Abstract
In this paper is presented a research of electrical spare parts demand forecasting through application of conventional (moving average, exponential smoothing and naive theory), more sophisticated forecasting techniques (support vector regression, feed-forward neural networks) and adaptive model selection methodologies. Electrical spare parts demand forecasting is a fundamental task that should be performed in order to improve SCM (supply chain management). If it would be possible to know what the demand for electrical parts will be in the future, the logistics of the companies that manufacture electrical parts or retailers could be managed more accurately: selection of appropriate warehouse safety limits for each part and ability to plan the resources more precisely. Customer sales and marketing departments always perform formal forecasts, this is usually done through application of conventional methods in order to prepare future plans. Experimental results reveal that application of SVR technique guarantees the best and precise results of forecasting of weekly and daily demand of electrical parts. Furthermore, application of adaptive methodology in order to select adaptive model allowed substantially to increase forecasting accuracy. DOI: http://dx.doi.org/10.5755/j01.eee.20.10.8870
Highlights
In order to increase a profit, manufacturing companies, wholesalers or retailers should be able to increase their productivity in the supply chain management (SCM), i.e., logistics
Collection of the data lasted for approximately two years, it varies depending on time the certain component was added to the database
Experimental investigation and analysis of forecasting results revealed that the best precision on real spare parts demand data was achieved through application of support vector regression (SVR) technique
Summary
In order to increase a profit, manufacturing companies, wholesalers or retailers should be able to increase their productivity in the supply chain management (SCM), i.e., logistics. Future demand of electrical spare parts could be estimated basically in two ways: 1) either using historical demand (end customers) data; 2) or modelling electrical parts’ life cycles and estimating electrical parts demand in the future in accordance with prediction of electrical part’s fault time The latter option could be applied in order to optimize electrical devices service, but it’s effect in regard of retailer’s (manufacturer’s) logistics is not direct, this is in Manuscript received January 28, 2014; accepted September 13, 2014. R. Tabar [9] compare various neural network architectures, Croston’s and Syntetos-Boylan approximation methods in lumpy demand forecasting for spare parts in process industries. Tabar [9] compare various neural network architectures, Croston’s and Syntetos-Boylan approximation methods in lumpy demand forecasting for spare parts in process industries Their results show that RNN (Recurrent Neural Network) model is the most precise using spare parts demand data from Arak Petrochemical Company. The equation (1) clearly reveals that a minimization problem is a quadratic optimisation problem, which means that the local optimum is always a global optimum
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