MARC details
| 000 -LEADER |
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| fixed length control field |
03019ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111037.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250429t20232023my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010015 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .T36 2023 r Bc. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tan, Wei Qing, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Development of demand forecasting model for inventory management using deep learning approach via transfer learning / |
| Statement of responsibility, etc. |
Tan Wei Qing |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
vi, 84 pages : |
| Other physical details |
illustrations ; |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Centre for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Inventory Management is crucial for small and medium-sized enterprises since it requires significant financial and human resources. However, businesses now must contend with seasonal client demand in addition to a competitive and unstable economic environment. Stock predicting management inventory would be required in this situation in order to reduce losses and boost profitability. Predicting or projecting a future occurrence or trend is known as demand forecasting. Demand forecasting of inventory management is able to assist businesses from preventing overstock or stock-outs. Due to the capital commitment made by excess inventory, high inventory levels might result in revenue losses. Losses in sales and a loss in consumer satisfaction and brand loyalty may result from shortages or out-of-stock situations. To estimate future seasonal product demand, inventory management has thus integrated the traditional time series approach, machine learning, and deep learning techniques. This research discusses how to construct a demand forecasting model for inventory management via transfer learning and determine whether classical time series analysis or machine learning methods are more suitable in forecasting demand of inventory management or deep learning method is better. The models are evaluated by using confusion matrix, root mean square error (error terms) and accuracy of each model. In this research, a demand forecasting model for inventory management using transfer learning is constructed and achieved accuracy of 90.97% and error term (MAE) is 0.318. However, after the comparison between deep learning model and machine learning model as well as time series methods, LSTM model seems achieved a better result which accuracy is 91.62 and error term (MAE) is 0.316. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Centre for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Project |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |