 2010.