APPLICATION OF DEEP LEARNING IN PRODUCT DEMAND FORECASTING
With the growth of e-commerce there has been a change in people's buying behavior. People who previously had to buy products face-to-face can now more easily buy their needs online. When buying products online, people generally expect the items they ordered to be delivered quickly. The selle...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/76309 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | With the growth of e-commerce there has been a change in people's buying
behavior. People who previously had to buy products face-to-face can now more
easily buy their needs online. When buying products online, people generally
expect the items they ordered to be delivered quickly. The sellers of products must
respond to these fast demands by ensuring they have sufficient stock in advance to
meet future buyer demand. To provide stock effectively, sellers need to predict
future demand for products. However, small and medium businesses often rely on
approximating previous sales to make predictions. This can lead to inaccuracies and
problems such as stock shortages or excessive inventory that exceeds people's
purchasing power. This Final Project aims to construct a demand prediction model
for general stores using advanced Deep Learning algorithms namely, Gated
Recurrent Unit (GRU), Long Short Term Memory (LSTM), and Transformer. The
primary objective is to revolutionize the method of demand prediction specifically
for SMEs, ensuring precise and optimal stock management. Among the algorithms
assessed, GRU exhibited the most promising outcomes, achieving a mean absolute
percentage error (MAPE) of 19.94% when predicting demand based on data from
existing general stores. |
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