How to Improve Accessories Sales Forecasting of a Medium-Sized Swiss Enterprise? A Comparison Between Statistical Methods and Machine Learning Algorithms
DOI:
https://doi.org/10.33423/jabe.v26i4.7261Keywords:
business, economics, demand forecast, key account manager (KAM), seasonal autoregressive integrated moving average (SARIMA), machine learning (ML), K-nearest neighbors (k-NN), least absolute shrinkage and selection operator (LASSO), linear regression, random forest (RF), root mean square error (RMSE), mean absolute error (MAE)Abstract
Forecast accuracy is a crucial topic for industrial companies, and its impacts are particularly important for the finance and production departments. The company can incur high costs if forecasts are inaccurate, for example, due to stock-outs or excess inventory.
Therefore, this study aimed to optimize accessories forecasting for a medium-sized Swiss enterprise. To do so, different forecasting techniques were tested, and statistical methods and machine learning (ML) algorithms were compared. The results were adjusted according to key account managers’ (KAM) expertise.
This paper presents a comparison between exponential smoothing, seasonal autoregressive integrated moving average (SARIMA), SARIMAX (SARIMA with exogenous variables) and ML algorithms, such as k-nearest neighbors (k-NN), least absolute shrinkage and selection operator (LASSO) regression, linear regression, and even random forest (RF).
To compare these different methods, two measures of statistical dispersion are computed: mean absolute error (MAE) and root mean squared error (RMSE). The results are standardized to enable a better comparison. For our dataset, SARIMAX (with the KAMs’ expertise as an exogenous variable) gives better results than all the ML algorithms tested.
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