Provide an approach to anticipate supply processes with utilizing machine learning algorithms
Saman Siadati *, M. Jafar Tarokh **
The manufacturing industry is heavily influenced by the stagnation, as well as the competitive atmosphere resulting from the globalization process, which necessitates further forecasting of future trends to overcome the risks and market fluctuations. In order to provide the right equipment, a large variety of components must be delivered from suppliers in different positions to the assembly line. Planning supply processes often depends on changing information about product development, scheduling assembly lines and purchasing. At present, much time is spent on collecting information during planning, and the knowledge gained from the previous planning process is not used for future planning. This issue is particularly significant in the home appliance manufacturing and assembly industry due to the variety of components. This paper presents a method for predictive supply planning using machine learning algorithms. In summary, general knowledge about extraction processes and for prediction of future scenarios is used.
Machine learning, supply process; modeling; knowledge management; ontology
*,** Strategic Intelligence Research Laboratory, K.N.T. university of technology, Tehran, Iran