Highlights In this paper, we innovatively apply two advanced technologies to product demand estimation which are: video analyzing techniques and group lasso models from machine learning. These two techniques are rarely used in previous literature, and we propose a novel group-lasso based product demand model using the lost sales information (GDMLSI) to incorporate them in the demand estimation model. The proposed model improves the forecast accuracy of 8% at the total sales level as compared to time series forecasting and also does well at the SKU sales level as compared to a naive method. With the proposed demand estimation model, demands can be forecasted more accurately. Moreover, the model is applied to vending machines, which are widespread nowadays, making our research more beneficial for the society.

    Abstract Lost sales information has significant impacts on the estimation of product demand and substitution. However, the difficulties to recognize such information in real applications make it rarely used in research. In this paper, we come up with a novel group-lasso based product demand model using the lost sales information, which is extracted from the video surveillance data provided by the cooperate retailer. A group-lasso method is used to characterize the substitution behaviours among each pair of SKUs. Then, an alternating minimization algorithm whose efficiency and convergence have been proved is designed to solve the model. To evaluate the model, we provide comparative experiments between the proposed method, time series forecasting and a naive method by applying these models to a real data set. The experiment results show that the proposed model obtains 8% higher accuracy at the total sales level and forecasts more accurately at the SKU sales level as well, which demonstrate its superiority. Moreover, we define a pair of welfare functions to measure the social impacts from both the retailer’s and customer’s end.


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    Titel :

    Product demand estimation for vending machines using video surveillance data: A group-lasso method


    Beteiligte:
    Ding, Xiaohui (Autor:in) / Chen, Caihua (Autor:in) / Li, Chongshou (Autor:in) / Lim, Andrew (Autor:in)


    Erscheinungsdatum :

    2021-04-10




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch