Journal article 524 views
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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DOI (Published version): 10.3182/20130619-3-RU-3018.00619
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...
| Published in: | IFAC Proceedings Volumes |
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| ISSN: | 14746670 |
| Published: |
2013
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa43561 |
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<?xml version="1.0"?><rfc1807><datestamp>2018-10-09T16:00:14.9517545</datestamp><bib-version>v2</bib-version><id>43561</id><entry>2018-08-24</entry><title>Sales forecasting using combination of diffusion model and forecast market – an adaption of prediction/preference markets</title><swanseaauthors><author><sid>159ce7d6be8f1aff521f126f9699bb6d</sid><firstname>Karima</firstname><surname>Dyussekeneva</surname><name>Karima Dyussekeneva</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2018-08-24</date><deptcode>BBU</deptcode><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 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.</abstract><type>Journal Article</type><journal>IFAC Proceedings Volumes</journal><volume>46</volume><journalNumber>9</journalNumber><paginationStart>87</paginationStart><paginationEnd>92</paginationEnd><publisher/><issnPrint>14746670</issnPrint><keywords>New product forecasts, prediction methods, diffusion model, preference markets, forecast market</keywords><publishedDay>20</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2013</publishedYear><publishedDate>2013-09-20</publishedDate><doi>10.3182/20130619-3-RU-3018.00619</doi><url/><notes/><college>COLLEGE NANME</college><department>Business</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BBU</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2018-10-09T16:00:14.9517545</lastEdited><Created>2018-08-24T12:52:15.0439661</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Business Management</level></path><authors><author><firstname>S.</firstname><surname>Meeran</surname><order>1</order></author><author><firstname>K.</firstname><surname>Dyussekeneva</surname><order>2</order></author><author><firstname>P.</firstname><surname>Goodwin</surname><order>3</order></author><author><firstname>Karima</firstname><surname>Dyussekeneva</surname><order>4</order></author></authors><documents/><OutputDurs/></rfc1807> |
| 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 |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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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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1763752739648569344 |
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11.036531 |