An improved parameter estimation of bass model for forecasting new car demand
A new product forecasting model for new Proton car is developed and tested empirically against the data gathered from the first twelve months of launching. The basic assumption to this model is the innovators who make decision to purchase the new car based on the information obtained from the mass m...
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my.utm.372392017-09-26T01:40:03Z http://eprints.utm.my/id/eprint/37239/ An improved parameter estimation of bass model for forecasting new car demand Ismail, Zuhaimy Abu, Noratikah Q Science A new product forecasting model for new Proton car is developed and tested empirically against the data gathered from the first twelve months of launching. The basic assumption to this model is the innovators who make decision to purchase the new car based on the information obtained from the mass media and the imitators are those who make decision to buy based on the existing social system through the personal selling and advertising. Forecasting new product or service is a critical process in marketing strategies and product performance for an organisation. This proposed Bass diffusion model represent the level of spread on the new car among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new product. We highlight the selection of analogy product and selection of parameter estimation method. Results obtained by applying the proposed model and numerical calculation shows that the proposed model is robust and effective for forecasting demand of new product. This study concludes that the newly developed bass diffusion for forecasting new car demand has significantly contributed towards forecasting the diffusion of other new products. 2013 Conference or Workshop Item PeerReviewed Ismail, Zuhaimy and Abu, Noratikah (2013) An improved parameter estimation of bass model for forecasting new car demand. In: Prosiding Seminar Kebangsaan Aplikasi Sains dan Matematik 2013 (SKASM2013), 2013. |
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Q Science Ismail, Zuhaimy Abu, Noratikah An improved parameter estimation of bass model for forecasting new car demand |
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A new product forecasting model for new Proton car is developed and tested empirically against the data gathered from the first twelve months of launching. The basic assumption to this model is the innovators who make decision to purchase the new car based on the information obtained from the mass media and the imitators are those who make decision to buy based on the existing social system through the personal selling and advertising. Forecasting new product or service is a critical process in marketing strategies and product performance for an organisation. This proposed Bass diffusion model represent the level of spread on the new car among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new product. We highlight the selection of analogy product and selection of parameter estimation method. Results obtained by applying the proposed model and numerical calculation shows that the proposed model is robust and effective for forecasting demand of new product. This study concludes that the newly developed bass diffusion for forecasting new car demand has significantly contributed towards forecasting the diffusion of other new products. |
format |
Conference or Workshop Item |
author |
Ismail, Zuhaimy Abu, Noratikah |
author_facet |
Ismail, Zuhaimy Abu, Noratikah |
author_sort |
Ismail, Zuhaimy |
title |
An improved parameter estimation of bass model for forecasting new car demand |
title_short |
An improved parameter estimation of bass model for forecasting new car demand |
title_full |
An improved parameter estimation of bass model for forecasting new car demand |
title_fullStr |
An improved parameter estimation of bass model for forecasting new car demand |
title_full_unstemmed |
An improved parameter estimation of bass model for forecasting new car demand |
title_sort |
improved parameter estimation of bass model for forecasting new car demand |
publishDate |
2013 |
url |
http://eprints.utm.my/id/eprint/37239/ |
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1643650101711732736 |
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13.244414 |