Report

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

BP2-STS

  • 2024

26 p.

English 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 not accurate, for example, due to stock-outs or excess inventory.
Therefore, the purpose of this study was 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) appears to give better results than all the ML algorithms tested.
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Faculty
Faculté des sciences et de médecine
Department
Département d'informatique
Language
  • English
Classification
Computer science and technology
Series statement
  • Internal working papers DIUF ; 24-01
License
License undefined
Persistent URL
https://folia.unifr.ch/unifr/documents/328639
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