Choice of Quantitative Method for Forecasting of Parquet Sales

Oblak, Leon; Stirn, Lidija Zadnik; Moro, Maja; Hrovatin, Jasna; Mole, Samo; Kuzman, Manja Kitek

doi:10.5552/drind.2012.1204

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The commonly-used prediction methods proposed in other related studies [9][10][11][12][13][14][15][16] include trend analysis, cause analysis, judgment analysis, survey analysis, and artificial neural networks,etc.…”

Section: Prediction Methodsmentioning

confidence: 99%

Tourism Demand Forecasting Model Using Neural Network

Huang¹,

Hou²

2017

IJCSIT

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Section: Prediction Methodsmentioning

confidence: 99%

Tourism Demand Forecasting Model Using Neural Network

Huang¹,

Hou²

2017

IJCSIT

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“…The results of the study show that ANN model performs much better than the linear Brown's double smoothing model. Oblak et al (2012) applied two quantitative methods named Holt-Winters method of exponent smoothing of higher orders and linear regression of the 1st order for forecasting of parquet sales. Parquet sales data by month in the years 2000 to 2009 was used in the models and the best result was obtained with the use of Holt-Winters multiplicative model of exponent smoothing of higher orders.…”

Section: Uvodmentioning

confidence: 99%

Performance Comparison between ARIMAX, ANN and ARIMAX-ANN Hybridization in Sales Forecasting for Furniture Industry

2018

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Manufacturing fi rms aim to increase their profi ts and reduce costs in a competitive and rapidly changing market. One of the most important ways to reach these goals is to forecast sales correctly. Furniture manufacturing, which is considered a prosperous and growing industry in Turkey, has an increasing trend related to the growth in construction and associated industries, increase in urban migration and increase in per capita income. Accuracy of sales forecasting in furniture industry is affected by external factors, such as consumer confidence index, producer price index, month of the year and number of vacation days as well as the time factor itself. This study aims to develop an Autoregressive Integrated Moving Average with external variables (ARIMAX) to forecast the total monthly sales of furniture products of a well-known manufacturer in Turkey. As a follow up study, a performance comparison between ARIMAX, artifi cial neural networks (ANNs) and ARIMAX-ANN hybridization is performed. In conclusion, results of performance measures demonstrate that hybrid model developed for each amount of product sales give better accuracy values than single methods. Overall, it is proved that using the ARI-MAX and hybridization of this method with ANN are applicable for forecasting monthly sales of furniture products.

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“…Reducing these uncertainties and ambiguity with good sale forecasting represented a big problem for the companies (Oblak et al, 2012) For all enterprises, whether small, medium or large, the issue of financing their business activities is very significant. Many different bank loans are offered on the market, but banks are very careful in providing them in these times.…”

Section: Uvodmentioning

confidence: 99%