| dc.description.abstract | Supply chains are complicated, unpredictably varying systems. Logistics
managers are currently confronted with two major challenges: increasingly
diverse and fluctuating consumer demand that is difficult to predict.
Forecasting demand is critical in the supply chain because it informs
essential operational operations such as demand-driven material resource
planning (DDMRP), inbound logistics, production, financial planning, and
risk assessment. Machine Learning (ML) technologies are increasingly being
used for time series forecasting. When forecasting time series data, machine
learning is an application of artificial intelligence (AI) that allows
forecasting models to learn historical demand patterns and anomalies and
enhance future demand prediction accuracy. This reduces the disparity
between projected and actual demand levels, giving firms a better sense of
what's to come. In this thesis, to emphasize the use of ML methods in
demand forecasting, we play the role of logistics service for a meal delivery
company which operates in many locations to forecast consumer demand in
the upcoming weeks for the raw materials planning, preparing and storing.
The proposed method is Multilayer Perceptron (MLP). The suggested
approach is compared with other popular time series forecasting approaches,
such as Linear Regression, Decision Tree Regressor, Random Forest
Regressor, Gradient Tree Boosting, and Multilayer Perceptron. According to
the experimental findings, the proposed strategy outperforms the others in
terms of performance metrics. | en_US |
