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New product forecasting: Methods

Karima Dyussekeneva, Sheik Meeran

Swansea University Author: Karima Dyussekeneva

Abstract

This book investigates three approaches to new product sales forecasting:statistical, judgmental and the integration of these two approaches. The aim of the bookis to find a simple, easy-to-use, low cost and accurate tool which can be used bymanagers to forecast the sales of new products. A review o...

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ISBN: 978-3659179532
Published: LAP LAMBERT Academic Publishing 2012
URI: https://cronfa.swan.ac.uk/Record/cronfa43562
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spelling 2018-08-28T16:28:34.7095642 v2 43562 2018-08-24 New product forecasting: Methods 159ce7d6be8f1aff521f126f9699bb6d Karima Dyussekeneva Karima Dyussekeneva true false 2018-08-24 BBU This book investigates three approaches to new product sales forecasting:statistical, judgmental and the integration of these two approaches. The aim of the bookis to find a simple, easy-to-use, low cost and accurate tool which can be used bymanagers to forecast the sales of new products. A review of the literature suggested thatthe Bass diffusion model was an appropriate statistical method for new product salesforecasting. For the judgmental approach, after considering different methods andconstraints, such as bias, complexity, lack of accuracy, high cost and time involvement,the Delphi method was identified from the literature as a method, which has thepotential to mitigate bias and produces accurate predictions at a low cost in a relativelyshort time. However, the literature also revealed that neither of the methods: statisticalor judgmental, can be guaranteed to give the best forecasts independently, and acombination of them is the often best approach to obtaining the most accuratepredictions.The study in this book compares these three approaches by applying them toactual sales data. To forecast the sales of new products, the Bass diffusion model wasfitted to the sales history of similar (analogous) products that had been launched in thepast and the resulting model was used to produce forecasts for the new products at thetime of their launch. These forecasts were compared with forecasts produced throughthe Delphi method and also through a combination of statistical and judgmentalmethods. All results were also compared to the benchmark levels of accuracy, based onprevious research and forecasts based on various combinations of the analogousproducts’ historic sales data. Although no statistically significant difference was foundin the accuracy of forecasts, produced by the three approaches, the results were moreaccurate than those obtained using parameters suggested by previous researchers. Book LAP LAMBERT Academic Publishing 978-3659179532 New product forecasting, judgmental, quantitative methods, diffusion model, analogous products 14 9 2012 2012-09-14 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2018-08-28T16:28:34.7095642 2018-08-24T12:58:35.9220647 Faculty of Humanities and Social Sciences School of Management - Business Management Karima Dyussekeneva 1 Sheik Meeran 2
title New product forecasting: Methods
spellingShingle New product forecasting: Methods
Karima Dyussekeneva
title_short New product forecasting: Methods
title_full New product forecasting: Methods
title_fullStr New product forecasting: Methods
title_full_unstemmed New product forecasting: Methods
title_sort New product forecasting: Methods
author_id_str_mv 159ce7d6be8f1aff521f126f9699bb6d
author_id_fullname_str_mv 159ce7d6be8f1aff521f126f9699bb6d_***_Karima Dyussekeneva
author Karima Dyussekeneva
author2 Karima Dyussekeneva
Sheik Meeran
format Book
publishDate 2012
institution Swansea University
isbn 978-3659179532
publisher LAP LAMBERT Academic Publishing
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description This book investigates three approaches to new product sales forecasting:statistical, judgmental and the integration of these two approaches. The aim of the bookis to find a simple, easy-to-use, low cost and accurate tool which can be used bymanagers to forecast the sales of new products. A review of the literature suggested thatthe Bass diffusion model was an appropriate statistical method for new product salesforecasting. For the judgmental approach, after considering different methods andconstraints, such as bias, complexity, lack of accuracy, high cost and time involvement,the Delphi method was identified from the literature as a method, which has thepotential to mitigate bias and produces accurate predictions at a low cost in a relativelyshort time. However, the literature also revealed that neither of the methods: statisticalor judgmental, can be guaranteed to give the best forecasts independently, and acombination of them is the often best approach to obtaining the most accuratepredictions.The study in this book compares these three approaches by applying them toactual sales data. To forecast the sales of new products, the Bass diffusion model wasfitted to the sales history of similar (analogous) products that had been launched in thepast and the resulting model was used to produce forecasts for the new products at thetime of their launch. These forecasts were compared with forecasts produced throughthe Delphi method and also through a combination of statistical and judgmentalmethods. All results were also compared to the benchmark levels of accuracy, based onprevious research and forecasts based on various combinations of the analogousproducts’ historic sales data. Although no statistically significant difference was foundin the accuracy of forecasts, produced by the three approaches, the results were moreaccurate than those obtained using parameters suggested by previous researchers.
published_date 2012-09-14T03:54:48Z
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score 11.036531

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