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The use of analogies in forecasting the annual sales of new electronics products

P. Goodwin, K. Dyussekeneva, S. Meeran, Karima Dyussekeneva

IMA Journal of Management Mathematics, Volume: 24, Issue: 4, Pages: 407 - 422

Swansea University Author: Karima Dyussekeneva

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DOI (Published version): 10.1093/imaman/dpr025

Abstract

Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no...

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Published in: IMA Journal of Management Mathematics
ISSN: 1471-678X 1471-6798
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa43560
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spelling 2019-07-24T12:16:38.9846233 v2 43560 2018-08-24 The use of analogies in forecasting the annual sales of new electronics products 159ce7d6be8f1aff521f126f9699bb6d Karima Dyussekeneva Karima Dyussekeneva true false 2018-08-24 BBU Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no historic time series data with which to estimate the model’s parameters. One possible solution is to fit the model to the sales time series of analogous products that have been launched in an earlier time period and to assume that the parameter values identified for the analogy are applicable to the new product. In this paper we investigate the effectiveness of this approach by applying four forecasting methods based on analogies (and variants of these methods) to the sales of consumer electronics products marketed in the USA. We found that all of the methods tended to lead to forecasts with high absolute percentage errors, which is consistent with other studies of new product sales forecasting. The use of the means of published parameter values for analogies led to higher errors than the parameters we estimated from our own data. When using this data averaging the parameter values of multiple analogies, rather than relying on a single most-similar, product led to improved accuracy. However, there was little to be gained by using more than 5 or 6 analogies. Journal Article IMA Journal of Management Mathematics 24 4 407 422 1471-678X 1471-6798 Bass model, diffusion models, new product forecasting, analogies. 1 10 2013 2013-10-01 10.1093/imaman/dpr025 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2019-07-24T12:16:38.9846233 2018-08-24T12:40:40.6336848 Faculty of Humanities and Social Sciences School of Management - Business Management P. Goodwin 1 K. Dyussekeneva 2 S. Meeran 3 Karima Dyussekeneva 4 0043560-24082018124109.pdf Theuseofanalogiesinforecastingtheannualsalesofnewelectronicsproducts.pdf 2018-08-24T12:41:09.5730000 Output 291311 application/pdf Accepted Manuscript true 2018-08-24T00:00:00.0000000 true eng
title The use of analogies in forecasting the annual sales of new electronics products
spellingShingle The use of analogies in forecasting the annual sales of new electronics products
Karima Dyussekeneva
title_short The use of analogies in forecasting the annual sales of new electronics products
title_full The use of analogies in forecasting the annual sales of new electronics products
title_fullStr The use of analogies in forecasting the annual sales of new electronics products
title_full_unstemmed The use of analogies in forecasting the annual sales of new electronics products
title_sort The use of analogies in forecasting the annual sales of new electronics products
author_id_str_mv 159ce7d6be8f1aff521f126f9699bb6d
author_id_fullname_str_mv 159ce7d6be8f1aff521f126f9699bb6d_***_Karima Dyussekeneva
author Karima Dyussekeneva
author2 P. Goodwin
K. Dyussekeneva
S. Meeran
Karima Dyussekeneva
format Journal article
container_title IMA Journal of Management Mathematics
container_volume 24
container_issue 4
container_start_page 407
publishDate 2013
institution Swansea University
issn 1471-678X
1471-6798
doi_str_mv 10.1093/imaman/dpr025
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 1
active_str 0
description Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no historic time series data with which to estimate the model’s parameters. One possible solution is to fit the model to the sales time series of analogous products that have been launched in an earlier time period and to assume that the parameter values identified for the analogy are applicable to the new product. In this paper we investigate the effectiveness of this approach by applying four forecasting methods based on analogies (and variants of these methods) to the sales of consumer electronics products marketed in the USA. We found that all of the methods tended to lead to forecasts with high absolute percentage errors, which is consistent with other studies of new product sales forecasting. The use of the means of published parameter values for analogies led to higher errors than the parameters we estimated from our own data. When using this data averaging the parameter values of multiple analogies, rather than relying on a single most-similar, product led to improved accuracy. However, there was little to be gained by using more than 5 or 6 analogies.
published_date 2013-10-01T03:54:48Z
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score 11.012678

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