Abstract
When faced with a price war, the accuracy of forecasting sales in e-commerce greatly influences an enterprise’s or a retailer’s merchandise inventory strategies. When faced with a price war, an enterprise might obtain certain consumption patterns by analyzing previous sales data. This case study research was conducted in collaboration with a medical product company to explore which of the various forecasting models can better inform a company’s inventory plan. The study used the company’s data from Amazon.com regarding sales volume, number of views, company ranking, etc. between February 7 2016 and March 28 of 2018. Three potential methods of data mining were selected from the literature: the exponential smoothing method, the linear trend method, and the seasonal variation method. Of these, the most suitable was identified for price war situations to forecast the sales volume for April 2018 and to provide concrete information for the company’s inventory plan. The results showed that the seasonal variation method is more suitable than the other two sales forecasting methods. To obtain a more accurate sales forecast during a price war, the seasonal variation method is recommended to be used in the following approaches: Adjust the seasonal index by using a simple moving average. Remove the seasonal index from the sales volume, and conduct a regression analysis using the data within the last month. The resulting predicted value (with the seasonal index removed) should be multiplied by each period’s corresponding weighted moving average to obtain a more accurate sales forecast during a price war.
| Original language | English |
|---|---|
| Title of host publication | HCI in Business, Government and Organizations. eCommerce and Consumer Behavior - 6th International Conference, HCIBGO 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings |
| Editors | Fiona Fui-Hoon Nah, Keng Siau |
| Publisher | Springer Verlag |
| Pages | 3-21 |
| Number of pages | 19 |
| ISBN (Print) | 9783030223342 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
| Event | 6th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019 - Orlando, United States Duration: 2019 Jul 26 → 2019 Jul 31 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11588 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 6th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019 |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 19-07-26 → 19-07-31 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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A Study of Models for Forecasting E-Commerce Sales During a Price War in the Medical Product Industry. / Hsieh, Pei-Hsuan.
HCI in Business, Government and Organizations. eCommerce and Consumer Behavior - 6th International Conference, HCIBGO 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings. ed. / Fiona Fui-Hoon Nah; Keng Siau. Springer Verlag, 2019. p. 3-21 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11588 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A Study of Models for Forecasting E-Commerce Sales During a Price War in the Medical Product Industry
AU - Hsieh, Pei-Hsuan
PY - 2019/1/1
Y1 - 2019/1/1
N2 - When faced with a price war, the accuracy of forecasting sales in e-commerce greatly influences an enterprise’s or a retailer’s merchandise inventory strategies. When faced with a price war, an enterprise might obtain certain consumption patterns by analyzing previous sales data. This case study research was conducted in collaboration with a medical product company to explore which of the various forecasting models can better inform a company’s inventory plan. The study used the company’s data from Amazon.com regarding sales volume, number of views, company ranking, etc. between February 7 2016 and March 28 of 2018. Three potential methods of data mining were selected from the literature: the exponential smoothing method, the linear trend method, and the seasonal variation method. Of these, the most suitable was identified for price war situations to forecast the sales volume for April 2018 and to provide concrete information for the company’s inventory plan. The results showed that the seasonal variation method is more suitable than the other two sales forecasting methods. To obtain a more accurate sales forecast during a price war, the seasonal variation method is recommended to be used in the following approaches: Adjust the seasonal index by using a simple moving average. Remove the seasonal index from the sales volume, and conduct a regression analysis using the data within the last month. The resulting predicted value (with the seasonal index removed) should be multiplied by each period’s corresponding weighted moving average to obtain a more accurate sales forecast during a price war.
AB - When faced with a price war, the accuracy of forecasting sales in e-commerce greatly influences an enterprise’s or a retailer’s merchandise inventory strategies. When faced with a price war, an enterprise might obtain certain consumption patterns by analyzing previous sales data. This case study research was conducted in collaboration with a medical product company to explore which of the various forecasting models can better inform a company’s inventory plan. The study used the company’s data from Amazon.com regarding sales volume, number of views, company ranking, etc. between February 7 2016 and March 28 of 2018. Three potential methods of data mining were selected from the literature: the exponential smoothing method, the linear trend method, and the seasonal variation method. Of these, the most suitable was identified for price war situations to forecast the sales volume for April 2018 and to provide concrete information for the company’s inventory plan. The results showed that the seasonal variation method is more suitable than the other two sales forecasting methods. To obtain a more accurate sales forecast during a price war, the seasonal variation method is recommended to be used in the following approaches: Adjust the seasonal index by using a simple moving average. Remove the seasonal index from the sales volume, and conduct a regression analysis using the data within the last month. The resulting predicted value (with the seasonal index removed) should be multiplied by each period’s corresponding weighted moving average to obtain a more accurate sales forecast during a price war.
UR - http://www.scopus.com/inward/record.url?scp=85069814547&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069814547&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-22335-9_1
DO - 10.1007/978-3-030-22335-9_1
M3 - Conference contribution
AN - SCOPUS:85069814547
SN - 9783030223342
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 21
BT - HCI in Business, Government and Organizations. eCommerce and Consumer Behavior - 6th International Conference, HCIBGO 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
A2 - Nah, Fiona Fui-Hoon
A2 - Siau, Keng
PB - Springer Verlag
ER -