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Identification of factors affecting lubricant demand and developing models for sales forecasting

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dc.contributor.advisor Sivakumar, T
dc.contributor.author Jayathissa, HPITB
dc.date.accessioned 2019-07-18T08:46:10Z
dc.date.available 2019-07-18T08:46:10Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/14598
dc.description.abstract With a bigger uncertainty and a lot of speedy modification in today's business setting, a heavier role to play lies inside prediction of future sales additionally referred to as sales forecasting. Though prediction becomes a lot of vital so as to not lose market shares, not all corporations regard the sales forecasting method as a key perform inside their organization. An overview of lubricants demand forecasting through identified factors, based on a developed regression models has been presented in this research. Annual data has been taken since 2012 to indicate close relations among the factors while considering quarter wise data. Firstly eight factors have been identified and then the following factors, Vehicle population, GDP growth and export values have been selected for the model developments after correlation analysis. After that regression analysis has been done for selected factors and based on the results forecasting methods have been developed. Then using actual data for the year 2016 the research convey the sales forecasting and evaluated model result. Furthermore if not accessible one in every of these factors, like if have only sales and vehicle population Company will do the sales forecasting with the 95.9% accuracy. Company have sales knowledge and GDP growth they can forecast the sales with the 69.10% accuracy. If the sales and export data accessible company will do the forecast 95.42% accuracy. en_US
dc.language.iso en en_US
dc.subject Lubricants demand factors en_US
dc.subject lubricants sales forecasting en_US
dc.title Identification of factors affecting lubricant demand and developing models for sales forecasting en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree MBA in Supply Chain Management en_US
dc.identifier.department Department of Transport & Logistic Management en_US
dc.date.accept 2018-02
dc.identifier.accno TH3673 en_US


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