Demand forecasting on supply chain using ML and NN
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| स्वरूप: | थीसिस |
| भाषा: | English |
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Brac University
2023
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| ऑनलाइन पहुंच: | http://hdl.handle.net/10361/18037 |
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10361-18037 |
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10361-180372023-03-30T21:01:39Z Demand forecasting on supply chain using ML and NN Hridi, Naoshin Anzum Farhan, Md Sharior Hossain Abed, Md. Junaed Rafsan, Mohammad Nafiz Fuad Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Demand forecasting Supply chain sales Deep learning LSTM DNN Prophet NeuralProphet ARIMA SARIMA CNN RNN Cognitive learning theory Machine learning Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-54). Demand forecasting is mainly a process whereby analyzing historical sales data, strategic and operational strategies are devised in order to estimate customer demand. One of the most fundamental aspects of supply chain management is inventory management, its major goal is to cut expenses, boost sales and profits, optimize inventory, and most importantly, promote customer loyalty. The process of extrapolating relevant sales data may be separated into qualitative and quantitative forecasting, with each relying on multiple sources and data sets. When there is previous sales data on certain items and a predetermined demand, the quantitative forecasting approach is employed. It necessitates the application of mathematical formulas as well as data sets such as financial reports, sales, and income numbers, as well as website analytic. The qualitative technique, on the other hand, is based on new technologies, pricing and availability changes, product life cycles, product upgrades and most significantly, the forecasters’ intuition and experience. Machine learning, clustering, time series analysis, neural networks, KNN, support vector regression, support vector machines, regression analysis, and deep learning are some of the approaches used to anticipate demand. A majority of study has gone into improving demand forecasting, which will enhance supply chain sales and profitability. To do that the researchers mainly focused on using machine learning or deep learning as its main methodology and others like support vector algorithm, time series analysis. However, to our best knowledge, only a handful of research is done using hybrid model consists of both deep learning and machine learning as its main methodology. That is why we want to concentrate on using hybrid models to develop dynamically configurable demand forecasting which eventually will give us promising results. Naoshin Anzum Hridi Md Sharior Hossain Farhan Md. Junaed Abed Mohammad Nafiz Fuad Rafsan B. Computer Science 2023-03-30T03:31:34Z 2023-03-30T03:31:34Z 2022 2022-05 Thesis ID 18301065 ID 18301266 ID 18101349 ID 18101558 http://hdl.handle.net/10361/18037 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 54 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Demand forecasting Supply chain sales Deep learning LSTM DNN Prophet NeuralProphet ARIMA SARIMA CNN RNN Cognitive learning theory Machine learning Neural networks (Computer science) |
| spellingShingle |
Demand forecasting Supply chain sales Deep learning LSTM DNN Prophet NeuralProphet ARIMA SARIMA CNN RNN Cognitive learning theory Machine learning Neural networks (Computer science) Hridi, Naoshin Anzum Farhan, Md Sharior Hossain Abed, Md. Junaed Rafsan, Mohammad Nafiz Fuad Demand forecasting on supply chain using ML and NN |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Hridi, Naoshin Anzum Farhan, Md Sharior Hossain Abed, Md. Junaed Rafsan, Mohammad Nafiz Fuad |
| format |
Thesis |
| author |
Hridi, Naoshin Anzum Farhan, Md Sharior Hossain Abed, Md. Junaed Rafsan, Mohammad Nafiz Fuad |
| author_sort |
Hridi, Naoshin Anzum |
| title |
Demand forecasting on supply chain using ML and NN |
| title_short |
Demand forecasting on supply chain using ML and NN |
| title_full |
Demand forecasting on supply chain using ML and NN |
| title_fullStr |
Demand forecasting on supply chain using ML and NN |
| title_full_unstemmed |
Demand forecasting on supply chain using ML and NN |
| title_sort |
demand forecasting on supply chain using ml and nn |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18037 |
| work_keys_str_mv |
AT hridinaoshinanzum demandforecastingonsupplychainusingmlandnn AT farhanmdshariorhossain demandforecastingonsupplychainusingmlandnn AT abedmdjunaed demandforecastingonsupplychainusingmlandnn AT rafsanmohammadnafizfuad demandforecastingonsupplychainusingmlandnn |
| _version_ |
1814309867332042752 |