Hybrid machine learning models for enhanced sales forecasting

Abstract

Sales forecasts are essential to every business’s strategic plans and can both save the company money and increase its competitive advantage. However, many current businesses underestimate the opportunities which accurate forecasts provide and rely solely on judgemental forecasts from experts within the business. Machine learning and statistical forecasting methods are used by both academics and practitioners to increase the accuracy of these forecasting methods and can be further improved by combining knowledge from within the business with the statistical and machine learning techniques presented in this work. The models introduced in this study combine domain knowledge with data-driven approaches to improve forecasting on small datasets. The first approach in this work gathers global sales pipeline data to build a short-term sales forecast for a newly proposed dynamic cluster-based Markov (DCBM) model. By applying a newly developed algorithm, which first clusters the training and test set, the prediction of future sales for the next three months can be improved over a regular Markov transition model. The second proposed approach applies product lifecycle (PLC) information to improve the sales forecast. The accuracy of the sales forecast was increased for all 11 years for a luxury car manufacturer, comparing the newly developed PLC detrending approach to a common detrending by differencing approach in a seasonal autoregressive integrated moving average (SARIMA) framework. In a third model, the DCBM and PLC approaches are synthesised by using a SARIMA- long short-term memory (LSTM) framework capable of combining different data sources and thereby further increasing sales forecasting accuracy. The SARIMA-LSTM was able to predict the changes in sales
occurring during the COVID-19 pandemic. All new models support short- and mid-term sales forecasting up to 12 months and represent an extension of knowledge in the area of sales forecasting.

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