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Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets

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

IFAC Proceedings Volumes, Volume: 46, Issue: 9, Pages: 87 - 92

Swansea University Author: Karima Dyussekeneva

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Abstract

Forecasting sales accurately for a new product is difficult and complex due to non-availability ofpast data. However, such forecast information is crucial for successful introduction of new products which, inturn, determines the survival of companies, in many cases. Decisions relating to new product...

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Published in: IFAC Proceedings Volumes
ISSN: 14746670
Published: 2013
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URI: https://cronfa.swan.ac.uk/Record/cronfa43561
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first_indexed 2018-08-24T13:49:24Z
last_indexed 2018-10-09T19:34:54Z
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spelling 2018-10-09T16:00:14.9517545 v2 43561 2018-08-24 Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets 159ce7d6be8f1aff521f126f9699bb6d Karima Dyussekeneva Karima Dyussekeneva true false 2018-08-24 BBU Forecasting sales accurately for a new product is difficult and complex due to non-availability ofpast data. However, such forecast information is crucial for successful introduction of new products which, inturn, determines the survival of companies, in many cases. Decisions relating to new products depend criticallyon reliable period-by-period sales forecasts (otherwise called forecast time series) as early as possible in the newproduct development cycle. This information is crucial in assessing cash flow and NPV relating to the newproduct. There have been many attempts to use growth curves (otherwise called diffusion models), such as theBass model. These models made use of past data about analogous products to do this task. However, thismethod, although considered the best method, available, has many problems, such as identifying analogousproducts which can reliably mimic the new product in its sales characteristics. These difficulties explain why theaccuracy of forecasts reported by such methods is, at best, 50%. Here we propose an innovative conceptualframework to obtain time series data required for forming the growth curve for the new product bybootstrapping the growth curve models with a novel ‘Forecast market’ mechanism. The effectiveness of the‘Forecast market’ in obtaining accurate estimates of the time series data itself is likely to be enhanced by lettingthe ‘Forecast market’ participants use product information ranging from simple pictures of the product to highendvirtual reality systems which enable them to visualise and appreciate the features of the new product. Journal Article IFAC Proceedings Volumes 46 9 87 92 14746670 New product forecasts, prediction methods, diffusion model, preference markets, forecast market 20 9 2013 2013-09-20 10.3182/20130619-3-RU-3018.00619 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2018-10-09T16:00:14.9517545 2018-08-24T12:52:15.0439661 Faculty of Humanities and Social Sciences School of Management - Business Management S. Meeran 1 K. Dyussekeneva 2 P. Goodwin 3 Karima Dyussekeneva 4
title Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
spellingShingle Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
Karima Dyussekeneva
title_short Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
title_full Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
title_fullStr Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
title_full_unstemmed Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
title_sort Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets
author_id_str_mv 159ce7d6be8f1aff521f126f9699bb6d
author_id_fullname_str_mv 159ce7d6be8f1aff521f126f9699bb6d_***_Karima Dyussekeneva
author Karima Dyussekeneva
author2 S. Meeran
K. Dyussekeneva
P. Goodwin
Karima Dyussekeneva
format Journal article
container_title IFAC Proceedings Volumes
container_volume 46
container_issue 9
container_start_page 87
publishDate 2013
institution Swansea University
issn 14746670
doi_str_mv 10.3182/20130619-3-RU-3018.00619
college_str Faculty of Humanities and Social Sciences
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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
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description Forecasting sales accurately for a new product is difficult and complex due to non-availability ofpast data. However, such forecast information is crucial for successful introduction of new products which, inturn, determines the survival of companies, in many cases. Decisions relating to new products depend criticallyon reliable period-by-period sales forecasts (otherwise called forecast time series) as early as possible in the newproduct development cycle. This information is crucial in assessing cash flow and NPV relating to the newproduct. There have been many attempts to use growth curves (otherwise called diffusion models), such as theBass model. These models made use of past data about analogous products to do this task. However, thismethod, although considered the best method, available, has many problems, such as identifying analogousproducts which can reliably mimic the new product in its sales characteristics. These difficulties explain why theaccuracy of forecasts reported by such methods is, at best, 50%. Here we propose an innovative conceptualframework to obtain time series data required for forming the growth curve for the new product bybootstrapping the growth curve models with a novel ‘Forecast market’ mechanism. The effectiveness of the‘Forecast market’ in obtaining accurate estimates of the time series data itself is likely to be enhanced by lettingthe ‘Forecast market’ participants use product information ranging from simple pictures of the product to highendvirtual reality systems which enable them to visualise and appreciate the features of the new product.
published_date 2013-09-20T03:54:48Z
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