EURO 2024 Copenhagen
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782. How to improve accessories sales forecasting of a medium-sized Swiss enterprise? A comparison between statistical methods and machine learning algorithms

Invited abstract in session TB-28: Advancements of OR-analytics in statistics, machine learning and data science 5, stream Advancements of OR-analytics in statistics, machine learning and data science.

Tuesday, 10:30-12:00
Room: 065 (building: 208)

Authors (first author is the speaker)

1. Agneta Ramosaj
Informatique, Universty of Fribourg
2. Nicolas Ramosaj
- Mechanical Engineering, University of Applied Sciences and Arts, Western Switzerland
3. Marino Widmer
Department of Informatics, University of Fribourg

Abstract

Forecasts accuracy is definitively a crucial topic for industrial companies. Indeed, its impacts are huge especially for finance and production departments. It can occur high costs for the company if the forecasts are not accurate, due to stock-outs or excesses of inventory, for example.
Therefore, the purpose of this study is to optimize accessories forecasting for a medium-sized Swiss enterprise. To do that, different forecasting techniques are tested and a comparison is made between statistical methods and machine learning algorithms. The results have been adjusted thanks to the key account managers (KAM) expertise.
In this talk, a comparison between exponential smoothing, seasonal autoregressive integrated moving average (SARIMA), SARIMAX (SARIMA with exogenous regressors) and Machine Learning algorithms such as k-nearest neighbors (k-NN), LASSO regression, linear regression and even random forest is presented.
To compare these different methods, two measures of statistical dispersion are computed: mean absolute error (MAE) and root mean squared error (RMSE). These results have been standardized for a better comparison. It results that for our dataset SARIMAX (with the KAM’s expertise as exogenous variable) gives better results that all the machine learning algorithms tested.

Keywords

Status: accepted

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