<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.</p>
</abstract>

This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Fibres & Textiles in Eastern Europe

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent...

	Intelligent Forecasting Approach
	MSE	RMSE	MAPE
A	4.64	2.15	3.78%
B	1.97	1.40	70.17%
C	4.46	2.11	21.11%
D	29.45	5.43	41.75%
E	706.60	26.58	30.91%
F	99.51	9.98	15.35%

Item	Real Sales	Baseline Model	Intelligent Forecasting Approach
A	57	49.51	59.15
B	2	12.61	0.60
C	10	20.22	12.11
D	13	25.99	7.57
E	86	144.70	112.58
F	65	51.45	55.02

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Article Category: Research Article

Publié en ligne: 15 déc. 2023

Pages: 30 - 38

DOI: https://doi.org/10.2478/ftee-2023-0052

Mots clés
new product, suggestion, forecasting, web crawler, machine learning, Google Trend

© 2023 Łukasiewicz Research Network-Łódź Institute of Technology, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Evaluation metrics of intelligent forecasting approach

Demand forecasting results after demand sense adjustment

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Article Category: Research Article

Publié en ligne: 15 déc. 2023

Pages: 30 - 38

DOI: https://doi.org/10.2478/ftee-2023-0052

Mots clésnew product, suggestion, forecasting, web crawler, machine learning, Google Trend

© 2023 Łukasiewicz Research Network-Łódź Institute of Technology, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Evaluation metrics of intelligent forecasting approach

Demand forecasting results after demand sense adjustment

Mots clés
new product, suggestion, forecasting, web crawler, machine learning, Google Trend